Package: optlang
Sympy based mathematical programming languageOptlang is a Python package for solving mathematical optimization
problems, i.e. maximizing or minimizing an objective function over a set
of variables subject to a number of constraints. Optlang provides a
common interface to a series of optimization tools, so different solver
backends can be changed in a transparent way.
Optlang’s object-oriented API takes advantage of the symbolic math librarysympyto allow objective functions
and constraints to be easily formulated from symbolic expressions of
variables (see examples).Show us some love by staring this repo if you find optlang useful!Also, please use the GitHubissue trackerto let us know about bugs or feature requests, or ourgitter channelif you have problems or questions regarding optlang.InstallationInstall using pippip install optlangThis will also installswiglpk, an interface to the open source (mixed integer) LP solverGLPK.
Quadratic programming (and MIQP) is supported through additional optional solvers (see below).DependenciesThe following dependencies are needed.sympy >= 1.0.0six >= 1.9.0swiglpk >= 1.4.3The following are optional dependencies that allow other solvers to be used.cplex(LP, MILP, QP, MIQP)gurobipy(LP, MILP, QP, MIQP)scipy(LP)osqp(LP, QP)ExampleFormulating and solving the problem is straightforward (example taken
fromGLPK documentation):from__future__importprint_functionfromoptlangimportModel,Variable,Constraint,Objective# All the (symbolic) variables are declared, with a name and optionally a lower and/or upper bound.x1=Variable('x1',lb=0)x2=Variable('x2',lb=0)x3=Variable('x3',lb=0)# A constraint is constructed from an expression of variables and a lower and/or upper bound (lb and ub).c1=Constraint(x1+x2+x3,ub=100)c2=Constraint(10*x1+4*x2+5*x3,ub=600)c3=Constraint(2*x1+2*x2+6*x3,ub=300)# An objective can be formulatedobj=Objective(10*x1+6*x2+4*x3,direction='max')# Variables, constraints and objective are combined in a Model object, which can subsequently be optimized.model=Model(name='Simple model')model.objective=objmodel.add([c1,c2,c3])status=model.optimize()print("status:",model.status)print("objective value:",model.objective.value)print("----------")forvar_name,varinmodel.variables.iteritems():print(var_name,"=",var.primal)The example will produce the following output:status: optimal
objective value: 733.333333333
----------
x2 = 66.6666666667
x3 = 0.0
x1 = 33.3333333333Using a particular solverIf you have more than one solver installed, it’s also possible to specify which one to use, by importing directly from the
respective solver interface, e.g.from optlang.glpk_interface import Model, Variable, Constraint, ObjectiveDocumentationDocumentation for optlang is provided atreadthedocs.org.CitationPlease citeif you use optlang in a scientific publication. In case you would like to reference a specific version of of optlang you can also include the respective Zenodo DOI (points to the latest version).ContributingPlease readCONTRIBUTING.md.FundingThe development of optlang was partly support by the Novo Nordisk Foundation.Future outlookMosekinterface (provides academic
licenses)GAMSoutput (support non-linear problem
formulation)DEAP(support for heuristic
optimization)Interface toNEOSoptimization
server (for testing purposes and solver evaluation)Automatically handle fractional and absolute value problems when
dealing with LP/MILP/QP solvers (like GLPK,CPLEXetc.)