This Python library provide a simplified way to use the CPLEX Python API. In other words, it provides a funtion that helps you formulate your linear programs.

Solve a linear program with the form $$ { c x : A x \le b } $$

from_matrices(c,A,B,
    relaxation=True,path=None,verbose=False,minimize=True,senses="L")

Parameters: * relaxation=False => Solve the Integer linear programs * path="mylp.lp" => When specified output the OPL formulation * verbose=True => Display CPLEX logs * minimize=True => Set to minimisation problem * sense="L" => Sense of inequation constraint ( "L" less, "G" greater, "E" Equal)

Solve a linear problem by providing a set of constraint and decision variables.

from_template(variables,constraints,
    minimize=True, path=None, verbose=False)

Each set of variables can be describe as following:

Xs = {
    "name" : [X(i) for i in I],
    "coef" : [c[i] for i],
    "type" : ["C" if relaxation else "I" for i in I],
    "ub"   : [1 for i in I],
    "lb"   : [0 for i in I],
}

[...]

variables = [Xs, ...]

Each set of constraint can be defined as following:

c1 = {
    "lin_expr": [[[
    # Decision variables
    ], [
    # Associated Coefficient
    ]] for i in I],
    "senses"  : ["L" for i in I],
    "rhs"     : [ 0  for i in I]
}

constraints = [c1]

You can use 0_template.ipynb as a starting point.

Several example are availiable at Location-routing-problems