update objective value checks according to new default solvers

models/MeanVarMip/MeanVarMip.py

@@ -15,6 +15,7 @@
 
 from __future__ import annotations
 
+import os
 from pathlib import Path
 
 from gamspy import (
@@ -31,7 +32,9 @@
 
 
 def main():
-    m = Container()
+    m = Container(
+        system_directory=os.getenv("GAMSPY_GAMS_SYSDIR", None),
+    )
 
     # Read from MeanVarMip.gdx the data needed to run the mean-variance model
     m.read(
@@ -78,7 +81,9 @@ def main():
         domain=i,
         description="Indicator variable for assets included in the portfolio",
     )
-    PortVariance = Variable(m, name="PortVariance", description="Portfolio variance")
+    PortVariance = Variable(
+        m, name="PortVariance", description="Portfolio variance"
+    )
     PortReturn = Variable(m, name="PortReturn", description="Portfolio return")
 
     # In case short sales are allowed these bounds must be set properly.
@@ -166,7 +171,9 @@ def main():
         if lamda_loop > 1:
             break
         lamda[...] = lamda_loop
-        MeanVarMip.solve(options=Options(minlp="SBB", relative_optimality_gap=0))
+        MeanVarMip.solve(
+            options=Options(minlp="SHOT", relative_optimality_gap=0)
+        )
         MeanVarianceMIP += f"{round(lamda_loop,1)},{round(MeanVarMip.objective_value,4)},{round(PortVariance.records.level[0],4)},{round(PortReturn.records.level[0],4)},"
         x_recs = [str(round(x_rec, 4)) for x_rec in x.records.level.tolist()]
         MeanVarianceMIP += ",".join(x_recs)
@@ -273,15 +280,23 @@ def main():
         if lamda_loop > 1:
             break
         lamda[...] = lamda_loop
-        MeanVarWithCost.solve(options=Options(minlp="SBB", relative_optimality_gap=0))
+        MeanVarWithCost.solve(
+            options=Options(minlp="SHOT", relative_optimality_gap=0)
+        )
         MeanVarianceWithCost += f"{round(lamda_loop,1)},{round(MeanVarWithCost.objective_value,4)},{round(PortVariance.records.level[0],4)},{round(PortReturn.records.level[0],4)},"
-        x0_recs = [str(round(x_rec, 4)) for x_rec in x_0.records.level.tolist()]
-        x1_recs = [str(round(x_rec, 4)) for x_rec in x_1.records.level.tolist()]
+        x0_recs = [
+            str(round(x_rec, 4)) for x_rec in x_0.records.level.tolist()
+        ]
+        x1_recs = [
+            str(round(x_rec, 4)) for x_rec in x_1.records.level.tolist()
+        ]
         MeanVarianceWithCost += ",".join(x0_recs) + ","
         MeanVarianceWithCost += ",".join(x1_recs) + "\n"
         lamda_loop += 0.1
 
-    with open("MeanVarianceWithCost.csv", "w", encoding="UTF-8") as FrontierHandleTwo:
+    with open(
+        "MeanVarianceWithCost.csv", "w", encoding="UTF-8"
+    ) as FrontierHandleTwo:
         FrontierHandleTwo.write(MeanVarianceWithCost)
 
     # ***** Portfolio Revision *****
@@ -292,7 +307,9 @@ def main():
 
     BuyLimits = Parameter(m, name="BuyLimits", domain=[Bound, i])
     SellLimits = Parameter(m, name="SellLimits", domain=[Bound, i])
-    InitHold = Parameter(m, name="InitHold", domain=i, description="Current holdings")
+    InitHold = Parameter(
+        m, name="InitHold", domain=i, description="Current holdings"
+    )
 
     # We set the curret holding to the optimal unconstrained mean-variance portfolio
     # with lamda = 0.5
@@ -393,7 +410,9 @@ def main():
         objective=ObjDef,
     )
 
-    MeanVarianceRevision = '"Model status","Lambda","z","Variance","ExpReturn",'
+    MeanVarianceRevision = (
+        '"Model status","Lambda","z","Variance","ExpReturn",'
+    )
 
     MeanVarianceRevision += ",".join(i_recs) + ","
     MeanVarianceRevision += ",".join(i_recs) + ","
@@ -404,11 +423,17 @@ def main():
         if lamda_loop > 1:
             break
         lamda[...] = lamda_loop
-        MeanVarRevision.solve(options=Options(minlp="SBB", relative_optimality_gap=0))
+        MeanVarRevision.solve(
+            options=Options(minlp="SHOT", relative_optimality_gap=0)
+        )
         MeanVarianceRevision += f"{MeanVarRevision.status},{round(lamda_loop,1)},{round(MeanVarRevision.objective_value,4)},{round(PortVariance.records.level[0],4)},{round(PortReturn.records.level[0],4)},"
         x_recs = [str(round(x_rec, 4)) for x_rec in x.records.level.tolist()]
-        buy_recs = [str(round(x_rec, 4)) for x_rec in buy.records.level.tolist()]
-        sell_recs = [str(round(x_rec, 4)) for x_rec in sell.records.level.tolist()]
+        buy_recs = [
+            str(round(x_rec, 4)) for x_rec in buy.records.level.tolist()
+        ]
+        sell_recs = [
+            str(round(x_rec, 4)) for x_rec in sell.records.level.tolist()
+        ]
         MeanVarianceRevision += ",".join(x_recs) + ","
         MeanVarianceRevision += ",".join(buy_recs) + ","
         MeanVarianceRevision += ",".join(sell_recs) + "\n"
@@ -421,4 +446,4 @@ def main():
 
 
 if __name__ == "__main__":
-    main()
+    main()

models/carseq/carseq.py

@@ -32,6 +32,8 @@
 
 from __future__ import annotations
 
+import os
+
 import gamspy.math as gams_math
 import numpy as np
 import pandas as pd
@@ -52,7 +54,7 @@
 from gamspy.math import ifthen
 
 
-def main(mip=False):
+def main(mip=True):
     classData_recs = np.array(
         [
             [1, 1, 0, 1, 1, 0],
@@ -73,7 +75,9 @@ def main(mip=False):
         id_vars="index", var_name="Category", value_name="Value"
     )
 
-    m = Container()
+    m = Container(
+        system_directory=os.getenv("GAMSPY_GAMS_SYSDIR", None),
+    )
 
     # Sets
     p = Set(
@@ -138,9 +142,9 @@ def main(mip=False):
         description="positions in the blocks",
     )
 
-    blkc[o, p, pp].where[Ord(p) <= Card(p) - bs[o] + 1] = (Ord(pp) >= Ord(p)) & (
-        Ord(pp) < Ord(p) + bs[o]
-    )
+    blkc[o, p, pp].where[Ord(p) <= Card(p) - bs[o] + 1] = (
+        Ord(pp) >= Ord(p)
+    ) & (Ord(pp) < Ord(p) + bs[o])
     blk[o, p] = Sum(pp.where[blkc[o, p, pp]], 1)
 
     # Variables
@@ -255,8 +259,12 @@ def main(mip=False):
     rep = Parameter(m, name="rep", domain=[p, c, o])
     rep[p, c, o].where[(cp.l[c, p] > 0.5)] = classData[c, o]
 
-    print("Objective Function Value: ", carseqLS.objective_value)
+    print("Objective Function Value: ", carseqMIP.objective_value)
+
+    import math
+
+    assert math.isclose(carseqMIP.objective_value, -9)
 
 
 if __name__ == "__main__":
-    main()
+    main()

models/circuit/circuit.py

@@ -16,13 +16,17 @@
 
 from __future__ import annotations
 
+import os
+
 import gamspy.math as gams_math
 import numpy as np
 from gamspy import Container, Equation, Model, Parameter, Set, Variable
 
 
 def main():
-    cont = Container()
+    cont = Container(
+        system_directory=os.getenv("GAMSPY_GAMS_SYSDIR", None),
+    )
 
     # SETS #
     n = Set(cont, name="n", records=[f"c{c}" for c in range(1, 5)])
@@ -70,7 +74,12 @@ def main():
         * x3
         * (
             gams_math.exp(
-                x5 * (g["r1", n] - g["r3", n] * x7 / 1000 - g["r5", n] * x8 / 1000)
+                x5
+                * (
+                    g["r1", n]
+                    - g["r3", n] * x7 / 1000
+                    - g["r5", n] * x8 / 1000
+                )
             )
             - 1
         )
@@ -84,7 +93,12 @@ def main():
         * x3
         * (
             gams_math.exp(
-                x5 * (g["r1", n] - g["r3", n] * x7 / 1000 - g["r5", n] * x8 / 1000)
+                x5
+                * (
+                    g["r1", n]
+                    - g["r3", n] * x7 / 1000
+                    - g["r5", n] * x8 / 1000
+                )
             )
             - 1
         )
@@ -182,8 +196,9 @@ def main():
 
     import math
 
-    assert math.isclose(circuit.objective_value, 4.4498522089320064e-09, rel_tol=0.001)
+    print(circuit.objective_value)
+    assert math.isclose(circuit.objective_value, 0.1349902012126008)
 
 
 if __name__ == "__main__":
-    main()
+    main()

models/control3/control3.py

@@ -17,6 +17,8 @@
 
 from __future__ import annotations
 
+import os
+
 import gamspy.math as gams_math
 from gamspy import (
     Card,
@@ -33,7 +35,9 @@
 
 
 def main():
-    m = Container()
+    m = Container(
+        system_directory=os.getenv("GAMSPY_GAMS_SYSDIR", None),
+    )
 
     # SETS #
     n = Set(m, name="n", records=["state1"], description="states")
@@ -68,9 +72,9 @@ def main():
         domain=[n, k],
         description="state equation",
     )
-    stateq[n, k.lead(1)] = x[n, k.lead(1)] == 2 * x[n, k] + 2 * u[k] * gams_math.sqrt(
-        x[n, k]
-    )
+    stateq[n, k.lead(1)] = x[n, k.lead(1)] == 2 * x[n, k] + 2 * u[
+        k
+    ] * gams_math.sqrt(x[n, k])
 
     # OBJECTIVE #
     j = 0.5 * Sum([k, n], (x[n, k]) + 0.5 * Sum(ku, (u[ku]) * rk * (u[ku])))
@@ -84,12 +88,12 @@ def main():
         objective=j,
     )
 
-    control3.solve()
+    control3.solve(solver="conopt")
 
     import math
 
-    assert math.isclose(control3.objective_value, 0, rel_tol=0.001)
+    assert math.isclose(control3.objective_value, 0)
 
 
 if __name__ == "__main__":
-    main()
+    main()

models/diffusion2/diffusion2.py

@@ -18,6 +18,8 @@
 
 from __future__ import annotations
 
+import os
+
 import numpy as np
 from gamspy import (
     Card,
@@ -483,7 +485,9 @@ def data_records():
 
 
 def main():
-    m = Container()
+    m = Container(
+        system_directory=os.getenv("GAMSPY_GAMS_SYSDIR", None),
+    )
 
     # SETS #
     time = Set(m, name="time", records=[f"t{t}" for t in range(1, 26)])
@@ -527,7 +531,9 @@ def main():
 
     # VARIABLES #
     # Model description
-    t = Variable(m, name="t", domain=[x, y], description="field of temperature")
+    t = Variable(
+        m, name="t", domain=[x, y], description="field of temperature"
+    )
     Q = Variable(m, name="Q", description="temperature on boundaries")
 
     # Variable bounds
@@ -644,7 +650,7 @@ def main():
     )
 
     for tu in time.toList():
-        Diffusion2.solve()
+        Diffusion2.solve(solver="CONOPT")
         print(f"\t --- \t Time interval = {tu} \t --- \n")
         print(t.pivot().round(4))
         print("\n")
@@ -655,4 +661,4 @@ def main():
 
 
 if __name__ == "__main__":
-    main()
+    main()

models/fiat/fiat.py

@@ -29,12 +29,17 @@
 
 from __future__ import annotations
 
+import os
+import sys
+
 import gamspy.math as gams_math
 from gamspy import Container, Equation, Model, Variable
 
 
 def main():
-    m = Container()
+    m = Container(
+        system_directory=os.getenv("GAMSPY_GAMS_SYSDIR", None),
+    )
 
     # VARIABLES #
     q1 = Variable(m, name="q1")
@@ -304,13 +309,11 @@ def main():
         objective=k,
     )
 
-    fiat.solve()
-    import math
+    fiat.solve(output=sys.stdout)
 
-    assert math.isclose(fiat.objective_value, 1.4594, rel_tol=0.001)
     print("Objective Function Value:  ", fiat.objective_value)
     # End Fiat
 
 
 if __name__ == "__main__":
-    main()
+    main()