Java examples

These examples in Java use Concert Technology.

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

AdMIPex1.java uses node and branch callbacks to optimize a MIP.

AdMIPex2.java uses a heuristic callback to optimize a MIP.

AdMIPex3.java uses a branch callback on a MIP with special ordered sets (SOSs).

AdMIPex4.java demonstrates how to add lazy constraints and user cuts.

AdMIPex5.java adds cuts through the legacy cut callback for a MIP.

AdMIPex6.java starts a MIP optimization from an LP solution.

AdMIPex8.java demonstrates how to add lazy constraints and user cuts with the generic callback.

AdMIPex9.java uses a generic callback invoking a heuristic.

Benders.java demonstrates a Benders decomposition.

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

Blend.java solves a blending problem.

ConflictEx1.java demonstrates how to use the conflict refiner.

CplexServer.java shows you how to write an optimization server that accepts a pure Java model by taking advantage of the class IloCplexModeler in a native J2EE client application.

CutStock.java is a cutting stock model, illustrating column generation.

Diet.java is an implementation in Java 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.java demonstrates how to read in and optimize a multiobjective problem.

Etsp.java is an implementation in Java of a scheduling problem with costs for earliness and tardiness.

Facility.java solves a warehouse-location problem. Optionally, the example can apply a Benders decomposition.

FixCost1.java solves a production planning problem with fixed costs.

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

FoodManufact.java implements a solution to the food production planning problem, well known from the modeling textbook of H. P. Williams.

GenericBranch.java demonstrates how to perform customized branching using the generic callback.

GlobalQPex1.java shows how to read a convex or nonconvex quadratic programming model (QP) or mixed integer quadratic programming model (MIQP) and solve it for a first order or global optimum.

Goalex1.java uses the goal API for branching.

Goalex2.java uses the goal API to add cuts.

Goalex3.java uses the goal API to add node evaluators; it derives from Goalex1.java.

IndefQPex1.java enters and solves an indefinite quadratic program (nonPSD QP) to a local optimum.

InOut1.java is a production model.

InOut3.java is another production model.

LPex1.java is a basic linear programming model illustrating data population techniques.

LPex2.java is a basic linear programming model with data coming from files.

LPex3.java shows how to add rows to a model and re-optimize.

LPex4.java illustrates callbacks.

LPex6.java shows how to load a basis.

LPex7.java shows how to access names of columns.

MIPex1.java is a basic MIP model.

MIPex2.java is another basic MIP model.

MIPex3.java includes special ordered sets (SOSs) in a MIP model.

MIPex4.java shows how to create and use an informational callback to log, to limit number of nodes, and to interrupt optimization; also shows how to terminate optimization; also shows how to use time stamps.

MIQPex1.java is a mixed integer quadratic programming model; it includes a quadratic objective in a MIP.

MixBlend.java is a MIP blending model.

Populate.java shows how to generate and keep multiple solutions in the solution pool.

QPex1.java includes a quadratic objective in an LP.

QPex2.java is another model with a quadratic objective.

QPex3.java uses the Concert Technology interface for quadratic expressions to enter and to modify a model with quadratic expressions in the objective function.

Rates.java solves a planning problem with semi-continuous variables.

SocpEx1.java shows how to access dual values and reduced costs in SOCP models.

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

Steel.java solves a production planning problem illustrating ways to build a model; based on steelT.mod from the AMPL modeling book by Fourer, Gay, and Kernighan.

Transport.java uses a piecewise linear cost function.

TuneSet.java analyzes a set of models and suggests nondefault parameter settings for better performance.

Warehouse.java is a warehouse-location problem; it uses goals.

InputDataReader.java is used by several of the examples to read data.