Merge branch 'feature/disjunctive-constraint' of github.com:ctdunc/pyscipopt into feature/disjunctive-constraint

Author: ctdunc

.github/workflows/coverage.yml

@@ -43,6 +43,7 @@ jobs:
 
       - name: Run pyscipopt tests
         run: |
+          sudo apt-get install tzdata locales -y && sudo locale-gen pt_PT && sudo update-locale # add pt_PT locale that is used in tests
           coverage run -m pytest
           coverage report -m
           coverage xml

CHANGELOG.md

@@ -2,8 +2,15 @@
 
 ## Unreleased
 ### Added
+- Added recipe for nonlinear objective functions
+- Added method for adding piecewise linear constraints
 - Add SCIP function SCIPgetTreesizeEstimation and wrapper getTreesizeEstimation
+- New test for model setLogFile
 ### Fixed
+- Fixed model.setLogFile(None) error
+- Add recipes sub-package
+- Fixed "weakly-referenced object no longer exists" when calling dropEvent in test_customizedbenders
+- Fixed incorrect writing/printing when user had a non-default locale
 ### Changed
 ### Removed
 

README.md

@@ -55,7 +55,7 @@ Building and solving a model
 
 There are several [examples](https://github.com/scipopt/PySCIPOpt/blob/master/examples/finished) and
 [tutorials](https://github.com/scipopt/PySCIPOpt/blob/master/examples/tutorial). These display some functionality of the
-interface and can serve as an entry point for writing more complex code.
+interface and can serve as an entry point for writing more complex code. Some of the common usecases are also available in the [recipes](https://github.com/scipopt/PySCIPOpt/blob/master/src/pyscipopt/recipes) sub-package.
 You might also want to have a look at this article about PySCIPOpt:
 <https://opus4.kobv.de/opus4-zib/frontdoor/index/index/docId/6045>. The
 following steps are always required when using the interface:

setup.py

@@ -1,4 +1,4 @@
-from setuptools import setup, Extension
+from setuptools import find_packages, setup, Extension
 import os, platform, sys, re
 
 # look for environment variable that specifies path to SCIP
@@ -124,7 +124,7 @@
         "Topic :: Scientific/Engineering :: Mathematics",
     ],
     ext_modules=extensions,
-    packages=["pyscipopt"],
+    packages=find_packages(where="src"),
     package_dir={"pyscipopt": packagedir},
     package_data={"pyscipopt": ["scip.pyx", "scip.pxd", "*.pxi", "scip/lib/*"]},
     include_package_data=True,

src/pyscipopt/__init__.py

@@ -9,6 +9,8 @@
 # export user-relevant objects:
 from pyscipopt.Multidict import multidict
 from pyscipopt.scip      import Model
+from pyscipopt.scip      import Variable
+from pyscipopt.scip      import Constraint
 from pyscipopt.scip      import Benders
 from pyscipopt.scip      import Benderscut
 from pyscipopt.scip      import Branchrule

src/pyscipopt/recipes/README.md

@@ -0,0 +1,3 @@
+# Recipes sub-package
+
+This sub-package provides a set of functions for common usecases for pyscipopt. This sub-package is for all functions that don't necessarily reflect the core functionality of SCIP, but are useful for working with the solver. The functions implemented in this sub-package might not be the most efficient way to solve/formulate a problem but would provide a good starting point.

src/pyscipopt/recipes/__init__.py

@@ -0,0 +1,18 @@
+from pyscipopt import Model
+
+def set_nonlinear_objective(model: Model, expr, sense="minimize"):
+        """
+        Takes a nonlinear expression and performs an epigraph reformulation.
+        """
+        
+        assert expr.degree() > 1, "For linear objectives, please use the setObjective method."
+        new_obj = model.addVar(lb=-float("inf"),obj=1)
+        if sense == "minimize":
+            model.addCons(expr <= new_obj)
+            model.setMinimize()
+        elif sense == "maximize":
+            model.addCons(expr >= new_obj)
+            model.setMaximize()
+        else:
+            raise Warning("unrecognized optimization sense: %s" % sense)
+    

src/pyscipopt/recipes/nonlinear.py

@@ -0,0 +1,36 @@
+
+from pyscipopt import Model, quicksum, Variable, Constraint
+
+def add_piecewise_linear_cons(model: Model, X: Variable, Y: Variable, a: list[float], b: list[float]) -> Constraint:
+        """add constraint of the form y = f(x), where f is a piecewise linear function
+
+            :param X: x variable
+            :param Y: y variable
+            :param a: array with x-coordinates of the points in the piecewise linear relation
+            :param b: array with y-coordinate of the points in the piecewise linear relation
+
+            Disclaimer: For the moment, can only model 2d piecewise linear functions
+            Adapted from https://github.com/scipopt/PySCIPOpt/blob/master/examples/finished/piecewise.py
+        """
+        assert len(a) == len(b), "Must have the same number of x and y-coordinates"
+
+        K = len(a)-1
+        w,z = {},{}
+        for k in range(K):
+            w[k] = model.addVar(lb=-model.infinity()) 
+            z[k] = model.addVar(vtype="B")
+
+        for k in range(K):
+            model.addCons(w[k] >= a[k]*z[k])
+            model.addCons(w[k] <= a[k+1]*z[k])
+
+        model.addCons(quicksum(z[k] for k in range(K)) == 1)
+
+        model.addCons(X == quicksum(w[k] for k in range(K)))
+
+        c = [float(b[k+1]-b[k]) / (a[k+1]-a[k]) for k in range(K)]
+        d = [b[k] - c[k]*a[k] for k in range(K)]
+        
+        new_cons = model.addCons(Y == quicksum(d[k]*z[k] + c[k]*w[k] for k in range(K)))
+        
+        return new_cons