Python examples

These examples in Python use the Callable Library.

The following examples are delivered with CPLEX in IBM ILOG CPLEX Optimization Studio.

admipex9.py uses a generic callback involving a heuristic to optimize a MIP.

admipex8.py demonstrates how to add lazy constraints and user cuts with the generic callback.

admipex6.py an advanced application, it solves a mixed integer programming (MIP) problem starting from an LP solution.

admipex5.py an advanced application, shows how to add cuts for a MIP by means of a cut callback.

admipex4.py demonstrates how to add lazy constraints and user cuts.

admipex3.py an advanced application, uses a branch callback on a MIP with special ordered sets (SOS).

admipex2.py an advanced application, uses a heuristic callback to optimize a MIP.

admipex1.py an advanced application, uses node and branch callbacks to optimize a MIP.

benders.py demonstrates a Benders decomposition.

bendersatsp.py demonstrates legacy lazy constraint callbacks to separate integer feasible LP solutions and legacy user cut callbacks to separate fractional infeasible LP solutions in a do-it-yourself Benders decomposition of an asymmetric traveling salesperson problem.

bendersatsp2.py demonstrates generic callbacks to separate integer feasible LP solutions in a context of lazy constraints and generic callbacks to separate fractional infeasible LP solutions in a context of user cuts, all in a do-it-yourself Benders decomposition of an asymmetric traveling salesperson problem.

blend.py solves a blending problem.

conflictex1.py demonstrates how to use the conflict refiner.

cutstock.py solves a cutting stock problem, where a maximum number of patterns should be cut from stock with minimum waste. The model illustrates column-generation.

diet.py is an implementation in Python of the classic diet problem; a linear program that can be generated by columns (add foods to the diet) or by rows (add requirements to the diet); optionally, demonstrates how to model a multiobjective problem by creating a second objective.

multiobjex1.py demonstrates how to read in and optimize a multiobjective problem.

etsp.py is an implementation in Python of an early-tardy scheduling problem that uses indicator constraints.

facility.py solves a facility-location problem where warehouses have limited storage and must meet distribution needs. This example also offers an option to use Benders decomposition and algorithm.

fixcost1.py solves a production planning problem with fixed costs.

fixnet.py uses indicator constraints to avoid numeric difficulty in a fixed-charge network flow problem.

foodmanu.py implements a solution to the food production planning problem, well known from the modeling textbook of H. P. Williams; this implementation uses indicator constraints and semi-continuous variables.

genericbranch.py demonstrates how to perform customized branching using the generic callback.

globalqpex1.py shows how to read a convex or nonconvex quadratic programming model (QP) and solve it for a first order or global optimum.

indefqpex1.py shows how to solve an indefinite quadratic programming example to a local optimum.

inout1.py a production planning problem aiming to minimize cost, where goods may be manufactured internally or purchased from outside sources.

inout3.py a production planning problem with additional constraints on costs.

lpex1.py is a basic linear programming example, illustrating data population techniques by rows, by columns, by coefficients.

lpex2.py is a basic linear programming example with data coming from files.

lpex3.py demonstrates how to add rows to a problem and re-optimize; the examples begins with a network flow model, uses the network optimizer to solve it, adds additional constraints, and invokes the dual simplex optimizer to resolve the new model.

lpex4.py illustrates CPLEX callback functions; it is derived from lpex1.py.

lpex5.py illustrates message handling.

lpex6.py shows how to load a basis.

lpex7.py shows how to access names of columns.

mipex1.py solves a basic mixed integer programming (MIP) problem.

mipex2.py solves a basic MIP with data from files.

mipex3.py illustrates SOS sets and priority orders.

mipex4.py demonstrates how to use the Aborter class to interrupt optimization and how to use informational callbacks for logging, timing, or setting a node limit.

miqpex1.py solves a mixed integer programming problem (MIP) with a quadratic term in the objective function.

populate.py shows how to generate and store multiple solutions in the solution pool.

qcpex1.py optimizes a quadratically constrained model.

qpex1.py solves a model with a quadratic term in the objective function.

qpex2.py solves a model with a quadratic term in the objective function and reads data from a file.

rates.py is a planning problem using semi-continuous variables.

socpex1.py shows how to access dual values and reduced costs in SOCP models.

qcpdual.py shows how to access dual values and reduced costs in quadratically constrained programming models (QCP).

steel.py solves the model known as steelT.mod in the AMPL book by Fourer, Gay, and Kernighan.

transport.py uses a piecewise linear cost function.

warehouse.py is a warehouse-location problem, using semi-continuous integer variables.