Add attributes

README.md

@@ -57,7 +57,39 @@ List of supported model attributes:
 
  * [`MOI.ObjectiveSense()`](@ref)
 
-## Algorithm
+## Attributes
+
+The algorithm is parametrized by the attributes that can be used both with `JuMP.set_attributes` and `JuMP.get_attributes`
+and have the following types and default values:
+```julia
+rho_f::Cdouble = 1.0e-5
+rho_c::Cdouble = 1.0e-1
+sigmafac::Cdouble = 2.0
+rankreduce::Csize_t = 0
+timelim::Csize_t = 3600
+printlevel::Csize_t = 1
+dthresh_dim::Csize_t = 10
+dthresh_dens::Cdouble = 0.75
+numbfgsvecs::Csize_t = 4
+rankredtol::Cdouble = 2.2204460492503131e-16
+gaptol::Cdouble = 1.0e-3
+checkbd::Cptrdiff_t = -1
+typebd::Cptrdiff_t = 1
+maxrank::Function = default_maxrank
+```
+
+The following attributes can be also be used both with `JuMP.set_attributes` and `JuMP.get_attributes`, but they are also
+modified by `optimize!`:
+* `majiter`
+* `iter`
+* `lambdaupdate`
+* `totaltime`
+* `sigma`
+
+When they are `set`, it provides the initial value of the algorithm.
+With `get`, they provide the value at the end of the algorithm.
+`totaltime` is the total time in second. For the other attributes,
+their meaning is best described by the following pseudo-code.
 
 Given values of `R`, `lambda` and `sigma`, let
 `vio = [dot(A[i], R * R') - b[i]) for i in 1:m]` (`vio[0]` is `dot(C, R * R')` in the C implementation, but we ignore this entry here),
@@ -96,6 +128,9 @@ for majiter in 1:100_000
     if val - 1e10 * abs(origval) > eps()
         return
     end
+    if norm(vio) / (norm(b) + 1) <= rho_f || totaltime >= timelim || iter >= 10_000_000
+        return
+    end
     while norm(G) / (norm(C) + 1) > rho_c / sigma
         sigma *= 2
     end

src/MOI_wrapper.jl

@@ -1,7 +1,16 @@
-# This file modifies code from SDPAFamily.jl (https://github.com/ericphanson/SDPAFamily.jl/), which is available under an MIT license (see LICENSE).
-
 import MathOptInterface as MOI
 
+const PIECES_MAP = Dict{String,Int}(
+    "majiter" => 1,
+    "iter" => 2,
+    "lambdaupdate" => 3,
+    "CG" => 4,
+    "curr_CG" => 5,
+    "totaltime" => 6,
+    "sigma" => 7,
+    "overallsc" => 8,
+)
+
 const SupportedSets =
     Union{MOI.Nonnegatives,MOI.PositiveSemidefiniteConeTriangle}
 
@@ -59,22 +68,29 @@ mutable struct Optimizer <: MOI.AbstractOptimizer
 end
 
 function MOI.supports(::Optimizer, param::MOI.RawOptimizerAttribute)
-    return hasfield(Parameters, Symbol(param.name))
+    return haskey(PIECES_MAP, param.name) || hasfield(Parameters, Symbol(param.name))
 end
 function MOI.set(optimizer::Optimizer, param::MOI.RawOptimizerAttribute, value)
     if !MOI.supports(optimizer, param)
         throw(MOI.UnsupportedAttribute(param))
     end
-    s = Symbol(param.name)
-    setfield!(optimizer.params, s, convert(fieldtype(Parameters, s), value))
+    if haskey(PIECES_MAP, param.name)
+        optimizer.pieces[PIECES_MAP[param.name]] = value
+    else
+        s = Symbol(param.name)
+        setfield!(optimizer.params, s, convert(fieldtype(Parameters, s), value))
+    end
     return
 end
 function MOI.get(optimizer::Optimizer, param::MOI.RawOptimizerAttribute)
     if !MOI.supports(optimizer, param)
         throw(MOI.UnsupportedAttribute(param))
     end
-    getfield!(optimizer.params, Symbol(param.name))
-    return
+    if haskey(PIECES_MAP, param.name)
+        return optimizer.pieces[PIECES_MAP[param.name]]
+    else
+        return getfield!(optimizer.params, Symbol(param.name))
+    end
 end
 
 MOI.supports(::Optimizer, ::MOI.Silent) = true
@@ -309,7 +325,7 @@ function MOI.get(optimizer::Optimizer, ::MOI.RawStatusString)
     return "majiter = $majiter, iter = $iter, λupdate = $λupdate, CG = $CG, curr_CG = $curr_CG, totaltime = $totaltime, σ = $σ, overallsc = $overallsc"
 end
 function MOI.get(optimizer::Optimizer, ::MOI.SolveTimeSec)
-    return optimizer.pieces[6]
+    return MOI.get(optimizer, MOI.RawOptimizerAttribute("totaltime"))
 end
 
 function MOI.is_empty(optimizer::Optimizer)
@@ -393,6 +409,10 @@ end
 function MOI.get(model::Optimizer, ::MOI.TerminationStatus)
     if isnothing(model.pieces)
         return MOI.OPTIMIZE_NOT_CALLED
+    elseif MOI.get(model, MOI.SolveTimeSec()) > MOI.get(model, MOI.RawOptimizerAttribute("timelim"))
+        return MOI.TIME_LIMIT
+    elseif MOI.get(model, MOI.RawOptimizerAttribute("iter")) > 10_000_000
+        return MOI.ITERATION_LIMIT
     else
         return MOI.LOCALLY_SOLVED
     end
@@ -401,8 +421,11 @@ end
 function MOI.get(m::Optimizer, attr::MOI.PrimalStatus)
     if attr.result_index > MOI.get(m, MOI.ResultCount())
         return MOI.NO_SOLUTION
+    elseif MOI.get(m, MOI.TerminationStatus()) != MOI.LOCALLY_SOLVED
+        return UNKNOWN_RESULT_STATUS
+    else
+        return MOI.FEASIBLE_POINT
     end
-    return MOI.FEASIBLE_POINT
 end
 
 function MOI.get(m::Optimizer, attr::MOI.DualStatus)