C#.NET examples

These examples in C#.NET use Concert Technology.

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

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

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

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

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

AdMIPex5.cs adds cuts through the cut callback of a MIP. This example also demonstrates both lazy constraints and user cuts.

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

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

AdMIPex9.cs uses a heuristic callback based on the knapsack principle.

Benders.cs demonstrates a Benders decomposition.

BendersATSP.cs 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.cs solves a blending problem.

CplexServer.cs shows how to enter a problem with the class CplexModeler and how to serialize the model for solving.

ConflictEx1.cs demonstrates how to use the conflict refiner.

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

Diet.cs is an implementation of the well known diet problem; this linear program 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.cs demonstrates how to read in and optimize a multiobjective problem.

Etsp.cs demonstrates a scheduling problem with costs for earliness and tardiness.

Facility.cs is a warehouse-location problem. Optionally, the example can apply a Benders decomposition.

FixCost1.cs is a production planning problem with fixed costs.

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

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

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

GlobalQPex1.cs 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.cs uses the goal API for branching.

Goalex2.cs uses the goal API to add cuts.

Goalex3.cs uses the goal API to add node evaluators.

IndefQPex1.cs solves an indefinite quadratic program (nonPSD QP) to a local optimum.

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

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

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

LPex4.cs illustrates callbacks.

LPex6.cs shows how to load a basis.

LPex7.cs shows how to access names of columns.

MIPex1.cs is a basic MIP model.

MIPex2.cs is another basic MIP model.

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

MIPex4.cs shows how to use an informational callback for logging, limiting number of nodes, and interrupting optimization; also shows how to terminate optimization; also shows how to use time stamps.

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

MixBlend.cs is a MIP blending model.

QPex1.cs includes a quadratic term in the objective function of an LP.

QPex2.cs is another model with a quadratic objective.

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

QCPex1.cs is a quadratically constrained problem.

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

Rates.cs is a planning problem with semi-continuous variables.

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

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

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

Transport.cs uses a piecewise linear cost function.

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

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