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ut(Ve.Layout,null,{})},enhanceApp({app:s,router:t,siteData:e}){Dr(s)}};export{Or as R,Qn as c,L as u}; diff --git a/dev/assets/constraints_generic_constraints.md.Yd2fivbQ.js b/dev/assets/constraints_generic_constraints.md.WUfYZUE9.js similarity index 98% rename from dev/assets/constraints_generic_constraints.md.Yd2fivbQ.js rename to dev/assets/constraints_generic_constraints.md.WUfYZUE9.js index 0fcb796..ec95cc2 100644 --- a/dev/assets/constraints_generic_constraints.md.Yd2fivbQ.js +++ b/dev/assets/constraints_generic_constraints.md.WUfYZUE9.js @@ -1,4 +1,4 @@ -import{_ as e,c as a,m as s,a as i,a7 as n,o as t}from"./chunks/framework.aA95Gx5L.js";const R=JSON.parse('{"title":"Generic Constraints","description":"","frontmatter":{},"headers":[],"relativePath":"constraints/generic_constraints.md","filePath":"constraints/generic_constraints.md","lastUpdated":null}'),l={name:"constraints/generic_constraints.md"},h=n('

Generic Constraints ​

In the XCSP³-core standard, generic constraints are categorized into two main types: intention and extension constraints.

Intention Constraints ​

',3),p={class:"MathJax",jax:"SVG",style:{direction:"ltr",position:"relative"}},k={style:{overflow:"visible","min-height":"1px","min-width":"1px","vertical-align":"-0.025ex"},xmlns:"http://www.w3.org/2000/svg",width:"1.294ex",height:"1.025ex",role:"img",focusable:"false",viewBox:"0 -442 572 453","aria-hidden":"true"},r=s("g",{stroke:"currentColor",fill:"currentColor","stroke-width":"0",transform:"scale(1,-1)"},[s("g",{"data-mml-node":"math"},[s("g",{"data-mml-node":"mi"},[s("path",{"data-c":"1D465",d:"M52 289Q59 331 106 386T222 442Q257 442 286 424T329 379Q371 442 430 442Q467 442 494 420T522 361Q522 332 508 314T481 292T458 288Q439 288 427 299T415 328Q415 374 465 391Q454 404 425 404Q412 404 406 402Q368 386 350 336Q290 115 290 78Q290 50 306 38T341 26Q378 26 414 59T463 140Q466 150 469 151T485 153H489Q504 153 504 145Q504 144 502 134Q486 77 440 33T333 -11Q263 -11 227 52Q186 -10 133 -10H127Q78 -10 57 16T35 71Q35 103 54 123T99 143Q142 143 142 101Q142 81 130 66T107 46T94 41L91 40Q91 39 97 36T113 29T132 26Q168 26 194 71Q203 87 217 139T245 247T261 313Q266 340 266 352Q266 380 251 392T217 404Q177 404 142 372T93 290Q91 281 88 280T72 278H58Q52 284 52 289Z",style:{"stroke-width":"3"}})])])],-1),d=[r],o=s("mjx-assistive-mml",{unselectable:"on",display:"inline",style:{top:"0px",left:"0px",clip:"rect(1px, 1px, 1px, 1px)","-webkit-touch-callout":"none","-webkit-user-select":"none","-khtml-user-select":"none","-moz-user-select":"none","-ms-user-select":"none","user-select":"none",position:"absolute",padding:"1px 0px 0px 0px",border:"0px",display:"block",width:"auto",overflow:"hidden"}},[s("math",{xmlns:"http://www.w3.org/1998/Math/MathML"},[s("mi",null,"x")])],-1),c={class:"MathJax",jax:"SVG",style:{direction:"ltr",position:"relative"}},E={style:{overflow:"visible","min-height":"1px","min-width":"1px","vertical-align":"-0.464ex"},xmlns:"http://www.w3.org/2000/svg",width:"1.109ex",height:"1.464ex",role:"img",focusable:"false",viewBox:"0 -442 490 647","aria-hidden":"true"},g=s("g",{stroke:"currentColor",fill:"currentColor","stroke-width":"0",transform:"scale(1,-1)"},[s("g",{"data-mml-node":"math"},[s("g",{"data-mml-node":"mi"},[s("path",{"data-c":"1D466",d:"M21 287Q21 301 36 335T84 406T158 442Q199 442 224 419T250 355Q248 336 247 334Q247 331 231 288T198 191T182 105Q182 62 196 45T238 27Q261 27 281 38T312 61T339 94Q339 95 344 114T358 173T377 247Q415 397 419 404Q432 431 462 431Q475 431 483 424T494 412T496 403Q496 390 447 193T391 -23Q363 -106 294 -155T156 -205Q111 -205 77 -183T43 -117Q43 -95 50 -80T69 -58T89 -48T106 -45Q150 -45 150 -87Q150 -107 138 -122T115 -142T102 -147L99 -148Q101 -153 118 -160T152 -167H160Q177 -167 186 -165Q219 -156 247 -127T290 -65T313 -9T321 21L315 17Q309 13 296 6T270 -6Q250 -11 231 -11Q185 -11 150 11T104 82Q103 89 103 113Q103 170 138 262T173 379Q173 380 173 381Q173 390 173 393T169 400T158 404H154Q131 404 112 385T82 344T65 302T57 280Q55 278 41 278H27Q21 284 21 287Z",style:{"stroke-width":"3"}})])])],-1),y=[g],C=s("mjx-assistive-mml",{unselectable:"on",display:"inline",style:{top:"0px",left:"0px",clip:"rect(1px, 1px, 1px, 1px)","-webkit-touch-callout":"none","-webkit-user-select":"none","-khtml-user-select":"none","-moz-user-select":"none","-ms-user-select":"none","user-select":"none",position:"absolute",padding:"1px 0px 0px 0px",border:"0px",display:"block",width:"auto",overflow:"hidden"}},[s("math",{xmlns:"http://www.w3.org/1998/Math/MathML"},[s("mi",null,"y")])],-1),u={class:"MathJax",jax:"SVG",style:{direction:"ltr",position:"relative"}},m={style:{overflow:"visible","min-height":"1px","min-width":"1px","vertical-align":"-0.464ex"},xmlns:"http://www.w3.org/2000/svg",width:"5.42ex",height:"1.686ex",role:"img",focusable:"false",viewBox:"0 -540 2395.6 745","aria-hidden":"true"},Q=n('',1),F=[Q],T=s("mjx-assistive-mml",{unselectable:"on",display:"inline",style:{top:"0px",left:"0px",clip:"rect(1px, 1px, 1px, 1px)","-webkit-touch-callout":"none","-webkit-user-select":"none","-khtml-user-select":"none","-moz-user-select":"none","-ms-user-select":"none","user-select":"none",position:"absolute",padding:"1px 0px 0px 0px",border:"0px",display:"block",width:"auto",overflow:"hidden"}},[s("math",{xmlns:"http://www.w3.org/1998/Math/MathML"},[s("mi",null,"x"),s("mo",null,"<"),s("mi",null,"y")])],-1),f=n('

Note that the intention constraint is not directly available through the JC-API in Constraints.jl. It is designed as such since defining a constraint through a predicate is the natural way.

We provide a straightforward example through the :dist_different constraint on how to define and add such a constraint in the USUAL_CONSTRAINTS collection.

Higher level modeling language such as JuMP should provide a Intention interface.

Defining an intention constraint in JC-API ​

',4),b=s("code",null,"dist_different",-1),x=s("em",null,"Constraints.jl",-1),v=s("code",null,"dist_different",-1),_={class:"MathJax",jax:"SVG",style:{direction:"ltr",position:"relative"}},B={style:{overflow:"visible","min-height":"1px","min-width":"1px","vertical-align":"-0.025ex"},xmlns:"http://www.w3.org/2000/svg",width:"1.294ex",height:"1.025ex",role:"img",focusable:"false",viewBox:"0 -442 572 453","aria-hidden":"true"},w=s("g",{stroke:"currentColor",fill:"currentColor","stroke-width":"0",transform:"scale(1,-1)"},[s("g",{"data-mml-node":"math"},[s("g",{"data-mml-node":"mi"},[s("path",{"data-c":"1D465",d:"M52 289Q59 331 106 386T222 442Q257 442 286 424T329 379Q371 442 430 442Q467 442 494 420T522 361Q522 332 508 314T481 292T458 288Q439 288 427 299T415 328Q415 374 465 391Q454 404 425 404Q412 404 406 402Q368 386 350 336Q290 115 290 78Q290 50 306 38T341 26Q378 26 414 59T463 140Q466 150 469 151T485 153H489Q504 153 504 145Q504 144 502 134Q486 77 440 33T333 -11Q263 -11 227 52Q186 -10 133 -10H127Q78 -10 57 16T35 71Q35 103 54 123T99 143Q142 143 142 101Q142 81 130 66T107 46T94 41L91 40Q91 39 97 36T113 29T132 26Q168 26 194 71Q203 87 217 139T245 247T261 313Q266 340 266 352Q266 380 251 392T217 404Q177 404 142 372T93 290Q91 281 88 280T72 278H58Q52 284 52 289Z",style:{"stroke-width":"3"}})])])],-1),D=[w],H=s("mjx-assistive-mml",{unselectable:"on",display:"inline",style:{top:"0px",left:"0px",clip:"rect(1px, 1px, 1px, 1px)","-webkit-touch-callout":"none","-webkit-user-select":"none","-khtml-user-select":"none","-moz-user-select":"none","-ms-user-select":"none","user-select":"none",position:"absolute",padding:"1px 0px 0px 0px",border:"0px",display:"block",width:"auto",overflow:"hidden"}},[s("math",{xmlns:"http://www.w3.org/1998/Math/MathML"},[s("mi",null,"x")])],-1),A={class:"MathJax",jax:"SVG",display:"true",style:{direction:"ltr",display:"block","text-align":"center",margin:"1em 0",position:"relative"}},V={style:{overflow:"visible","min-height":"1px","min-width":"1px","vertical-align":"-0.566ex"},xmlns:"http://www.w3.org/2000/svg",width:"25.797ex",height:"2.262ex",role:"img",focusable:"false",viewBox:"0 -750 11402.4 1000","aria-hidden":"true"},j=n('',1),M=[j],P=s("mjx-assistive-mml",{unselectable:"on",display:"block",style:{top:"0px",left:"0px",clip:"rect(1px, 1px, 1px, 1px)","-webkit-touch-callout":"none","-webkit-user-select":"none","-khtml-user-select":"none","-moz-user-select":"none","-ms-user-select":"none","user-select":"none",position:"absolute",padding:"1px 0px 0px 0px",border:"0px",display:"block",overflow:"hidden",width:"100%"}},[s("math",{xmlns:"http://www.w3.org/1998/Math/MathML",display:"block"},[s("mo",{"data-mjx-texclass":"ORD",stretchy:"false"},"|"),s("mi",null,"x"),s("mo",{stretchy:"false"},"["),s("mn",null,"1"),s("mo",{stretchy:"false"},"]"),s("mo",null,"−"),s("mi",null,"x"),s("mo",{stretchy:"false"},"["),s("mn",null,"2"),s("mo",{stretchy:"false"},"]"),s("mo",{"data-mjx-texclass":"ORD",stretchy:"false"},"|"),s("mo",null,"≠"),s("mo",{"data-mjx-texclass":"ORD",stretchy:"false"},"|"),s("mi",null,"x"),s("mo",{stretchy:"false"},"["),s("mn",null,"3"),s("mo",{stretchy:"false"},"]"),s("mo",null,"−"),s("mi",null,"x"),s("mo",{stretchy:"false"},"["),s("mn",null,"4"),s("mo",{stretchy:"false"},"]"),s("mo",{"data-mjx-texclass":"ORD",stretchy:"false"},"|")])],-1),I=n(`

The constraint is then added to the usual constraints collection.

julia
const description_dist_different = """
+import{_ as e,c as a,m as s,a as i,a7 as n,o as t}from"./chunks/framework.aA95Gx5L.js";const N=JSON.parse('{"title":"Generic Constraints","description":"","frontmatter":{},"headers":[],"relativePath":"constraints/generic_constraints.md","filePath":"constraints/generic_constraints.md","lastUpdated":null}'),l={name:"constraints/generic_constraints.md"},h=n('
Generic Constraints ​
In the XCSP³-core standard, generic constraints are categorized into two main types: intention and extension constraints.
Intention Constraints ​
',3),p={class:"MathJax",jax:"SVG",style:{direction:"ltr",position:"relative"}},k={style:{overflow:"visible","min-height":"1px","min-width":"1px","vertical-align":"-0.025ex"},xmlns:"http://www.w3.org/2000/svg",width:"1.294ex",height:"1.025ex",role:"img",focusable:"false",viewBox:"0 -442 572 453","aria-hidden":"true"},r=s("g",{stroke:"currentColor",fill:"currentColor","stroke-width":"0",transform:"scale(1,-1)"},[s("g",{"data-mml-node":"math"},[s("g",{"data-mml-node":"mi"},[s("path",{"data-c":"1D465",d:"M52 289Q59 331 106 386T222 442Q257 442 286 424T329 379Q371 442 430 442Q467 442 494 420T522 361Q522 332 508 314T481 292T458 288Q439 288 427 299T415 328Q415 374 465 391Q454 404 425 404Q412 404 406 402Q368 386 350 336Q290 115 290 78Q290 50 306 38T341 26Q378 26 414 59T463 140Q466 150 469 151T485 153H489Q504 153 504 145Q504 144 502 134Q486 77 440 33T333 -11Q263 -11 227 52Q186 -10 133 -10H127Q78 -10 57 16T35 71Q35 103 54 123T99 143Q142 143 142 101Q142 81 130 66T107 46T94 41L91 40Q91 39 97 36T113 29T132 26Q168 26 194 71Q203 87 217 139T245 247T261 313Q266 340 266 352Q266 380 251 392T217 404Q177 404 142 372T93 290Q91 281 88 280T72 278H58Q52 284 52 289Z",style:{"stroke-width":"3"}})])])],-1),d=[r],o=s("mjx-assistive-mml",{unselectable:"on",display:"inline",style:{top:"0px",left:"0px",clip:"rect(1px, 1px, 1px, 1px)","-webkit-touch-callout":"none","-webkit-user-select":"none","-khtml-user-select":"none","-moz-user-select":"none","-ms-user-select":"none","user-select":"none",position:"absolute",padding:"1px 0px 0px 0px",border:"0px",display:"block",width:"auto",overflow:"hidden"}},[s("math",{xmlns:"http://www.w3.org/1998/Math/MathML"},[s("mi",null,"x")])],-1),c={class:"MathJax",jax:"SVG",style:{direction:"ltr",position:"relative"}},E={style:{overflow:"visible","min-height":"1px","min-width":"1px","vertical-align":"-0.464ex"},xmlns:"http://www.w3.org/2000/svg",width:"1.109ex",height:"1.464ex",role:"img",focusable:"false",viewBox:"0 -442 490 647","aria-hidden":"true"},g=s("g",{stroke:"currentColor",fill:"currentColor","stroke-width":"0",transform:"scale(1,-1)"},[s("g",{"data-mml-node":"math"},[s("g",{"data-mml-node":"mi"},[s("path",{"data-c":"1D466",d:"M21 287Q21 301 36 335T84 406T158 442Q199 442 224 419T250 355Q248 336 247 334Q247 331 231 288T198 191T182 105Q182 62 196 45T238 27Q261 27 281 38T312 61T339 94Q339 95 344 114T358 173T377 247Q415 397 419 404Q432 431 462 431Q475 431 483 424T494 412T496 403Q496 390 447 193T391 -23Q363 -106 294 -155T156 -205Q111 -205 77 -183T43 -117Q43 -95 50 -80T69 -58T89 -48T106 -45Q150 -45 150 -87Q150 -107 138 -122T115 -142T102 -147L99 -148Q101 -153 118 -160T152 -167H160Q177 -167 186 -165Q219 -156 247 -127T290 -65T313 -9T321 21L315 17Q309 13 296 6T270 -6Q250 -11 231 -11Q185 -11 150 11T104 82Q103 89 103 113Q103 170 138 262T173 379Q173 380 173 381Q173 390 173 393T169 400T158 404H154Q131 404 112 385T82 344T65 302T57 280Q55 278 41 278H27Q21 284 21 287Z",style:{"stroke-width":"3"}})])])],-1),y=[g],C=s("mjx-assistive-mml",{unselectable:"on",display:"inline",style:{top:"0px",left:"0px",clip:"rect(1px, 1px, 1px, 1px)","-webkit-touch-callout":"none","-webkit-user-select":"none","-khtml-user-select":"none","-moz-user-select":"none","-ms-user-select":"none","user-select":"none",position:"absolute",padding:"1px 0px 0px 0px",border:"0px",display:"block",width:"auto",overflow:"hidden"}},[s("math",{xmlns:"http://www.w3.org/1998/Math/MathML"},[s("mi",null,"y")])],-1),u={class:"MathJax",jax:"SVG",style:{direction:"ltr",position:"relative"}},m={style:{overflow:"visible","min-height":"1px","min-width":"1px","vertical-align":"-0.464ex"},xmlns:"http://www.w3.org/2000/svg",width:"5.42ex",height:"1.686ex",role:"img",focusable:"false",viewBox:"0 -540 2395.6 745","aria-hidden":"true"},Q=n('',1),F=[Q],T=s("mjx-assistive-mml",{unselectable:"on",display:"inline",style:{top:"0px",left:"0px",clip:"rect(1px, 1px, 1px, 1px)","-webkit-touch-callout":"none","-webkit-user-select":"none","-khtml-user-select":"none","-moz-user-select":"none","-ms-user-select":"none","user-select":"none",position:"absolute",padding:"1px 0px 0px 0px",border:"0px",display:"block",width:"auto",overflow:"hidden"}},[s("math",{xmlns:"http://www.w3.org/1998/Math/MathML"},[s("mi",null,"x"),s("mo",null,"<"),s("mi",null,"y")])],-1),f=n('
Note that the intention constraint is not directly available through the JC-API in Constraints.jl. It is designed as such since defining a constraint through a predicate is the natural way.
We provide a straightforward example through the :dist_different constraint on how to define and add such a constraint in the USUAL_CONSTRAINTS collection.
Higher level modeling language such as JuMP should provide a Intention interface.
Defining an intention constraint in JC-API ​
',4),b=s("code",null,"dist_different",-1),x=s("em",null,"Constraints.jl",-1),v=s("code",null,"dist_different",-1),_={class:"MathJax",jax:"SVG",style:{direction:"ltr",position:"relative"}},B={style:{overflow:"visible","min-height":"1px","min-width":"1px","vertical-align":"-0.025ex"},xmlns:"http://www.w3.org/2000/svg",width:"1.294ex",height:"1.025ex",role:"img",focusable:"false",viewBox:"0 -442 572 453","aria-hidden":"true"},w=s("g",{stroke:"currentColor",fill:"currentColor","stroke-width":"0",transform:"scale(1,-1)"},[s("g",{"data-mml-node":"math"},[s("g",{"data-mml-node":"mi"},[s("path",{"data-c":"1D465",d:"M52 289Q59 331 106 386T222 442Q257 442 286 424T329 379Q371 442 430 442Q467 442 494 420T522 361Q522 332 508 314T481 292T458 288Q439 288 427 299T415 328Q415 374 465 391Q454 404 425 404Q412 404 406 402Q368 386 350 336Q290 115 290 78Q290 50 306 38T341 26Q378 26 414 59T463 140Q466 150 469 151T485 153H489Q504 153 504 145Q504 144 502 134Q486 77 440 33T333 -11Q263 -11 227 52Q186 -10 133 -10H127Q78 -10 57 16T35 71Q35 103 54 123T99 143Q142 143 142 101Q142 81 130 66T107 46T94 41L91 40Q91 39 97 36T113 29T132 26Q168 26 194 71Q203 87 217 139T245 247T261 313Q266 340 266 352Q266 380 251 392T217 404Q177 404 142 372T93 290Q91 281 88 280T72 278H58Q52 284 52 289Z",style:{"stroke-width":"3"}})])])],-1),H=[w],A=s("mjx-assistive-mml",{unselectable:"on",display:"inline",style:{top:"0px",left:"0px",clip:"rect(1px, 1px, 1px, 1px)","-webkit-touch-callout":"none","-webkit-user-select":"none","-khtml-user-select":"none","-moz-user-select":"none","-ms-user-select":"none","user-select":"none",position:"absolute",padding:"1px 0px 0px 0px",border:"0px",display:"block",width:"auto",overflow:"hidden"}},[s("math",{xmlns:"http://www.w3.org/1998/Math/MathML"},[s("mi",null,"x")])],-1),D={class:"MathJax",jax:"SVG",display:"true",style:{direction:"ltr",display:"block","text-align":"center",margin:"1em 0",position:"relative"}},V={style:{overflow:"visible","min-height":"1px","min-width":"1px","vertical-align":"-0.566ex"},xmlns:"http://www.w3.org/2000/svg",width:"25.797ex",height:"2.262ex",role:"img",focusable:"false",viewBox:"0 -750 11402.4 1000","aria-hidden":"true"},j=n('',1),M=[j],I=s("mjx-assistive-mml",{unselectable:"on",display:"block",style:{top:"0px",left:"0px",clip:"rect(1px, 1px, 1px, 1px)","-webkit-touch-callout":"none","-webkit-user-select":"none","-khtml-user-select":"none","-moz-user-select":"none","-ms-user-select":"none","user-select":"none",position:"absolute",padding:"1px 0px 0px 0px",border:"0px",display:"block",overflow:"hidden",width:"100%"}},[s("math",{xmlns:"http://www.w3.org/1998/Math/MathML",display:"block"},[s("mo",{"data-mjx-texclass":"ORD",stretchy:"false"},"|"),s("mi",null,"x"),s("mo",{stretchy:"false"},"["),s("mn",null,"1"),s("mo",{stretchy:"false"},"]"),s("mo",null,"−"),s("mi",null,"x"),s("mo",{stretchy:"false"},"["),s("mn",null,"2"),s("mo",{stretchy:"false"},"]"),s("mo",{"data-mjx-texclass":"ORD",stretchy:"false"},"|"),s("mo",null,"≠"),s("mo",{"data-mjx-texclass":"ORD",stretchy:"false"},"|"),s("mi",null,"x"),s("mo",{stretchy:"false"},"["),s("mn",null,"3"),s("mo",{stretchy:"false"},"]"),s("mo",null,"−"),s("mi",null,"x"),s("mo",{stretchy:"false"},"["),s("mn",null,"4"),s("mo",{stretchy:"false"},"]"),s("mo",{"data-mjx-texclass":"ORD",stretchy:"false"},"|")])],-1),P=n(`
The constraint is then added to the usual constraints collection.
julia
const description_dist_different = """
 Ensures that the distances between marks on the ruler are unique.
 """
 
@@ -9,7 +9,7 @@ import{_ as e,c as a,m as s,a as i,a7 as n,o as t}from"./chunks/framework.aA95Gx
 @usual concept_dist_different(x) = xcsp_intension(
     list = x,
     predicate = predicate_dist_different
-)
Please check the section dedicated to the Golomb Ruler problem to see a use for this constraint. <!– TODO: Golomb Ruler –>
APIs ​
Note that the intension constraint is not directly available through the JC-API in Constraints.jl. It is designed as such since defining a constraint through a predicate is the natural way.
We provide here a usage example for the :dist_different constraint, previously added to the USUAL_CONSTRAINTS collection.
Higher level modeling language such as JuMP should provide an Intension interface.
julia
concept(:dist_different, x)
+)
Please check the section dedicated to the Golomb Ruler problem to see a use for this constraint. <!– TODO: Golomb Ruler –>
APIs ​
Note that the intension constraint is not directly available through the JC-API in Constraints.jl. It is designed as such since defining a constraint through a predicate is the natural way.
We provide here a usage example for the :dist_different constraint, previously added to the USUAL_CONSTRAINTS collection.
Higher level modeling language such as JuMP should provide an Intension interface.
julia
concept(:dist_different, x)
 concept(:dist_different)(x)
julia
# Defines the DistDifferent constraint
 c = x -> xcsp_intension(
     list = x,
@@ -36,4 +36,4 @@ import{_ as e,c as a,m as s,a as i,a7 as n,o as t}from"./chunks/framework.aA95Gx
 c([1, 2, 3, 4, 5]; pair_vars=[[1, 2, 3, 4, 5]])
 
 c = concept(:conflicts)
-c([1, 2, 3, 4, 5]; pair_vars=[[1, 2, 1, 4, 5], [1, 2, 3, 5, 5]])
source

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Getting Started with Julia for CP and Optimization ​

Why Julia? ​

  • Discuss the advantages of Julia for computational science and optimization, highlighting its performance and ease of use.

Setting Up Your Julia Environment ​

We encourage users to install Julia through juliaup, a version manager for the Julia language. Please look at the official Julia language download page for further information. Once installed, Julia can be used through various editors (Visual Studio Code), notebooks (Pluto.jl), or command-line (REPL).

Although a part of the CP solvers available within the Julia ecosystem have their own interface, we encourage users to use the JuMP modeling language if possible.

Julia Constraints host several solvers(' interfaces). Due to its flexibility in modeling and solving, we will use LocalSearchSolvers.jl through its JuMP interface CBLS.jl as the basic example. Note that depending on the targeted instances, available hardware, and expectations, it is not necessarily the best choice.

All along the documentation, we will try to provide syntax examples for different setup.

julia
using LocalSearchSolvers
julia
using JuMP, CBLS
julia
# TODO: Add other solvers

Your First Julia CP Model ​

We will start with a classic puzzle game and some of its not that simple variants: the Sudoku.

(From Wikipedia) In classic Sudoku, the objective is to fill a 9 × 9 grid with digits so that each column, each row, and each of the nine 3 × 3 subgrids that compose the grid (also called "boxes", "blocks", or "regions") contains all of the digits from 1 to 9. The puzzle setter provides a partially completed grid, which for a well-posed puzzle has a single solution.

Constraint Programming follows the model-and-solve approach. We first need to model our Sudoku problem.

julia
m = JuMP.Model(CBLS.Optimizer)
julia
# TODO: Add other solvers

But what are the basis of CP models? It is quite simple:

',15),o={class:"MathJax",jax:"SVG",style:{direction:"ltr",position:"relative"}},p={style:{overflow:"visible","min-height":"1px","min-width":"1px","vertical-align":"-0.439ex"},xmlns:"http://www.w3.org/2000/svg",width:"15.867ex",height:"1.984ex",role:"img",focusable:"false",viewBox:"0 -683 7013.4 877","aria-hidden":"true"},d=a('',1),r=[d],k=s("mjx-assistive-mml",{unselectable:"on",display:"inline",style:{top:"0px",left:"0px",clip:"rect(1px, 1px, 1px, 1px)","-webkit-touch-callout":"none","-webkit-user-select":"none","-khtml-user-select":"none","-moz-user-select":"none","-ms-user-select":"none","user-select":"none",position:"absolute",padding:"1px 0px 0px 0px",border:"0px",display:"block",width:"auto",overflow:"hidden"}},[s("math",{xmlns:"http://www.w3.org/1998/Math/MathML"},[s("mi",null,"X"),s("mo",null,"="),s("msub",null,[s("mi",null,"X"),s("mn",null,"1")]),s("mo",null,","),s("mo",null,"⋯"),s("mo",null,","),s("msub",null,[s("mi",null,"X"),s("mi",null,"n")])])],-1),c=a('
julia
@variable(m, 1 ≤ X[1:9, 1:9] ≤ 9, Int)
julia
# TODO: Add other solvers
',1),Q={class:"MathJax",jax:"SVG",style:{direction:"ltr",position:"relative"}},g={style:{overflow:"visible","min-height":"1px","min-width":"1px","vertical-align":"-0.439ex"},xmlns:"http://www.w3.org/2000/svg",width:"15.148ex",height:"2.034ex",role:"img",focusable:"false",viewBox:"0 -705 6695.4 899","aria-hidden":"true"},T=a('',1),u=[T],m=s("mjx-assistive-mml",{unselectable:"on",display:"inline",style:{top:"0px",left:"0px",clip:"rect(1px, 1px, 1px, 1px)","-webkit-touch-callout":"none","-webkit-user-select":"none","-khtml-user-select":"none","-moz-user-select":"none","-ms-user-select":"none","user-select":"none",position:"absolute",padding:"1px 0px 0px 0px",border:"0px",display:"block",width:"auto",overflow:"hidden"}},[s("math",{xmlns:"http://www.w3.org/1998/Math/MathML"},[s("mi",null,"C"),s("mo",null,"="),s("msub",null,[s("mi",null,"C"),s("mn",null,"1")]),s("mo",null,","),s("mo",null,"⋯"),s("mo",null,","),s("msub",null,[s("mi",null,"C"),s("mi",null,"n")])])],-1),v={class:"MathJax",jax:"SVG",style:{direction:"ltr",position:"relative"}},y={style:{overflow:"visible","min-height":"1px","min-width":"1px","vertical-align":"0"},xmlns:"http://www.w3.org/2000/svg",width:"1.928ex",height:"1.545ex",role:"img",focusable:"false",viewBox:"0 -683 852 683","aria-hidden":"true"},b=s("g",{stroke:"currentColor",fill:"currentColor","stroke-width":"0",transform:"scale(1,-1)"},[s("g",{"data-mml-node":"math"},[s("g",{"data-mml-node":"mi"},[s("path",{"data-c":"1D44B",d:"M42 0H40Q26 0 26 11Q26 15 29 27Q33 41 36 43T55 46Q141 49 190 98Q200 108 306 224T411 342Q302 620 297 625Q288 636 234 637H206Q200 643 200 645T202 664Q206 677 212 683H226Q260 681 347 681Q380 681 408 681T453 682T473 682Q490 682 490 671Q490 670 488 658Q484 643 481 640T465 637Q434 634 411 620L488 426L541 485Q646 598 646 610Q646 628 622 635Q617 635 609 637Q594 637 594 648Q594 650 596 664Q600 677 606 683H618Q619 683 643 683T697 681T738 680Q828 680 837 683H845Q852 676 852 672Q850 647 840 637H824Q790 636 763 628T722 611T698 593L687 584Q687 585 592 480L505 384Q505 383 536 304T601 142T638 56Q648 47 699 46Q734 46 734 37Q734 35 732 23Q728 7 725 4T711 1Q708 1 678 1T589 2Q528 2 496 2T461 1Q444 1 444 10Q444 11 446 25Q448 35 450 39T455 44T464 46T480 47T506 54Q523 62 523 64Q522 64 476 181L429 299Q241 95 236 84Q232 76 232 72Q232 53 261 47Q262 47 267 47T273 46Q276 46 277 46T280 45T283 42T284 35Q284 26 282 19Q279 6 276 4T261 1Q258 1 243 1T201 2T142 2Q64 2 42 0Z",style:{"stroke-width":"3"}})])])],-1),E=[b],C=s("mjx-assistive-mml",{unselectable:"on",display:"inline",style:{top:"0px",left:"0px",clip:"rect(1px, 1px, 1px, 1px)","-webkit-touch-callout":"none","-webkit-user-select":"none","-khtml-user-select":"none","-moz-user-select":"none","-ms-user-select":"none","user-select":"none",position:"absolute",padding:"1px 0px 0px 0px",border:"0px",display:"block",width:"auto",overflow:"hidden"}},[s("math",{xmlns:"http://www.w3.org/1998/Math/MathML"},[s("mi",null,"X")])],-1),f=a(`

When modeling problems as CP, one might define and use their own predicates. However, a large collection of already defined constraints exists. One, if not the most, iconic global constraint is called AllDifferent. It ensures that all variables take distinct values.

Sudoku puzzles can be defined using only this one constraint applied to different subsets of variables.

julia
for i in 1:9
+import{_ as l,c as t,m as s,a as i,a7 as a,o as e}from"./chunks/framework.aA95Gx5L.js";const L=JSON.parse('{"title":"Getting Started with Julia for CP and Optimization","description":"","frontmatter":{},"headers":[],"relativePath":"cp/getting_started.md","filePath":"cp/getting_started.md","lastUpdated":null}'),n={name:"cp/getting_started.md"},h=a('
Getting Started with Julia for CP and Optimization ​
Why Julia? ​
  • Discuss the advantages of Julia for computational science and optimization, highlighting its performance and ease of use.
Setting Up Your Julia Environment ​
We encourage users to install Julia through juliaup, a version manager for the Julia language. Please look at the official Julia language download page for further information. Once installed, Julia can be used through various editors (Visual Studio Code), notebooks (Pluto.jl), or command-line (REPL).
Although a part of the CP solvers available within the Julia ecosystem have their own interface, we encourage users to use the JuMP modeling language if possible.
Julia Constraints host several solvers(' interfaces). Due to its flexibility in modeling and solving, we will use LocalSearchSolvers.jl through its JuMP interface CBLS.jl as the basic example. Note that depending on the targeted instances, available hardware, and expectations, it is not necessarily the best choice.
All along the documentation, we will try to provide syntax examples for different setup.
julia
using LocalSearchSolvers
julia
using JuMP, CBLS
julia
# TODO: Add other solvers
Your First Julia CP Model ​
We will start with a classic puzzle game and some of its not that simple variants: the Sudoku.
(From Wikipedia) In classic Sudoku, the objective is to fill a 9 × 9 grid with digits so that each column, each row, and each of the nine 3 × 3 subgrids that compose the grid (also called "boxes", "blocks", or "regions") contains all of the digits from 1 to 9. The puzzle setter provides a partially completed grid, which for a well-posed puzzle has a single solution.
Constraint Programming follows the model-and-solve approach. We first need to model our Sudoku problem.
julia
m = JuMP.Model(CBLS.Optimizer)
julia
# TODO: Add other solvers
But what are the basis of CP models? It is quite simple:
',15),o={class:"MathJax",jax:"SVG",style:{direction:"ltr",position:"relative"}},p={style:{overflow:"visible","min-height":"1px","min-width":"1px","vertical-align":"-0.439ex"},xmlns:"http://www.w3.org/2000/svg",width:"15.867ex",height:"1.984ex",role:"img",focusable:"false",viewBox:"0 -683 7013.4 877","aria-hidden":"true"},d=a('',1),r=[d],k=s("mjx-assistive-mml",{unselectable:"on",display:"inline",style:{top:"0px",left:"0px",clip:"rect(1px, 1px, 1px, 1px)","-webkit-touch-callout":"none","-webkit-user-select":"none","-khtml-user-select":"none","-moz-user-select":"none","-ms-user-select":"none","user-select":"none",position:"absolute",padding:"1px 0px 0px 0px",border:"0px",display:"block",width:"auto",overflow:"hidden"}},[s("math",{xmlns:"http://www.w3.org/1998/Math/MathML"},[s("mi",null,"X"),s("mo",null,"="),s("msub",null,[s("mi",null,"X"),s("mn",null,"1")]),s("mo",null,","),s("mo",null,"⋯"),s("mo",null,","),s("msub",null,[s("mi",null,"X"),s("mi",null,"n")])])],-1),c=a('
julia
@variable(m, 1 ≤ X[1:9, 1:9] ≤ 9, Int)
julia
# TODO: Add other solvers
',1),Q={class:"MathJax",jax:"SVG",style:{direction:"ltr",position:"relative"}},g={style:{overflow:"visible","min-height":"1px","min-width":"1px","vertical-align":"-0.439ex"},xmlns:"http://www.w3.org/2000/svg",width:"15.148ex",height:"2.034ex",role:"img",focusable:"false",viewBox:"0 -705 6695.4 899","aria-hidden":"true"},T=a('',1),u=[T],m=s("mjx-assistive-mml",{unselectable:"on",display:"inline",style:{top:"0px",left:"0px",clip:"rect(1px, 1px, 1px, 1px)","-webkit-touch-callout":"none","-webkit-user-select":"none","-khtml-user-select":"none","-moz-user-select":"none","-ms-user-select":"none","user-select":"none",position:"absolute",padding:"1px 0px 0px 0px",border:"0px",display:"block",width:"auto",overflow:"hidden"}},[s("math",{xmlns:"http://www.w3.org/1998/Math/MathML"},[s("mi",null,"C"),s("mo",null,"="),s("msub",null,[s("mi",null,"C"),s("mn",null,"1")]),s("mo",null,","),s("mo",null,"⋯"),s("mo",null,","),s("msub",null,[s("mi",null,"C"),s("mi",null,"n")])])],-1),v={class:"MathJax",jax:"SVG",style:{direction:"ltr",position:"relative"}},y={style:{overflow:"visible","min-height":"1px","min-width":"1px","vertical-align":"0"},xmlns:"http://www.w3.org/2000/svg",width:"1.928ex",height:"1.545ex",role:"img",focusable:"false",viewBox:"0 -683 852 683","aria-hidden":"true"},b=s("g",{stroke:"currentColor",fill:"currentColor","stroke-width":"0",transform:"scale(1,-1)"},[s("g",{"data-mml-node":"math"},[s("g",{"data-mml-node":"mi"},[s("path",{"data-c":"1D44B",d:"M42 0H40Q26 0 26 11Q26 15 29 27Q33 41 36 43T55 46Q141 49 190 98Q200 108 306 224T411 342Q302 620 297 625Q288 636 234 637H206Q200 643 200 645T202 664Q206 677 212 683H226Q260 681 347 681Q380 681 408 681T453 682T473 682Q490 682 490 671Q490 670 488 658Q484 643 481 640T465 637Q434 634 411 620L488 426L541 485Q646 598 646 610Q646 628 622 635Q617 635 609 637Q594 637 594 648Q594 650 596 664Q600 677 606 683H618Q619 683 643 683T697 681T738 680Q828 680 837 683H845Q852 676 852 672Q850 647 840 637H824Q790 636 763 628T722 611T698 593L687 584Q687 585 592 480L505 384Q505 383 536 304T601 142T638 56Q648 47 699 46Q734 46 734 37Q734 35 732 23Q728 7 725 4T711 1Q708 1 678 1T589 2Q528 2 496 2T461 1Q444 1 444 10Q444 11 446 25Q448 35 450 39T455 44T464 46T480 47T506 54Q523 62 523 64Q522 64 476 181L429 299Q241 95 236 84Q232 76 232 72Q232 53 261 47Q262 47 267 47T273 46Q276 46 277 46T280 45T283 42T284 35Q284 26 282 19Q279 6 276 4T261 1Q258 1 243 1T201 2T142 2Q64 2 42 0Z",style:{"stroke-width":"3"}})])])],-1),E=[b],C=s("mjx-assistive-mml",{unselectable:"on",display:"inline",style:{top:"0px",left:"0px",clip:"rect(1px, 1px, 1px, 1px)","-webkit-touch-callout":"none","-webkit-user-select":"none","-khtml-user-select":"none","-moz-user-select":"none","-ms-user-select":"none","user-select":"none",position:"absolute",padding:"1px 0px 0px 0px",border:"0px",display:"block",width:"auto",overflow:"hidden"}},[s("math",{xmlns:"http://www.w3.org/1998/Math/MathML"},[s("mi",null,"X")])],-1),f=a(`
When modeling problems as CP, one might define and use their own predicates. However, a large collection of already defined constraints exists. One, if not the most, iconic global constraint is called AllDifferent. It ensures that all variables take distinct values.
Sudoku puzzles can be defined using only this one constraint applied to different subsets of variables.
julia
for i in 1:9
         @constraint(m, X[i,:] in AllDifferent()) # rows
         @constraint(m, X[:,i] in AllDifferent()) # columns
-end
julia
# TODO: Add other solvers
The last series of AllDifferent constraint is less straight forward. We need to ensure that each 3 × 3 subgrid (block) is filled with distinct values.
julia
for i in 0:2, j in 0:2 # blocks
+end
julia
# TODO: Add other solvers
The last series of AllDifferent constraint is less straight forward. We need to ensure that each 3 × 3 subgrid (block) is filled with distinct values.
julia
for i in 0:2, j in 0:2 # blocks
     @constraint(
         m,
         vec(X[(3i+1):(3(i+1)), (3j+1):(3(j+1))]) in AllDifferent(),
     )
-end
julia
# TODO: Add other solvers
We can now simply run our solver to look for a feasible solution.
julia
optimize!(m)
Note that this is heuristic solver, we might not get a feasible solution! Let's check it out. The value function print the value of a JuMP variable. We can cast it over a collection with the value. syntax.
julia
value.(X)
`,9);function _(F,w,D,A,x,B){return e(),t("div",null,[h,s("ol",null,[s("li",null,[i("A collection "),s("mjx-container",o,[(e(),t("svg",p,r)),k]),i(" of variables with each an associated domain.")])]),c,s("ol",null,[s("li",null,[i("A collection of predicates (called constraints) "),s("mjx-container",Q,[(e(),t("svg",g,u)),m]),i(" over (subsets of) "),s("mjx-container",v,[(e(),t("svg",y,E)),C]),i(".")])]),f])}const M=l(n,[["render",_]]);export{H as __pageData,M as default};
+end
julia
# TODO: Add other solvers

We can now simply run our solver to look for a feasible solution.

julia
optimize!(m)

Note that this is heuristic solver, we might not get a feasible solution! Let's check it out. The value function print the value of a JuMP variable. We can cast it over a collection with the value. syntax.

julia
value.(X)
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Optimization","description":"","frontmatter":{},"headers":[],"relativePath":"cp/getting_started.md","filePath":"cp/getting_started.md","lastUpdated":null}'),n={name:"cp/getting_started.md"},h=a("",15),o={class:"MathJax",jax:"SVG",style:{direction:"ltr",position:"relative"}},p={style:{overflow:"visible","min-height":"1px","min-width":"1px","vertical-align":"-0.439ex"},xmlns:"http://www.w3.org/2000/svg",width:"15.867ex",height:"1.984ex",role:"img",focusable:"false",viewBox:"0 -683 7013.4 877","aria-hidden":"true"},d=a("",1),r=[d],k=s("mjx-assistive-mml",{unselectable:"on",display:"inline",style:{top:"0px",left:"0px",clip:"rect(1px, 1px, 1px, 1px)","-webkit-touch-callout":"none","-webkit-user-select":"none","-khtml-user-select":"none","-moz-user-select":"none","-ms-user-select":"none","user-select":"none",position:"absolute",padding:"1px 0px 0px 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1px)","-webkit-touch-callout":"none","-webkit-user-select":"none","-khtml-user-select":"none","-moz-user-select":"none","-ms-user-select":"none","user-select":"none",position:"absolute",padding:"1px 0px 0px 0px",border:"0px",display:"block",width:"auto",overflow:"hidden"}},[s("math",{xmlns:"http://www.w3.org/1998/Math/MathML"},[s("mi",null,"X")])],-1),f=a("",9);function _(w,F,D,A,x,B){return e(),t("div",null,[h,s("ol",null,[s("li",null,[i("A collection "),s("mjx-container",o,[(e(),t("svg",p,r)),k]),i(" of variables with each an associated domain.")])]),c,s("ol",null,[s("li",null,[i("A collection of predicates (called constraints) "),s("mjx-container",Q,[(e(),t("svg",g,u)),m]),i(" over (subsets of) "),s("mjx-container",v,[(e(),t("svg",y,E)),C]),i(".")])]),f])}const S=l(n,[["render",_]]);export{L as __pageData,S as default}; diff --git a/dev/assets/perf_perf_checker.md.CXuw3agL.js b/dev/assets/perf_perf_checker.md.C3kXwfzJ.js similarity index 94% rename from dev/assets/perf_perf_checker.md.CXuw3agL.js rename to dev/assets/perf_perf_checker.md.C3kXwfzJ.js index 2dc434e..eff51d3 100644 --- a/dev/assets/perf_perf_checker.md.CXuw3agL.js +++ b/dev/assets/perf_perf_checker.md.C3kXwfzJ.js @@ -1,4 +1,4 @@ -import{_ as e,c as r,o as s,a7 as i}from"./chunks/framework.aA95Gx5L.js";const b=JSON.parse('{"title":"PerfChecker.jl","description":"","frontmatter":{},"headers":[],"relativePath":"perf/perf_checker.md","filePath":"perf/perf_checker.md","lastUpdated":null}'),a={name:"perf/perf_checker.md"},t=i(`

PerfChecker.jl ​

Documentation for PerfChecker.jl.

# PerfChecker.arrange_breaking — Method.

Outputs the last breaking or next breaking version.

source


# PerfChecker.arrange_major — Method.

Outputs the earlier or next major version.

source


# PerfChecker.arrange_patches — Method.

Outputs the last patch or first patch of a version.

source


# PerfChecker.get_pkg_versions — Function.

Finds all versions of a package in all the installed registries and returns it as a vector.

Example:

julia
julia> get_pkg_versions("ConstraintLearning")
+import{_ as e,c as r,o as s,a7 as i}from"./chunks/framework.aA95Gx5L.js";const b=JSON.parse('{"title":"PerfChecker.jl","description":"","frontmatter":{},"headers":[],"relativePath":"perf/perf_checker.md","filePath":"perf/perf_checker.md","lastUpdated":null}'),a={name:"perf/perf_checker.md"},t=i(`
PerfChecker.jl ​
Documentation for PerfChecker.jl.
# PerfChecker.arrange_breaking — Method. 
Outputs the last breaking or next breaking version.
source

# PerfChecker.arrange_major — Method. 
Outputs the earlier or next major version.
source

# PerfChecker.arrange_patches — Method. 
Outputs the last patch or first patch of a version.
source

# PerfChecker.get_pkg_versions — Function. 
Finds all versions of a package in all the installed registries and returns it as a vector.
Example:
julia
julia> get_pkg_versions("ConstraintLearning")
 7-element Vector{VersionNumber}:
  v"0.1.4"
  v"0.1.5"
@@ -6,4 +6,4 @@ import{_ as e,c as r,o as s,a7 as i}from"./chunks/framework.aA95Gx5L.js";const b
  v"0.1.6"
  v"0.1.1"
  v"0.1.3"
- v"0.1.2"
source

`,10),n=[t];function l(o,p,h,k,c,d){return s(),r("div",null,n)}const g=e(a,[["render",l]]);export{b as __pageData,g as default};
+ v"0.1.2"

source


`,10),n=[t];function l(o,p,h,k,c,d){return s(),r("div",null,n)}const g=e(a,[["render",l]]);export{b as __pageData,g as default}; diff --git a/dev/assets/perf_perf_checker.md.CXuw3agL.lean.js b/dev/assets/perf_perf_checker.md.C3kXwfzJ.lean.js similarity index 100% rename from dev/assets/perf_perf_checker.md.CXuw3agL.lean.js rename to dev/assets/perf_perf_checker.md.C3kXwfzJ.lean.js diff --git a/dev/constraints/comparison_constraints.html b/dev/constraints/comparison_constraints.html index 73789b7..321090d 100644 --- a/dev/constraints/comparison_constraints.html +++ b/dev/constraints/comparison_constraints.html @@ -8,10 +8,10 @@ - + - + @@ -42,7 +42,7 @@ c([1, 2, 3, 4, 5]; op=<) !c([1, 2, 3, 4, 3]; op=≤) !c([1, 2, 3, 4, 3]; op=<)

source


- + \ No newline at end of file diff --git a/dev/constraints/connection_constraints.html b/dev/constraints/connection_constraints.html index c082e74..d8958a9 100644 --- a/dev/constraints/connection_constraints.html +++ b/dev/constraints/connection_constraints.html @@ -8,10 +8,10 @@ - + - + @@ -41,7 +41,7 @@ c([2, 1, 4, 3, 5, 2, 1, 4, 5, 3]; dim=2) c([false, false, true, false]; id=3) c([false, false, true, false]; id=1)

source


- + \ No newline at end of file diff --git a/dev/constraints/constraint_commons.html b/dev/constraints/constraint_commons.html index 7e699e9..6905bf8 100644 --- a/dev/constraints/constraint_commons.html +++ b/dev/constraints/constraint_commons.html @@ -8,10 +8,10 @@ - + - + @@ -27,7 +27,7 @@ :val, :vals, ]

source


# ConstraintCommons.extract_parameters — Function.
julia
extract_parameters(m::Union{Method, Function}; parameters)

Extracts the intersection between the kargs of m and parameters (defaults to USUAL_CONSTRAINT_PARAMETERS).

source

julia
extract_parameters(s::Symbol, constraints_dict=USUAL_CONSTRAINTS; parameters=ConstraintCommons.USUAL_CONSTRAINT_PARAMETERS)

Return the parameters of the constraint s in constraints_dict.

Arguments

  • s::Symbol: the constraint name.

  • constraints_dict::Dict{Symbol,Constraint}: dictionary of constraints. Default is USUAL_CONSTRAINTS.

  • parameters::Vector{Symbol}: vector of parameters. Default is ConstraintCommons.USUAL_CONSTRAINT_PARAMETERS.

Example

julia
extract_parameters(:all_different)

source


Performances – TODO ​

Languages ​

XCSP3 considers two kinds of structure to recognize languages as core constraints: Automata, Multivalued Decision Diagrams (MMDs).

# ConstraintCommons.AbstractMultivaluedDecisionDiagram — Type.
julia
AbstractMultivaluedDecisionDiagram

An abstract interface for Multivalued Decision Diagrams (MDD) used in Julia Constraints packages. Requirements:

  • accept(a<:AbstractMultivaluedDecisionDiagram, word): return true if a accepts word.

source


# ConstraintCommons.MDD — Type.
julia
MDD{S,T} <: AbstractMultivaluedDecisionDiagram

A minimal implementation of a multivalued decision diagram structure.

source


# ConstraintCommons.AbstractAutomaton — Type.
julia
AbstractAutomaton

An abstract interface for automata used in Julia Constraints packages. Requirements:

  • accept(a<:AbstractAutomaton, word): return true if a accepts word.

source


# ConstraintCommons.Automaton — Type.
julia
Automaton{S, T, F <: Union{S, Vector{S}, Set{S}}} <: AbstractAutomaton

A minimal implementation of a deterministic automaton structure.

source


Missing docstring.

Missing docstring for Automaton(a::MDD). Check Documenter's build log for details.

# ConstraintCommons.accept — Function.
julia
accept(a::Union{Automaton, MDD}, w)

Return true if a accepts the word w and false otherwise.

source

julia
ConstraintCommons.accept(fa::FakeAutomaton, word)

Implement the accept methods for FakeAutomaton.

source


# ConstraintCommons.at_end — Function.
julia
at_end(a::Automaton, s)

Internal method used by accept with Automaton.

source


Performances – TODO ​

Extensions ​

We extended some operations for Nothing and Symbol.

Missing docstring.

Missing docstring for Base.:*. Check Documenter's build log for details.

Missing docstring.

Missing docstring for Base.in(::Any, ::Nothing). Check Documenter's build log for details.

Missing docstring.

Missing docstring for Base.isempty(::Nothing). Check Documenter's build log for details.

Performances – TODO ​

Sampling ​

During our constraint learning processes, we use sampling to efficiently make partial exploration of search spaces. Follows some sampling utilities.

# ConstraintCommons.oversample — Function.
julia
oversample(X, f)

Oversample elements of X until the boolean function f has as many true and false configurations.

source


Performances – TODO ​

Extrema ​

We need to compute the difference between extrema of various kind of collections in several situations.

# ConstraintCommons.δ_extrema — Function.
julia
δ_extrema(X...)

Compute both the difference between the maximum and the minimum of over all the collections of X.

source


Performances – TODO ​

Dictionaries ​

We provide the everuseful incsert! function for dictionaries.

# ConstraintCommons.incsert! — Function.
julia
incsert!(d::Union{AbstractDict, AbstractDictionary}, ind, val = 1)

Increase or insert a counter in a dictionary-based collection. The counter insertion defaults to val = 1.

source


Performances – TODO ​

- + \ No newline at end of file diff --git a/dev/constraints/constraint_domains.html b/dev/constraints/constraint_domains.html index 2b1546d..2aaf396 100644 --- a/dev/constraints/constraint_domains.html +++ b/dev/constraints/constraint_domains.html @@ -8,10 +8,10 @@ - + - + @@ -53,7 +53,7 @@ solutions_limit = floor(Int, sqrt(max_samplings)), )

Settings for the exploration of a search space composed by a collection of domains.

source


# ConstraintDomains._explore — Function.
julia
_explore(args...)

Internals of the explore function. Behavior is automatically adjusted on the kind of exploration: :flexible, :complete, :partial.

source


# ConstraintDomains.explore — Function.
julia
explore(domains, concept, param = nothing; search_limit = 1000, solutions_limit = 100)

Search (a part of) a search space and returns a pair of vector of configurations: (solutions, non_solutions). If the search space size is over search_limit, then both solutions and non_solutions are limited to solutions_limit.

Beware that if the density of the solutions in the search space is low, solutions_limit needs to be reduced. This process will be automatic in the future (simple reinforcement learning).

Arguments:

  • domains: a collection of domains

  • concept: the concept of the targeted constraint

  • param: an optional parameter of the constraint

  • sol_number: the required number of solutions (half of the number of configurations), default to 100

source


Parameters ​

# ConstraintDomains.BoolParameterDomain — Type.
julia
BoolParameterDomain <: AbstractDomain

A domain to store boolean values. It is used to generate random parameters.

source


# ConstraintDomains.DimParameterDomain — Type.
julia
DimParameterDomain <: AbstractDomain

A domain to store dimensions. It is used to generate random parameters.

source


# ConstraintDomains.IdParameterDomain — Type.
julia
IdParameterDomain <: AbstractDomain

A domain to store ids. It is used to generate random parameters.

source


# ConstraintDomains.FakeAutomaton — Type.
julia
FakeAutomaton{T} <: ConstraintCommons.AbstractAutomaton

A structure to generate pseudo automaton enough for parameter exploration.

source


# ConstraintCommons.accept — Function.
julia
accept(a::Union{Automaton, MDD}, w)

Return true if a accepts the word w and false otherwise.

source

julia
ConstraintCommons.accept(fa::FakeAutomaton, word)

Implement the accept methods for FakeAutomaton.

source


# ConstraintDomains.fake_automaton — Function.
julia
fake_automaton(d)

Construct a FakeAutomaton.

source


# ConstraintDomains.LanguageParameterDomain — Type.
julia
LanguageParameterDomain <: AbstractDomain

A domain to store languages. It is used to generate random parameters.

source


# ConstraintDomains.OpParameterDomain — Type.
julia
OpParameterDomain{T} <: AbstractDomain

A domain to store operators. It is used to generate random parameters.

source


# ConstraintDomains.PairVarsParameterDomain — Type.
julia
PairVarsParameterDomain{T} <: AbstractDomain

A domain to store values paired with variables. It is used to generate random parameters.

source


# ConstraintDomains.ValParameterDomain — Type.
julia
ValParameterDomain{T} <: AbstractDomain

A domain to store one value. It is used to generate random parameters.

source


# ConstraintDomains.ValsParameterDomain — Type.
julia
ValsParameterDomain{T} <: AbstractDomain

A domain to store values. It is used to generate random parameters.

source


# Base.rand — Function.
julia
Base.rand(d::Union{Vector{D},Set{D}, D}) where {D<:AbstractDomain}

Extends Base.rand to (a collection of) domains.

source

julia
Base.rand(itv::Intervals)
 Base.rand(itv::Intervals, i)

Return a random value from itv, specifically from the ith interval if i is specified.

source

julia
Base.rand(d::D) where D <: DiscreteDomain

Draw randomly a point in d.

source

julia
Base.rand(fa::FakeAutomaton)

Extends Base.rand. Currently simply returns fa.

source


# ConstraintDomains.generate_parameters — Function.
julia
generate_parameters(d<:AbstractDomain, param)

Generates random parameters based on the domain d and the kind of parameters param.

source


- + \ No newline at end of file diff --git a/dev/constraints/constraint_models.html b/dev/constraints/constraint_models.html index d241c6d..89e0deb 100644 --- a/dev/constraints/constraint_models.html +++ b/dev/constraints/constraint_models.html @@ -8,10 +8,10 @@ - + - + @@ -47,7 +47,7 @@ # Retrieve and display the values solution = value.(grid) display(solution, Val(:sudoku))

source


- + \ No newline at end of file diff --git a/dev/constraints/constraints.html b/dev/constraints/constraints.html index 73bbc7a..0796258 100644 --- a/dev/constraints/constraints.html +++ b/dev/constraints/constraints.html @@ -8,10 +8,10 @@ - + - + @@ -20,7 +20,7 @@
Julia Constraints

Appearance

Constraints.jl: Streamlining Constraint Definition and Integration in Julia ​

Constraints.jl is a pivotal package within the JuliaConstraints ecosystem, designed to facilitate the definition, manipulation, and application of constraints in constraint programming (CP). This package is central to handling both standard and complex constraints, making it an indispensable tool for developers and researchers working in CP.

Key Features and Functionalities ​

  • Integration of XCSP3-core Constraints: One of the standout features of Constraints.jl is its incorporation of the XCSP3-core constraints as usual constraints within Julia. This integration ensures that users can define and work with a wide range of standard constraints, following the specifications outlined in the XCSP3-core, directly in Julia. The use of USUAL_CONSTRAINTS dictionary allows for straightforward addition and manipulation of these constraints, enhancing the package's utility and flexibility.

  • Learning Package Integration: Constraints.jl goes beyond traditional constraint handling by offering the capability to include results from various learning packages within the JuliaConstraints organization. This feature allows for the enhancement of usual constraints and those from the Global Constraints Catalog with learned parameters and behaviors, significantly improving constraint applicability and performance in complex CP problems.

  • Constraint Definition and Symmetry Handling: The package provides a simple yet powerful syntax for defining new constraints (@usual) and managing their symmetries through the USUAL_SYMMETRIES dictionary. This approach simplifies the creation of new constraints and the optimization of constraint search spaces by avoiding redundant explorations.

  • Advanced Constraint Functionalities: At the core of Constraints.jl is the Constraint type, encapsulating the essential elements of a constraint, including its concept (a Boolean function determining satisfaction) and an error function (providing a preference measure over invalid assignments). These components are crucial for defining how constraints behave and are evaluated within a CP model.

  • Flexible Constraint Application: The package supports a range of methods for interacting with constraints, such as args, concept, error_f, params_length, symmetries, and xcsp_intension. These methods offer users the ability to examine constraint properties, apply constraints to variable assignments, and work with intensional constraints defined by predicates. Such flexibility is vital for tailoring constraint behavior to specific problems and contexts.

Enabling Advanced Modeling in Constraint Programming ​

Constraints.jl embodies the JuliaConstraints ecosystem's commitment to providing robust, flexible tools for constraint programming. By integrating standard constraints, facilitating the incorporation of learned behaviors, and offering comprehensive tools for constraint definition and application, Constraints.jl significantly enhances the modeling capabilities available to CP practitioners. Whether for educational purposes, research, or solving practical CP problems, Constraints.jl offers a sophisticated, user-friendly platform for working with constraints in Julia.

Basic tools ​

# Constraints.USUAL_SYMMETRIES — Constant.
julia
USUAL_SYMMETRIES

A Dictionary that contains the function to apply for each symmetry to avoid searching a whole space.

source


# Constraints.Constraint — Type.
julia
Constraint

Parametric structure with the following fields.

  • concept: a Boolean function that, given an assignment x, outputs true if x satisfies the constraint, and false otherwise.

  • error: a positive function that works as preferences over invalid assignments. Return 0.0 if the constraint is satisfied, and a strictly positive real otherwise.

source


# Constraints.concept — Function.
julia
concept(c::Constraint)

Return the concept (function) of constraint c. concept(c::Constraint, x...; param = nothing) Apply the concept of c to values x and optionally param.

source

julia
concept(s::Symbol, args...; kargs...)

Return the concept of the constraint s applied to args and kargs. This is a shortcut for concept(USUAL_CONSTRAINTS[s])(args...; kargs...).

Arguments

  • s::Symbol: the constraint name.

  • args...: the arguments to apply the concept to.

  • kargs...: the keyword arguments to apply the concept to.

Example

julia
concept(:all_different, [1, 2, 3])

source


# Constraints.error_f — Function.
julia
error_f(c::Constraint)

Return the error function of constraint c. error_f(c::Constraint, x; param = nothing) Apply the error function of c to values x and optionally param.

source


# Constraints.args — Function.
julia
args(c::Constraint)

Return the expected length restriction of the arguments in a constraint c. The value nothing indicates that any strictly positive number of value is accepted.

source


# Constraints.params_length — Function.
julia
params_length(c::Constraint)

Return the expected length restriction of the arguments in a constraint c. The value nothing indicates that any strictly positive number of parameters is accepted.

source


# Constraints.symmetries — Function.
julia
symmetries(c::Constraint)

Return the list of symmetries of c.

source


# Constraints.make_error — Function.
julia
make_error(symb::Symbol)

Create a function that returns an error based on the predicate of the constraint identified by the symbol provided.

Arguments

  • symb::Symbol: The symbol used to determine the error function to be returned. The function first checks if a predicate with the prefix "icn_" exists in the Constraints module. If it does, it returns that function. If it doesn't, it checks for a predicate with the prefix "error_". If that exists, it returns that function. If neither exists, it returns a function that evaluates the predicate with the prefix "concept_" and returns the negation of its result cast to Float64.

Returns

  • Function: A function that takes in a variable x and an arbitrary number of parameters params. The function returns a Float64.

Examples

julia
e = make_error(:all_different)
 e([1, 2, 3]) # Returns 0.0
 e([1, 1, 3]) # Returns 1.0

source


# Constraints.shrink_concept — Function.
julia
shrink_concept(s)

Simply delete the concept_ part of symbol or string starting with it. TODO: add a check with a warning if s starts with something different.

source


# Constraints.concept_vs_error — Function.
julia
concept_vs_error(c, e, args...; kargs...)

Compare the results of a concept function and an error function for the same inputs. It is mainly used for testing purposes.

Arguments

  • c: The concept function.

  • e: The error function.

  • args...: Positional arguments to be passed to both the concept and error functions.

  • kargs...: Keyword arguments to be passed to both the concept and error functions.

Returns

  • Boolean: Returns true if the result of the concept function is not equal to whether the result of the error function is greater than 0.0. Otherwise, it returns false.

Examples

julia
concept_vs_error(all_different, make_error(:all_different), [1, 2, 3]) # Returns false

source


Usual constraints (based on and including XCSP3-core categories) ​

# Constraints.USUAL_CONSTRAINTS — Constant.
julia
USUAL_CONSTRAINTS::Dict

Dictionary that contains all the usual constraints defined in Constraint.jl. It is based on XCSP3-core specifications available at https://arxiv.org/abs/2009.00514

Adding a new constraint is as simple as defining a new function with the same name as the constraint and using the @usual macro to define it. The macro will take care of adding the new constraint to the USUAL_CONSTRAINTS dictionary.

Example

julia
@usual concept_all_different(x; vals=nothing) = xcsp_all_different(list=x, except=vals)

source


# Constraints.describe — Function.
julia
describe(constraints::Dict{Symbol,Constraint}=USUAL_CONSTRAINTS; width=150)

Return a pretty table with the description of the constraints in constraints.

Arguments

  • constraints::Dict{Symbol,Constraint}: dictionary of constraints to describe. Default is USUAL_CONSTRAINTS.

  • width::Int: width of the table.

Example

julia
describe()

source


# ConstraintCommons.extract_parameters — Function.
julia
extract_parameters(m::Union{Method, Function}; parameters)

Extracts the intersection between the kargs of m and parameters (defaults to USUAL_CONSTRAINT_PARAMETERS).

source

julia
extract_parameters(s::Symbol, constraints_dict=USUAL_CONSTRAINTS; parameters=ConstraintCommons.USUAL_CONSTRAINT_PARAMETERS)

Return the parameters of the constraint s in constraints_dict.

Arguments

  • s::Symbol: the constraint name.

  • constraints_dict::Dict{Symbol,Constraint}: dictionary of constraints. Default is USUAL_CONSTRAINTS.

  • parameters::Vector{Symbol}: vector of parameters. Default is ConstraintCommons.USUAL_CONSTRAINT_PARAMETERS.

Example

julia
extract_parameters(:all_different)

source


# Constraints.@usual — Macro.
julia
usual(ex::Expr)

This macro is used to define a new constraint or update an existing one in the USUAL_CONSTRAINTS dictionary. It takes an expression ex as input, which represents the definition of a constraint.

Here's a step-by-step explanation of what the macro does:

  1. It first extracts the symbol of the concept from the input expression. This symbol is expected to be the first argument of the first argument of the expression. For example, if the expression is @usual all_different(x; y=1), the symbol would be :all_different.

  2. It then calls the shrink_concept function on the symbol to get a simplified version of the concept symbol.

  3. It initializes a dictionary defaults to store whether each keyword argument of the concept has a default value or not.

  4. It checks if the expression has more than two arguments. If it does, it means that there are keyword arguments present. It then loops over these keyword arguments. If a keyword argument is a symbol, it means it doesn't have a default value, so it adds an entry to the defaults dictionary with the keyword argument as the key and false as the value. If a keyword argument is not a symbol, it means it has a default value, so it adds an entry to the defaults dictionary with the keyword argument as the key and true as the value.

  5. It calls the make_error function on the simplified concept symbol to generate an error function for the constraint.

  6. It evaluates the input expression to get the concept function.

  7. It checks if the USUAL_CONSTRAINTS dictionary already contains an entry for the simplified concept symbol. If it does, it adds the defaults dictionary to the parameters of the existing constraint. If it doesn't, it creates a new constraint with the concept function, a description, the error function, and the defaults dictionary as the parameters, and adds it to the USUAL_CONSTRAINTS dictionary.

This macro is used to make it easier to define and update constraints in a consistent and possibly automated way.

Arguments

  • ex::Expr: expression to parse.

Example

julia
@usual concept_all_different(x; vals=nothing) = xcsp_all_different(list=x, except=vals)

source


# Constraints.constraints_parameters — Function.
julia
constraints_parameters(C=USUAL_CONSTRAINTS)

Return a pretty table with the parameters of the constraints in C.

Arguments

  • C::Dict{Symbol,Constraint}: dictionary of constraints. Default is USUAL_CONSTRAINTS.

Example

julia
constraints_parameters()

source


# Constraints.constraints_descriptions — Function.
julia
constraints_descriptions(C=USUAL_CONSTRAINTS)

Return a pretty table with the descriptions of the constraints in C.

Arguments

  • C::Dict{Symbol,Constraint}: dictionary of constraints. Default is USUAL_CONSTRAINTS.

Example

julia
constraints_descriptions()

source


# Constraints.concept — Function.
julia
concept(c::Constraint)

Return the concept (function) of constraint c. concept(c::Constraint, x...; param = nothing) Apply the concept of c to values x and optionally param.

source

julia
concept(s::Symbol, args...; kargs...)

Return the concept of the constraint s applied to args and kargs. This is a shortcut for concept(USUAL_CONSTRAINTS[s])(args...; kargs...).

Arguments

  • s::Symbol: the constraint name.

  • args...: the arguments to apply the concept to.

  • kargs...: the keyword arguments to apply the concept to.

Example

julia
concept(:all_different, [1, 2, 3])

source


- + \ No newline at end of file diff --git a/dev/constraints/counting_summing_constraints.html b/dev/constraints/counting_summing_constraints.html index 08a7160..3ced709 100644 --- a/dev/constraints/counting_summing_constraints.html +++ b/dev/constraints/counting_summing_constraints.html @@ -8,10 +8,10 @@ - + - + @@ -56,7 +56,7 @@ co = concept(:cardinality_open) co([8, 5, 10, 10]; vals=[2 0 1; 5 1 3; 10 2 3])

source


- + \ No newline at end of file diff --git a/dev/constraints/elementary_constraints.html b/dev/constraints/elementary_constraints.html index 136cd67..50d72ee 100644 --- a/dev/constraints/elementary_constraints.html +++ b/dev/constraints/elementary_constraints.html @@ -8,10 +8,10 @@ - + - + @@ -22,7 +22,7 @@ c([1, 2, 3, 4, 5]; pair_vars=[1, 2, 3, 4, 5]) c([1, 2, 3, 4, 5]; pair_vars=[1, 2, 3, 4, 6])

source


- + \ No newline at end of file diff --git a/dev/constraints/generic_constraints.html b/dev/constraints/generic_constraints.html index 450dbbd..8e907c9 100644 --- a/dev/constraints/generic_constraints.html +++ b/dev/constraints/generic_constraints.html @@ -8,11 +8,11 @@ - + - - + + @@ -28,7 +28,7 @@ @usual concept_dist_different(x) = xcsp_intension( list = x, predicate = predicate_dist_different -)

Please check the section dedicated to the Golomb Ruler problem to see a use for this constraint. <!– TODO: Golomb Ruler –>

APIs ​

Note that the intension constraint is not directly available through the JC-API in Constraints.jl. It is designed as such since defining a constraint through a predicate is the natural way.

We provide here a usage example for the :dist_different constraint, previously added to the USUAL_CONSTRAINTS collection.

Higher level modeling language such as JuMP should provide an Intension interface.

julia
concept(:dist_different, x)
+)

Please check the section dedicated to the Golomb Ruler problem to see a use for this constraint. <!– TODO: Golomb Ruler –>

APIs ​

Note that the intension constraint is not directly available through the JC-API in Constraints.jl. It is designed as such since defining a constraint through a predicate is the natural way.

We provide here a usage example for the :dist_different constraint, previously added to the USUAL_CONSTRAINTS collection.

Higher level modeling language such as JuMP should provide an Intension interface.

julia
concept(:dist_different, x)
 concept(:dist_different)(x)
julia
# Defines the DistDifferent constraint
 c = x -> xcsp_intension(
     list = x,
@@ -56,7 +56,7 @@
 
 c = concept(:conflicts)
 c([1, 2, 3, 4, 5]; pair_vars=[[1, 2, 1, 4, 5], [1, 2, 3, 5, 5]])

source


- + \ No newline at end of file diff --git a/dev/constraints/graph_constraints.html b/dev/constraints/graph_constraints.html index 2fca37d..a700b09 100644 --- a/dev/constraints/graph_constraints.html +++ b/dev/constraints/graph_constraints.html @@ -8,10 +8,10 @@ - + - + @@ -24,7 +24,7 @@ c([2, 3, 4, 1]) c([2, 3, 1, 4]; op = ==, val = 3) c([4, 3, 1, 3]; op = >, val = 0)

source


- + \ No newline at end of file diff --git a/dev/constraints/intro.html b/dev/constraints/intro.html index e6e010f..ee20bec 100644 --- a/dev/constraints/intro.html +++ b/dev/constraints/intro.html @@ -8,17 +8,17 @@ - + - +
Julia Constraints

Appearance

Introduction to basics cosntraints related tools ​

About constraints.

- + \ No newline at end of file diff --git a/dev/constraints/language_constraints.html b/dev/constraints/language_constraints.html index 122db15..31d7067 100644 --- a/dev/constraints/language_constraints.html +++ b/dev/constraints/language_constraints.html @@ -8,10 +8,10 @@ - + - + @@ -65,7 +65,7 @@ c([2,1,2]; language = a) c([1,0,2]; language = a) c([0,1,2]; language = a)

source


- + \ No newline at end of file diff --git a/dev/constraints/packing_scheduling_constraints.html b/dev/constraints/packing_scheduling_constraints.html index 020e3d7..2986fa3 100644 --- a/dev/constraints/packing_scheduling_constraints.html +++ b/dev/constraints/packing_scheduling_constraints.html @@ -8,10 +8,10 @@ - + - + @@ -35,7 +35,7 @@ c([1, 2, 4, 6, 3]; pair_vars = [1, 1, 3, 1, 1]) c([1, 1, 1, 3, 5, 2, 7, 7, 5, 12, 8, 7]; pair_vars = [2, 4, 1, 4 ,2 ,3, 5, 1, 2, 3, 3, 2], dim = 3) c([1, 1, 1, 2, 2, 2, 3, 3, 3, 4, 4, 4]; pair_vars = [2, 4, 1, 4 ,2 ,3, 5, 1, 2, 3, 3, 2], dim = 3)

source


- + \ No newline at end of file diff --git a/dev/cp/advanced.html b/dev/cp/advanced.html index 864b812..27993c2 100644 --- a/dev/cp/advanced.html +++ b/dev/cp/advanced.html @@ -8,17 +8,17 @@ - + - +
Julia Constraints

Appearance

Advanced Constraint Programming Techniques ​

Global Constraints and Their Uses ​

  • Dive deeper into global constraints and how they simplify complex problems.

Search Strategies and Optimization ​

  • Discuss various search strategies and their impact on solving CP problems.
- + \ No newline at end of file diff --git a/dev/cp/applications.html b/dev/cp/applications.html index b4e0ff4..16b82b5 100644 --- a/dev/cp/applications.html +++ b/dev/cp/applications.html @@ -8,17 +8,17 @@ - + - +
Julia Constraints

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Applying Optimization Methods ​

Case Studies and Real-World Applications ​

  • Showcase studies where CP and optimization have been successfully applied.

From Theory to Practice ​

  • Guide readers through the process of formulating and solving an optimization problem from a real-world scenario.
- + \ No newline at end of file diff --git a/dev/cp/contribution.html b/dev/cp/contribution.html index 79c3203..8c1f86b 100644 --- a/dev/cp/contribution.html +++ b/dev/cp/contribution.html @@ -8,17 +8,17 @@ - + - +
Julia Constraints

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Community and Contribution ​

Joining the JuliaConstraint Community ​

  • Encourage readers to join the community, highlighting how they can contribute and collaborate.

Future Directions ​

  • Share the vision for JuliaConstraint and upcoming projects or areas of research.
- + \ No newline at end of file diff --git a/dev/cp/cp101.html b/dev/cp/cp101.html index e57c10d..eb8afb5 100644 --- a/dev/cp/cp101.html +++ b/dev/cp/cp101.html @@ -8,17 +8,17 @@ - + - +
Julia Constraints

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Constraint Programming 101 ​

What is Constraint Programming? ​

  • Define CP and its significance in solving combinatorial problems.

Basic Concepts and Terminology ​

  • Introduce key concepts such as constraints, domains, and variables.

How CP differs from other optimization techniques ​

  • Contrast with other methods like linear programming and metaheuristics.
- + \ No newline at end of file diff --git a/dev/cp/ecosystem.html b/dev/cp/ecosystem.html index 58c4c47..e18ccd5 100644 --- a/dev/cp/ecosystem.html +++ b/dev/cp/ecosystem.html @@ -8,17 +8,17 @@ - + - +
Julia Constraints

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Exploring JuliaConstraint Packages ​

Package Overviews ​

  • Introduce each package within the JuliaConstraint organization, its purpose, and primary features.

Installation and Getting Started Guides ​

  • Provide step-by-step instructions for installing and getting started with each package.
- + \ No newline at end of file diff --git a/dev/cp/getting_started.html b/dev/cp/getting_started.html index 60ed97b..c8b14a8 100644 --- a/dev/cp/getting_started.html +++ b/dev/cp/getting_started.html @@ -8,25 +8,25 @@ - + - - + + -
Julia Constraints

Appearance

Getting Started with Julia for CP and Optimization ​

Why Julia? ​

  • Discuss the advantages of Julia for computational science and optimization, highlighting its performance and ease of use.

Setting Up Your Julia Environment ​

We encourage users to install Julia through juliaup, a version manager for the Julia language. Please look at the official Julia language download page for further information. Once installed, Julia can be used through various editors (Visual Studio Code), notebooks (Pluto.jl), or command-line (REPL).

Although a part of the CP solvers available within the Julia ecosystem have their own interface, we encourage users to use the JuMP modeling language if possible.

Julia Constraints host several solvers(' interfaces). Due to its flexibility in modeling and solving, we will use LocalSearchSolvers.jl through its JuMP interface CBLS.jl as the basic example. Note that depending on the targeted instances, available hardware, and expectations, it is not necessarily the best choice.

All along the documentation, we will try to provide syntax examples for different setup.

julia
using LocalSearchSolvers
julia
using JuMP, CBLS
julia
# TODO: Add other solvers

Your First Julia CP Model ​

We will start with a classic puzzle game and some of its not that simple variants: the Sudoku.

(From Wikipedia) In classic Sudoku, the objective is to fill a 9 × 9 grid with digits so that each column, each row, and each of the nine 3 × 3 subgrids that compose the grid (also called "boxes", "blocks", or "regions") contains all of the digits from 1 to 9. The puzzle setter provides a partially completed grid, which for a well-posed puzzle has a single solution.

Constraint Programming follows the model-and-solve approach. We first need to model our Sudoku problem.

julia
m = JuMP.Model(CBLS.Optimizer)
julia
# TODO: Add other solvers

But what are the basis of CP models? It is quite simple:

  1. A collection X=X1,⋯,Xn of variables with each an associated domain.
julia
@variable(m, 1 ≤ X[1:9, 1:9] ≤ 9, Int)
julia
# TODO: Add other solvers
  1. A collection of predicates (called constraints) C=C1,⋯,Cn over (subsets of) X.

When modeling problems as CP, one might define and use their own predicates. However, a large collection of already defined constraints exists. One, if not the most, iconic global constraint is called AllDifferent. It ensures that all variables take distinct values.

Sudoku puzzles can be defined using only this one constraint applied to different subsets of variables.

julia
for i in 1:9
+    
Julia Constraints
Appearance
Getting Started with Julia for CP and Optimization ​
Why Julia? ​
  • Discuss the advantages of Julia for computational science and optimization, highlighting its performance and ease of use.
Setting Up Your Julia Environment ​
We encourage users to install Julia through juliaup, a version manager for the Julia language. Please look at the official Julia language download page for further information. Once installed, Julia can be used through various editors (Visual Studio Code), notebooks (Pluto.jl), or command-line (REPL).
Although a part of the CP solvers available within the Julia ecosystem have their own interface, we encourage users to use the JuMP modeling language if possible.
Julia Constraints host several solvers(' interfaces). Due to its flexibility in modeling and solving, we will use LocalSearchSolvers.jl through its JuMP interface CBLS.jl as the basic example. Note that depending on the targeted instances, available hardware, and expectations, it is not necessarily the best choice.
All along the documentation, we will try to provide syntax examples for different setup.
julia
using LocalSearchSolvers
julia
using JuMP, CBLS
julia
# TODO: Add other solvers
Your First Julia CP Model ​
We will start with a classic puzzle game and some of its not that simple variants: the Sudoku.
(From Wikipedia) In classic Sudoku, the objective is to fill a 9 × 9 grid with digits so that each column, each row, and each of the nine 3 × 3 subgrids that compose the grid (also called "boxes", "blocks", or "regions") contains all of the digits from 1 to 9. The puzzle setter provides a partially completed grid, which for a well-posed puzzle has a single solution.
Constraint Programming follows the model-and-solve approach. We first need to model our Sudoku problem.
julia
m = JuMP.Model(CBLS.Optimizer)
julia
# TODO: Add other solvers
But what are the basis of CP models? It is quite simple:
  1. A collection X=X1,⋯,Xn of variables with each an associated domain.
julia
@variable(m, 1 ≤ X[1:9, 1:9] ≤ 9, Int)
julia
# TODO: Add other solvers
  1. A collection of predicates (called constraints) C=C1,⋯,Cn over (subsets of) X.
When modeling problems as CP, one might define and use their own predicates. However, a large collection of already defined constraints exists. One, if not the most, iconic global constraint is called AllDifferent. It ensures that all variables take distinct values.
Sudoku puzzles can be defined using only this one constraint applied to different subsets of variables.
julia
for i in 1:9
         @constraint(m, X[i,:] in AllDifferent()) # rows
         @constraint(m, X[:,i] in AllDifferent()) # columns
-end
julia
# TODO: Add other solvers
The last series of AllDifferent constraint is less straight forward. We need to ensure that each 3 × 3 subgrid (block) is filled with distinct values.
julia
for i in 0:2, j in 0:2 # blocks
+end
julia
# TODO: Add other solvers
The last series of AllDifferent constraint is less straight forward. We need to ensure that each 3 × 3 subgrid (block) is filled with distinct values.
julia
for i in 0:2, j in 0:2 # blocks
     @constraint(
         m,
         vec(X[(3i+1):(3(i+1)), (3j+1):(3(j+1))]) in AllDifferent(),
     )
-end
julia
# TODO: Add other solvers
We can now simply run our solver to look for a feasible solution.
julia
optimize!(m)
Note that this is heuristic solver, we might not get a feasible solution! Let's check it out. The value function print the value of a JuMP variable. We can cast it over a collection with the value. syntax.
julia
value.(X)

-    
+end
julia
# TODO: Add other solvers

We can now simply run our solver to look for a feasible solution.

julia
optimize!(m)

Note that this is heuristic solver, we might not get a feasible solution! Let's check it out. The value function print the value of a JuMP variable. We can cast it over a collection with the value. syntax.

julia
value.(X)
+ \ No newline at end of file diff --git a/dev/cp/intro.html b/dev/cp/intro.html index c64fa2c..9f82d04 100644 --- a/dev/cp/intro.html +++ b/dev/cp/intro.html @@ -8,17 +8,17 @@ - + - +
Julia Constraints

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Welcome to Julia Constraints ​

An introductory post/chapter that provides an overview of the JuliaConstraint organization, its mission, and what readers can expect to learn from the content. Highlight the importance of Constraint Programming (CP) and optimization in solving real-world problems.

- + \ No newline at end of file diff --git a/dev/cp/models.html b/dev/cp/models.html index 3cc4a11..99058ce 100644 --- a/dev/cp/models.html +++ b/dev/cp/models.html @@ -8,17 +8,17 @@ - + - +
Julia Constraints

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Building and Analyzing Models ​

Modeling Best Practices ​

  • Share best practices and tips for building efficient CP and optimization models.

Performance Analysis and Improvement ​

  • Teach how to analyze and improve the performance of models.
- + \ No newline at end of file diff --git a/dev/cp/opt.html b/dev/cp/opt.html index c8d2ac2..afceb21 100644 --- a/dev/cp/opt.html +++ b/dev/cp/opt.html @@ -8,17 +8,17 @@ - + - +
Julia Constraints

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Dive into Optimization ​

Understanding Optimization ​

  • Explanation of optimization, types of optimization problems (e.g., linear, nonlinear, integer programming).

Metaheuristics Overview ​

  • Introduce concepts like Genetic Algorithms, Simulated Annealing, and Tabu Search.

Mathematical Programming Basics ​

  • Cover the fundamentals of mathematical programming and its role in optimization.
- + \ No newline at end of file diff --git a/dev/cp/tuto_xp.html b/dev/cp/tuto_xp.html index ad6a820..9eb30ab 100644 --- a/dev/cp/tuto_xp.html +++ b/dev/cp/tuto_xp.html @@ -8,17 +8,17 @@ - + - +
Julia Constraints

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Tutorials and Experiments ​

Hands-On Tutorials ​

  • Provide step-by-step tutorials covering various topics and complexity levels.

Experimental Analysis ​

  • Discuss the importance of experimental analysis in CP and how to conduct meaningful experiments.
- + \ No newline at end of file diff --git a/dev/full_api.html b/dev/full_api.html index ceeec3a..0cd5c61 100644 --- a/dev/full_api.html +++ b/dev/full_api.html @@ -8,10 +8,10 @@ - + - + @@ -272,7 +272,7 @@ tr_param_minus_val(x, X::AbstractVector; param)

Return the difference param - x[i] if positive, 0.0 otherwise. Extended method to vector with sig (x, param) are generated. When X is provided, the result is computed without allocations.

source


# CompositionalNetworks.tr_val_minus_param — Method.
julia
tr_val_minus_param(i, x; param)
 tr_val_minus_param(x; param)
 tr_val_minus_param(x, X::AbstractVector; param)

Return the difference x[i] - param if positive, 0.0 otherwise. Extended method to vector with sig (x, param) are generated. When X is provided, the result is computed without allocations.

source


# CompositionalNetworks.transformation_layer — Function.
julia
transformation_layer(param = false)

Generate the layer of transformations functions of the ICN. Iff param value is true, also includes all the parametric transformations.

source


# CompositionalNetworks.weights! — Method.
julia
weights!(icn, weights)

Set the weights of an ICN with a BitVector.

source


# CompositionalNetworks.weights — Method.
julia
weights(icn)

Access the current set of weights of an ICN.

source


# CompositionalNetworks.weights_bias — Method.
julia
weights_bias(x)

A metric that bias x towards operations with a lower bit. Do not affect the main metric.

source


# QUBOConstraints.AbstractOptimizer — Type.
julia
AbstractOptimizer

An abstract type (interface) used to learn QUBO matrices from constraints. Only a train method is required.

source


# QUBOConstraints.QUBO_base — Function.
julia
QUBO_base(n, weight = 1)

A basic QUBO matrix to ensure that binarized variables keep a valid encoding.

source


# QUBOConstraints.QUBO_linear_sum — Method.
julia
QUBO_linear_sum(n, σ)

One valid QUBO matrix given n variables and parameter σ for the linear sum constraint.

source


# QUBOConstraints.binarize — Method.
julia
binarize(x[, domain]; binarization = :one_hot)

Binarize x following the binarization encoding. If x is a vector (instead of a number per say), domain is optional.

source


# QUBOConstraints.debinarize — Method.
julia
debinarize(x[, domain]; binarization = :one_hot)

Transform a binary vector into a number or a set of number. If domain is not given, it will compute a default value based on binarization and x.

source


# QUBOConstraints.is_valid — Function.
julia
is_valid(x, encoding::Symbol = :none)

Check if x has a valid format for encoding.

For instance, if encoding == :one_hot, at most one bit of x can be set to 1.

source


# QUBOConstraints.train — Method.
julia
train(args...)

Default train method for any AbstractOptimizer.

source


- + \ No newline at end of file diff --git a/dev/hashmap.json b/dev/hashmap.json index 90c26f7..99f1e8c 100644 --- a/dev/hashmap.json +++ b/dev/hashmap.json @@ -1 +1 @@ -{"constraints_comparison_constraints.md":"2Ukc8viw","constraints_connection_constraints.md":"0kSxmxNT","solvers_intro.md":"BOddHRCt","constraints_constraint_models.md":"DjPMcFlD","perf_perf_interface.md":"DaCOMv6z","index-old.md":"BzPVACYs","index.md":"BcgCFTkL","learning_aggregation.md":"5RLMQ4Bd","learning_arithmetic.md":"DW5u7RMW","learning_comparison.md":"BsnqPWC-","learning_compositional_networks.md":"BZgNyUW3","constraints_intro.md":"SiDBJ4N_","learning_intro.md":"497AVcuz","learning_constraint_learning.md":"DAfFojnp","constraints_language_constraints.md":"CE20vPnP","cp_opt.md":"oWksNiMs","solvers_cbls.md":"BxYapv-Y","cp_tuto_xp.md":"CLy9H2hK","cp_models.md":"DjhzTYet","learning_qubo_constraints.md":"DpCFckdQ","cp_contribution.md":"CvjPxUVA","learning_qubo_encoding.md":"CDyoKOWI","learning_qubo_learning.md":"CLlNBMzd","constraints_constraint_domains.md":"Cd0YH57K","constraints_constraints.md":"ijIsQmJK","constraints_packing_scheduling_constraints.md":"CSsKQ9F3","constraints_counting_summing_constraints.md":"BIdrSepq","meta_meta_strategist.md":"CuHkGJNL","constraints_graph_constraints.md":"CTf_lkG3","cp_advanced.md":"Dsfkdtcs","perf_perf_checker.md":"CXuw3agL","cp_ecosystem.md":"BROmdRLS","constraints_generic_constraints.md":"Yd2fivbQ","learning_layers.md":"DuFC9ol_","constraints_elementary_constraints.md":"BbiPuUtE","constraints_constraint_commons.md":"PTLdrUW2","public_api.md":"ByXB8t1V","cp_intro.md":"qCFhsnKE","perf_benchmark_ext.md":"CVYCQYDt","cp_getting_started.md":"BYxKzX2X","cp_applications.md":"-HdwrgYe","cp_cp101.md":"CrtqNaW3","solvers_local_search_solvers.md":"BuFLqJZe","learning_transformation.md":"BtKPMNbs","full_api.md":"BiWge5EP"} +{"constraints_comparison_constraints.md":"2Ukc8viw","constraints_connection_constraints.md":"0kSxmxNT","constraints_constraint_commons.md":"PTLdrUW2","cp_cp101.md":"CrtqNaW3","constraints_constraint_domains.md":"Cd0YH57K","constraints_constraints.md":"ijIsQmJK","constraints_counting_summing_constraints.md":"BIdrSepq","cp_advanced.md":"Dsfkdtcs","cp_applications.md":"-HdwrgYe","cp_contribution.md":"CvjPxUVA","constraints_constraint_models.md":"DjPMcFlD","cp_intro.md":"qCFhsnKE","cp_models.md":"DjhzTYet","cp_ecosystem.md":"BROmdRLS","cp_getting_started.md":"CgxLSopb","cp_opt.md":"oWksNiMs","index.md":"BcgCFTkL","learning_aggregation.md":"5RLMQ4Bd","index-old.md":"BzPVACYs","cp_tuto_xp.md":"CLy9H2hK","learning_arithmetic.md":"DW5u7RMW","learning_comparison.md":"BsnqPWC-","constraints_graph_constraints.md":"CTf_lkG3","learning_qubo_constraints.md":"DpCFckdQ","learning_qubo_encoding.md":"CDyoKOWI","learning_layers.md":"DuFC9ol_","meta_meta_strategist.md":"CuHkGJNL","learning_qubo_learning.md":"CLlNBMzd","perf_benchmark_ext.md":"CVYCQYDt","learning_transformation.md":"BtKPMNbs","constraints_elementary_constraints.md":"BbiPuUtE","constraints_intro.md":"SiDBJ4N_","perf_perf_checker.md":"C3kXwfzJ","constraints_language_constraints.md":"CE20vPnP","constraints_packing_scheduling_constraints.md":"CSsKQ9F3","learning_intro.md":"497AVcuz","learning_compositional_networks.md":"BZgNyUW3","learning_constraint_learning.md":"DAfFojnp","perf_perf_interface.md":"DaCOMv6z","solvers_intro.md":"BOddHRCt","constraints_generic_constraints.md":"WUfYZUE9","public_api.md":"ByXB8t1V","solvers_cbls.md":"BxYapv-Y","solvers_local_search_solvers.md":"BuFLqJZe","full_api.md":"BiWge5EP"} diff --git a/dev/index-old.html b/dev/index-old.html index 37360fa..a0dd0c4 100644 --- a/dev/index-old.html +++ b/dev/index-old.html @@ -8,17 +8,17 @@ - + - +
Julia Constraints

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JuliaConstraints ​

JuliaConstraints is a collection of packages that help you solve constraint programming problems in Julia. Constraint programming involves modeling problems with constraints, such as "x > 5" or "x + y = 10", and finding solutions that satisfy all of the constraints. It is a part of the JuMP ecosystem that focuses on constraint programming in Julia.

The goal of packages in JuliaConstraints are two-fold: some of them provide a generic interface, others are solvers for CP models (either purely in Julia or wrapping). They make it easy to solve constraint-satisfaction problems (CSPs) and constraint-optimisation problems (COPs) in Julia using industry-standard solvers and mixed-integer solvers.

Other packages for CP in Julia include:

Operational Research vs Constraint Programming ​

Operational research (OR) is a problem-solving approach that uses mathematical models, statistical analysis, and optimization techniques to help organizations make better decisions. OR is concerned with understanding and optimizing complex systems, such as supply chains, transportation networks, and manufacturing processes, to improve efficiency and reduce costs.

On the other hand, constraint programming (CP) is a programming paradigm that focuses on solving problems with constraints. Constraints are conditions that must be satisfied for a solution to be valid. CP is often used to solve combinatorial problems, such as scheduling, routing, and allocation, where the search space of possible solutions is very large.

So, while both OR and CP are concerned with solving complex problems, they approach the problem-solving process from different angles. OR typically uses mathematical models and optimization techniques to analyze and optimize existing systems, while CP focuses on finding valid solutions that satisfy a set of constraints.

Constraint-based local search (CBLS) is a type of constraint programming solver that uses a heuristic search algorithm to find solutions to problems. It starts with an initial solution and tries to improve it by making small changes that satisfy the constraints. CBLS is especially useful for large and complex problems where finding an exact solution may take too much time or be impossible.

In contrast, other constraint programming solvers use a variety of algorithms and techniques to find exact solutions to problems. These solvers try to find a solution that satisfies all of the constraints in the problem. They can be useful for smaller problems where finding an exact solution is feasible, or for problems that have a clear mathematical structure.

In summary, CBLS is a type of constraint programming solver that uses a heuristic search algorithm to find good solutions, while other constraint programming solvers use various techniques to find exact solutions to problems.

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Julia Constraints

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Julia Constraints

Model Smoothly Decide Wisely

A Toolkit for Constraint Programming

Constraint Programming ?!
View on Github
JuliaConstraints
JuMP.jl

JuMP.jl

Model optimization problems via JuMP.jl!

ToQUBO.jl

ToQUBO.jl

Convert and send optimization models to QUBO solvers!

Pluto.jl

Pluto.jl

Simple, reactive programming environment via Julia notebooks

<p style="margin-bottom:2cm"></p>

<div class="vp-doc" style="width:80%; margin:auto">

<h1>What is Julia Constraints? (chatGPTed atm)</h1>

<p>The Julia Constraints organization is dedicated to advancing Constraint Programming within the Julia ecosystem, serving as a hub for resources that facilitate the creation, understanding, and solution of constraint programming problems. Our goal is to make Constraint Programming accessible and efficient for users at all levels of expertise, by providing a comprehensive suite of tools that integrate seamlessly with JuMP.jl, a popular Julia package for mathematical optimization.</p>

<h2>Our offerings include:</h2>

<h3>Core Packages:</h3> <p>A foundation of common packages (ConstraintCommons, ConstraintDomains, Constraints, ConstraintModels) that supply essential features for constraint programming, ensuring users have the basic tools necessary for their projects.</p>

<h3>Learning and Translation Tools:</h3> <p>Advanced packages like CompositionalNetworks, QUBOConstraints, and ConstraintsTranslator bridge the gap between ease of modeling and computational efficiency. These tools learn from constraints and convert natural language problems into constraint programming solutions, requiring minimal input from the user beyond the model itself.</p>

<h3>Solvers:</h3> <p>We provide a range of solvers, from native Julia solvers (LocalSearchSolvers) to interfaces with JuMP for external CP solvers, catering to various problem-solving needs.</p>

<h3>MetaStrategist (Emerging Technology):</h3> <p>In its formative stages, MetaStrategist embodies our pioneering spirit. As a burgeoning meta-solving package, it aims to harness the strengths of CP and JuMP. Its vision is to formulate tailored strategies that consider the unique hardware and software resources at hand, offering a new horizon in problem-solving efficiency and adaptability.</p>

<h3>Performance Checker (Community Resource):</h3> <p>PerfChecker.jl transcends its role within Julia Constraints, offering its capabilities to the broader Julia package ecosystem. This indispensable tool for cross-version performance checking not only safeguards the high efficiency and reliability of our packages but also serves the wider community. By facilitating clear and simple performance evaluations, PerfChecker.jl enhances both development and maintenance, contributing to the overall health and progress of Julia's growing library of resources.</p>

<p>At Julia Constraints, our mission is to democratize Constraint Programming by providing robust, user-friendly tools that simplify the modeling process, enhance efficiency, and empower users to solve complex problems with ease.</p>

</div>

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Julia Constraints

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Aggregation Layer ​

Some text to describe the aggragation layer within usual ICNs.

List of aggregations ​

# CompositionalNetworks.ag_sum — Function.
julia
ag_sum(x)

Aggregate through + a vector into a single scalar.

source


# CompositionalNetworks.ag_count_positive — Function.
julia
ag_count_positive(x)

Count the number of strictly positive elements of x.

source


Layer generation ​

# CompositionalNetworks.aggregation_layer — Function.
julia
aggregation_layer()

Generate the layer of aggregations of the ICN. The operations are mutually exclusive, that is only one will be selected.

source


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Julia Constraints

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Arithmetic Layer ​

Some text to describe the arithmetic layer within usual ICNs.

List of arithmetic operations ​

# CompositionalNetworks.ar_sum — Function.
julia
ar_sum(x)

Reduce k = length(x) vectors through sum to a single vector.

source


# CompositionalNetworks.ar_prod — Function.
julia
ar_prod(x)

Reduce k = length(x) vectors through product to a single vector.

source


Layer generation ​

# CompositionalNetworks.arithmetic_layer — Function.
julia
arithmetic_layer()

Generate the layer of arithmetic operations of the ICN. The operations are mutually exclusive, that is only one will be selected.

source


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Julia Constraints

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Comparison Layer ​

Some text to describe the comparison layer within usual ICNs.

List of comparisons ​

List the possible parameters and how it affects the comparison.

Non-parametric ​

# CompositionalNetworks.co_identity — Function.
julia
co_identity(x)

Identity function. Already defined in Julia as identity, specialized for scalars in the comparison layer.

source


Missing docstring.

Missing docstring for co_euclidian. Check Documenter's build log for details.

# CompositionalNetworks.co_abs_diff_val_vars — Function.
julia
co_abs_diff_val_vars(x; nvars)

Return the absolute difference between x and the number of variables nvars.

source


# CompositionalNetworks.co_val_minus_vars — Function.
julia
co_val_minus_vars(x; nvars)

Return the difference x - nvars if positive, 0.0 otherwise, where nvars denotes the numbers of variables.

source


# CompositionalNetworks.co_vars_minus_val — Function.
julia
co_vars_minus_val(x; nvars)

Return the difference nvars - x if positive, 0.0 otherwise, where nvars denotes the numbers of variables.

source


Param: :val ​

# CompositionalNetworks.co_abs_diff_val_param — Function.
julia
co_abs_diff_val_param(x; param)

Return the absolute difference between x and param.

source


# CompositionalNetworks.co_val_minus_param — Function.
julia
co_val_minus_param(x; param)

Return the difference x - param if positive, 0.0 otherwise.

source


# CompositionalNetworks.co_param_minus_val — Function.
julia
co_param_minus_val(x; param)

Return the difference param - x if positive, 0.0 otherwise.

source


Missing docstring.

Missing docstring for co_euclidian_param. Check Documenter's build log for details.

Layer generation ​

Missing docstring.

Missing docstring for make_comparisons. Check Documenter's build log for details.

# CompositionalNetworks.comparison_layer — Function.
julia
comparison_layer(param = false)

Generate the layer of transformations functions of the ICN. Iff param value is set, also includes all the parametric comparison with that value. The operations are mutually exclusive, that is only one will be selected.

source


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Julia Constraints

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CompositionalNetworks.jl ​

Documentation for CompositionalNetworks.jl.

Utilities ​

# CompositionalNetworks.map_tr! — Function.
julia
map_tr!(f, x, X, param)

Return an anonymous function that applies f to all elements of x and store the result in X, with a parameter param (which is set to nothing for function with no parameter).

source


# CompositionalNetworks.lazy — Function.
julia
lazy(funcs::Function...)

Generate methods extended to a vector instead of one of its components. A function f should have the following signature: f(i::Int, x::V).

source


# CompositionalNetworks.lazy_param — Function.
julia
lazy_param(funcs::Function...)

Generate methods extended to a vector instead of one of its components. A function f should have the following signature: f(i::Int, x::V; param).

source


# CompositionalNetworks.as_bitvector — Function.
julia
as_bitvector(n::Int, max_n::Int = n)

Convert an Int to a BitVector of minimal size (relatively to max_n).

source


# CompositionalNetworks.as_int — Function.
julia
as_int(v::AbstractVector)

Convert a BitVector into an Int.

source


# CompositionalNetworks.reduce_symbols — Function.
julia
reduce_symbols(symbols, sep)

Produce a formatted string that separates the symbols by sep. Used internally for show_composition.

source


Missing docstring.

Missing docstring for CompositionalNeworks.tr_in. Check Documenter's build log for details.

Metrics ​

# CompositionalNetworks.hamming — Function.
julia
hamming(x, X)

Compute the hamming distance of x over a collection of solutions X, i.e. the minimal number of variables to switch in xto reach a solution.

source


# CompositionalNetworks.minkowski — Function.
julia
minkowski(x, X, p)

source


# CompositionalNetworks.manhattan — Function.
julia
manhattan(x, X)

source


Missing docstring.

Missing docstring for weigths_bias. Check Documenter's build log for details.

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Julia Constraints

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ConstraintLearning.jl ​

Documentation for ConstraintLearning.jl.

# ConstraintLearning.ICNConfig — Type.
julia
struct ICNConfig{O <: ICNOptimizer}

A structure to hold the metric and optimizer configurations used in learning the weights of an ICN.

source


# ConstraintLearning.ICNConfig — Method.
julia
ICNConfig(; metric = :hamming, optimizer = ICNGeneticOptimizer())

Constructor for ICNConfig. Defaults to hamming metric using a genetic algorithm.

source


# ConstraintLearning.ICNGeneticOptimizer — Method.
julia
ICNGeneticOptimizer(; kargs...)

Default constructor to learn an ICN through a Genetic Algorithm. Default kargs TBW.

source


# ConstraintLearning.ICNLocalSearchOptimizer — Type.
julia
ICNLocalSearchOptimizer(options = LocalSearchSolvers.Options())

Default constructor to learn an ICN through a CBLS solver.

source


# ConstraintLearning.ICNOptimizer — Type.
julia
const ICNOptimizer = CompositionalNetworks.AbstractOptimizer

An abstract type for optmizers defined to learn ICNs.

source


# ConstraintLearning.QUBOGradientOptimizer — Method.
julia
QUBOGradientOptimizer(; kargs...)

A QUBO optimizer based on gradient descent. Defaults TBW

source


# ConstraintLearning.QUBOOptimizer — Type.
julia
const QUBOOptimizer = QUBOConstraints.AbstractOptimizer

An abstract type for optimizers used to learn QUBO matrices from constraints.

source


# CompositionalNetworks.optimize! — Method.
julia
CompositionalNetworks.optimize!(icn, solutions, non_sltns, dom_size, metric, optimizer::ICNGeneticOptimizer; parameters...)

Extends the optimize! method to ICNGeneticOptimizer.

source


# CompositionalNetworks.optimize! — Method.
julia
CompositionalNetworks.optimize!(icn, solutions, non_sltns, dom_size, metric, optimizer::ICNLocalSearchOptimizer; parameters...)

Extends the optimize! method to ICNLocalSearchOptimizer.

source


# ConstraintLearning._optimize! — Method.
julia
_optimize!(icn, X, X_sols; metric = hamming, pop_size = 200)

Optimize and set the weights of an ICN with a given set of configuration X and solutions X_sols.

source


# ConstraintLearning.domain_size — Method.
julia
domain_size(ds::Number)

Extends the domain_size function when ds is number (for dispatch purposes).

source


# ConstraintLearning.generate_population — Method.
julia
generate_population(icn, pop_size

Generate a pôpulation of weights (individuals) for the genetic algorithm weighting icn.

source


# ConstraintLearning.icn — Method.
julia
icn(X,X̅; kargs..., parameters...)

TBW

source


# ConstraintLearning.loss — Method.
julia
loss(x, y, Q)

Loss of the prediction given by Q, a training set y, and a given configuration x.

source


# ConstraintLearning.make_df — Method.
julia
make_df(X, Q, penalty, binarization, domains)

DataFrame arrangement to output some basic evaluation of a matrix Q.

source


# ConstraintLearning.make_set_penalty — Method.
julia
make_set_penalty(X, X̅, args...; kargs)

Return a penalty function when the training set is already split into a pair of solutions X and non solutions X̅.

source


# ConstraintLearning.make_training_sets — Method.
julia
make_training_sets(X, penalty, args...)

Return a pair of solutions and non solutions sets based on X and penalty.

source


# ConstraintLearning.mutually_exclusive — Method.
julia
mutually_exclusive(layer, w)

Constraint ensuring that w encode exclusive operations in layer.

source


# ConstraintLearning.no_empty_layer — Method.
julia
no_empty_layer(x; X = nothing)

Constraint ensuring that at least one operation is selected.

source


# ConstraintLearning.optimize! — Method.
julia
optimize!(icn, X, X_sols, global_iter, local_iter; metric=hamming, popSize=100)

Optimize and set the weights of an ICN with a given set of configuration X and solutions X_sols. The best weights among global_iter will be set.

source


# ConstraintLearning.parameter_specific_operations — Method.
julia
parameter_specific_operations(x; X = nothing)

Constraint ensuring that at least one operation related to parameters is selected if the error function to be learned is parametric.

source


# ConstraintLearning.predict — Method.
julia
predict(x, Q)

Return the predictions given by Q for a given configuration x.

source


# ConstraintLearning.preliminaries — Method.
julia
preliminaries(args)

Preliminaries to the training process in a QUBOGradientOptimizer run.

source


# ConstraintLearning.qubo — Function.
julia
qubo(X,X̅; kargs..., parameters...)

TBW

source


# ConstraintLearning.sub_eltype — Method.
julia
sub_eltype(X)

Return the element type of of the first element of a collection.

source


# ConstraintLearning.train! — Method.
julia
train!(Q, X, penalty, η, precision, X_test, oversampling, binarization, domains)

Training inner method.

source


# ConstraintLearning.train — Method.
julia
train(X, penalty[, d]; optimizer = QUBOGradientOptimizer(), X_test = X)

Learn a QUBO matrix on training set X for a constraint defined by penalty with optional domain information d. By default, it uses a QUBOGradientOptimizer and X as a testing set.

source


# ConstraintLearning.δ — Method.
julia
δ(X[, Y]; discrete = true)

Compute the extrema over a collection X``or a pair of collection(X, Y)`.

source


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Julia Constraints

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Learning about Constraints ​

About learning constraints related matters.

- + \ No newline at end of file diff --git a/dev/learning/layers.html b/dev/learning/layers.html index 20dc950..18a75af 100644 --- a/dev/learning/layers.html +++ b/dev/learning/layers.html @@ -8,10 +8,10 @@ - + - + @@ -21,7 +21,7 @@ is_viable(icn) is_viable(icn, w)

Assert if a pair of layer/icn and weights compose a viable pattern. If no weights are given with an icn, it will check the current internal value.

source


# CompositionalNetworks.generate_inclusive_operations — Function.
julia
generate_inclusive_operations(predicate, bits)
 generate_exclusive_operation(max_op_number)

Generates the operations (weights) of a layer with inclusive/exclusive operations.

source


# CompositionalNetworks.generate_exclusive_operation — Function.
julia
generate_exclusive_operation(max_op_number)

Generates the operations (weigths) of a layer with exclusive operations.

source


Missing docstring.

Missing docstring for generate_weigths. Check Documenter's build log for details.

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Julia Constraints

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Introduction to QUBOConstraints.jl ​

Introduction to QUBOConstraints.jl.

Basic features ​

# QUBOConstraints.QUBO_base — Function.
julia
QUBO_base(n, weight = 1)

A basic QUBO matrix to ensure that binarized variables keep a valid encoding.

source


# QUBOConstraints.QUBO_linear_sum — Function.
julia
QUBO_linear_sum(n, σ)

One valid QUBO matrix given n variables and parameter σ for the linear sum constraint.

source


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Julia Constraints

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Encoding for QUBO programs ​

# QUBOConstraints.is_valid — Function.
julia
is_valid(x, encoding::Symbol = :none)

Check if x has a valid format for encoding.

For instance, if encoding == :one_hot, at most one bit of x can be set to 1.

source


# QUBOConstraints.binarize — Function.
julia
binarize(x[, domain]; binarization = :one_hot)

Binarize x following the binarization encoding. If x is a vector (instead of a number per say), domain is optional.

source


# QUBOConstraints.debinarize — Function.
julia
debinarize(x[, domain]; binarization = :one_hot)

Transform a binary vector into a number or a set of number. If domain is not given, it will compute a default value based on binarization and x.

source


- + \ No newline at end of file diff --git a/dev/learning/qubo_learning.html b/dev/learning/qubo_learning.html index a530647..d7b9ab6 100644 --- a/dev/learning/qubo_learning.html +++ b/dev/learning/qubo_learning.html @@ -8,10 +8,10 @@ - + - + @@ -136,7 +136,7 @@ ) return train(X, penalty, to_domains(X, dom_stuff); optimizer, X_test) end - + \ No newline at end of file diff --git a/dev/learning/transformation.html b/dev/learning/transformation.html index 9bb7c1a..b091144 100644 --- a/dev/learning/transformation.html +++ b/dev/learning/transformation.html @@ -8,10 +8,10 @@ - + - + @@ -62,7 +62,7 @@ # Apply a count equal to parameter transformation count_eq_param_result = val_transforms[:count_eq_param](data, param)

source


# CompositionalNetworks.transformation_layer — Function.
julia
transformation_layer(param = false)

Generate the layer of transformations functions of the ICN. Iff param value is true, also includes all the parametric transformations.

source


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Julia Constraints

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MetaStrategist.jl ​

Documentation for MetaStrategist.jl.

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Julia Constraints

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BenchmarkTools Extension ​

A benchmarking extension, based on BenchmarkTools.jl, has been interfaced with PerfChecker.jl. This section (will) provides some usage examples, documentation, and links to related notebooks.

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Julia Constraints

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PerfChecker.jl ​

Documentation for PerfChecker.jl.

# PerfChecker.arrange_breaking — Method.

Outputs the last breaking or next breaking version.

source


# PerfChecker.arrange_major — Method.

Outputs the earlier or next major version.

source


# PerfChecker.arrange_patches — Method.

Outputs the last patch or first patch of a version.

source


# PerfChecker.get_pkg_versions — Function.

Finds all versions of a package in all the installed registries and returns it as a vector.

Example:

julia
julia> get_pkg_versions("ConstraintLearning")
+    
Julia Constraints
Appearance
PerfChecker.jl ​
Documentation for PerfChecker.jl.
# PerfChecker.arrange_breaking — Method. 
Outputs the last breaking or next breaking version.
source

# PerfChecker.arrange_major — Method. 
Outputs the earlier or next major version.
source

# PerfChecker.arrange_patches — Method. 
Outputs the last patch or first patch of a version.
source

# PerfChecker.get_pkg_versions — Function. 
Finds all versions of a package in all the installed registries and returns it as a vector.
Example:
julia
julia> get_pkg_versions("ConstraintLearning")
 7-element Vector{VersionNumber}:
  v"0.1.4"
  v"0.1.5"
@@ -25,8 +25,8 @@
  v"0.1.6"
  v"0.1.1"
  v"0.1.3"
- v"0.1.2"
source


-    
+ v"0.1.2"

source


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Julia Constraints

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Interfacing PerfChecker ​

PerfChecker was build as an easy to extend interface. This section will cover the few method required.

- + \ No newline at end of file diff --git a/dev/public_api.html b/dev/public_api.html index a646d4e..d4f6a22 100644 --- a/dev/public_api.html +++ b/dev/public_api.html @@ -8,10 +8,10 @@ - + - + @@ -33,7 +33,7 @@ nvars, dom_size, param=nothing, icn=ICN(nvars, dom_size, param), X, X_sols, global_iter=100, local_iter=100, metric=hamming, popSize=200 )

Create an ICN, optimize it, and return its composition.

source


# CompositionalNetworks.manhattan — Method.
julia
manhattan(x, X)

source


# CompositionalNetworks.minkowski — Method.
julia
minkowski(x, X, p)

source


# CompositionalNetworks.nbits — Method.
julia
nbits(icn)

Return the expected number of bits of a viable weight of an ICN.

source


# CompositionalNetworks.regularization — Method.
julia
regularization(icn)

Return the regularization value of an ICN weights, which is proportional to the normalized number of operations selected in the icn layers.

source


# CompositionalNetworks.show_layers — Method.
julia
show_layers(icn)

Return a formatted string with each layers in the icn.

source


# CompositionalNetworks.symbols — Method.
julia
symbols(c::Composition)

Output the composition as a layered collection of Symbols.

source


# CompositionalNetworks.transformation_layer — Function.
julia
transformation_layer(param = false)

Generate the layer of transformations functions of the ICN. Iff param value is true, also includes all the parametric transformations.

source


# CompositionalNetworks.weights! — Method.
julia
weights!(icn, weights)

Set the weights of an ICN with a BitVector.

source


# CompositionalNetworks.weights — Method.
julia
weights(icn)

Access the current set of weights of an ICN.

source


# CompositionalNetworks.weights_bias — Method.
julia
weights_bias(x)

A metric that bias x towards operations with a lower bit. Do not affect the main metric.

source


# QUBOConstraints.QUBO_linear_sum — Method.
julia
QUBO_linear_sum(n, σ)

One valid QUBO matrix given n variables and parameter σ for the linear sum constraint.

source


# QUBOConstraints.binarize — Method.
julia
binarize(x[, domain]; binarization = :one_hot)

Binarize x following the binarization encoding. If x is a vector (instead of a number per say), domain is optional.

source


# QUBOConstraints.debinarize — Method.
julia
debinarize(x[, domain]; binarization = :one_hot)

Transform a binary vector into a number or a set of number. If domain is not given, it will compute a default value based on binarization and x.

source


# QUBOConstraints.is_valid — Function.
julia
is_valid(x, encoding::Symbol = :none)

Check if x has a valid format for encoding.

For instance, if encoding == :one_hot, at most one bit of x can be set to 1.

source


# QUBOConstraints.train — Method.
julia
train(args...)

Default train method for any AbstractOptimizer.

source


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source


# CBLS.SequentialTasks — Type.

Local constraint ensuring that, given a vector X of size 4, |X[1] - X[2]| ≠ |X[3] - X[4]|).

julia
@constraint(model, X in SequentialTasks())

source


# CBLS.SumEqualParam — Type.

Global constraint ensuring that the sum of the values of X is equal to a given parameter param.

julia
@constraint(model, X in SumEqualParam(param))

source


# Base.copy — Method.
julia
Base.copy(set::MOIError) = begin

DOCSTRING

source


# Base.copy — Method.
julia
Base.copy(set::DiscreteSet) = begin

DOCSTRING

source


# JuMP.build_variable — Method.
julia
JuMP.build_variable(::Function, info::JuMP.VariableInfo, set::T) where T <: MOI.AbstractScalarSet

DOCSTRING

Arguments:

  • ``: DESCRIPTION

  • info: DESCRIPTION

  • set: DESCRIPTION

source


# MathOptInterface.add_constraint — Method.
julia
MOI.add_constraint(optimizer::Optimizer, vars::MOI.VectorOfVariables, set::MOIError)

DOCSTRING

Arguments:

  • optimizer: DESCRIPTION

  • vars: DESCRIPTION

  • set: DESCRIPTION

source


# MathOptInterface.add_constraint — Method.
julia
MOI.add_constraint(optimizer::Optimizer, v::VI, set::DiscreteSet{T}) where T <: Number

DOCSTRING

Arguments:

  • optimizer: DESCRIPTION

  • v: DESCRIPTION

  • set: DESCRIPTION

source


# MathOptInterface.add_variable — Method.
julia
MOI.add_variable(model::Optimizer) = begin

DOCSTRING

source


# MathOptInterface.empty! — Method.
julia
MOI.empty!(opt) = begin

DOCSTRING

source


# MathOptInterface.get — Method.
julia
MOI.get(::Optimizer, ::MOI.SolverName) = begin

DOCSTRING

source


# MathOptInterface.is_empty — Method.
julia
MOI.is_empty(model::Optimizer) = begin

DOCSTRING

source


# MathOptInterface.optimize! — Method.
julia
MOI.optimize!(model::Optimizer)

source


# MathOptInterface.set — Function.
julia
MOI.set(::Optimizer, ::MOI.Silent, bool = true) = begin

DOCSTRING

Arguments:

  • ``: DESCRIPTION

  • ``: DESCRIPTION

  • bool: DESCRIPTION

source


# MathOptInterface.set — Method.
julia
MOI.set(model::Optimizer, p::MOI.RawOptimizerAttribute, value)

Set a RawOptimizerAttribute to value

source


# MathOptInterface.set — Method.
julia
MOI.set(model::Optimizer, ::MOI.TimeLimitSec, value::Union{Nothing,Float64})

Set the time limit

source


# MathOptInterface.supports_constraint — Method.
julia
MOI.supports_constraint(::Optimizer, ::Type{VOV}, ::Type{MOIError}) = begin

DOCSTRING

Arguments:

  • ``: DESCRIPTION

  • ``: DESCRIPTION

  • ``: DESCRIPTION

source


# MathOptInterface.supports_incremental_interface — Method.

Copy constructor for the optimizer

source


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Julia Constraints

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Solvers ​

About solvers.

- + \ No newline at end of file diff --git a/dev/solvers/local_search_solvers.html b/dev/solvers/local_search_solvers.html index a0b46b0..d859bce 100644 --- a/dev/solvers/local_search_solvers.html +++ b/dev/solvers/local_search_solvers.html @@ -8,10 +8,10 @@ - + - + @@ -53,7 +53,7 @@ variable(domain::AbstractDomain, name::AbstractString) where D <: AbstractDomain

Construct a variable with discrete domain. See the domain method for other options.

julia
d = domain([1,2,3,4], types = :indices)
 x1 = variable(d, "x1")
 x2 = variable([-89,56,28], "x2", domain = :indices)

source


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