In This Manual

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• Part I, Languages and APIs
This part collects chapters about each of the application programming interfaces (APIs) available for ILOG CPLEX. It is not necessary to read each of these chapters thoroughly. In fact, most users will concentrate only on the chapter about the API that they plan to use, whether C, C++, Java, .NET, or others.
• Part II, Programming Considerations
This part documents concepts that are valid as you develop an application, regardless of the programming language that you choose. It highlights software engineering concepts implemented in ILOG CPLEX, concepts that will enable you to develop effective applications to exploit it efficiently.
• Part III, Continuous Optimization
This part focuses on algorithmic considerations about the optimizers of ILOG CPLEX that solve problems formulated in terms of continuous variables. While ILOG CPLEX is delivered with default settings that enable you to solve many problems without changing parameters, these chapters also document features that you can customize for your application.
• Part IV, Discrete Optimization
This part focuses on algorithmic considerations about the optimizers of ILOG CPLEX that solve problems formulated in terms of discrete variables, such as integer, Boolean, piecewise-linear, or semi-continuous variables. Again, though default settings of ILOG CPLEX enable you to solve many problems without changing parameters, these chapters also document features that enable you to tune performance.
• Part V, Infeasibility and Unboundedness
This part confronts unsatisfactory results of optimization, such as infeasibility of solutions or unboundedness of decision variables, and suggests ways of formulating or reformulating a model to eliminate or at least to minimize such obstacles.
• Part VI, Advanced Programming Techniques
This part documents advanced programming techniques for users of ILOG CPLEX. It shows you how to apply query routines to gather information while ILOG CPLEX is working. It demonstrates how to redirect the search with goals or callbacks. This part also covers pools of user-defined cuts and pools of lazy constraints. It documents the advanced MIP control interface and the advanced aspects of preprocessing: presolve and aggregation. It also introduces special considerations about parallel programming with ILOG CPLEX. This part of the manual assumes that you are already familiar with earlier parts of the manual.

Part I, Languages and APIs

ILOG Concert Technology for C++ Users introduces Concert Technology. It provides an overview of the design of the library, explains modeling techniques, and offers an example of programming with Concert Technology. It also provides information about controlling parameters.

ILOG Concert Technology for Java Users explores the full range of features that the ILOG CPLEX Java API offers to solve mathematical programming problems. An overview of the architecture is given, then techniques for creating models are explained through examples.

ILOG Concert Technology for .NET Users offers an example of this API.

ILOG CPLEX Callable Library introduces the ILOG CPLEX Callable Library. It sketches the architecture of the product, explains the relation between the Interactive Optimizer and the Callable Library, and offers an example of programming with the Callable Library. It also provides an overview about the parameters you control in ILOG CPLEX.

Part II, Programming Considerations

Developing CPLEX Applications provides tips for developing applications with ILOG CPLEX, suggests ways to debug your applications built around ILOG CPLEX, and provides a checklist to help avoid common programming errors.

Managing Input and Output explains how to enter mathematical programs efficiently and how to generate meaningful output from your ILOG CPLEX applications. It also lists the available file formats for entering data into ILOG CPLEX and writing bases and solutions from ILOG CPLEX.

Licensing an Application tells you what you must consider when you want to license your ILOG CPLEX application for deployment.

Tuning Tool introduces a utility to help you decide whether default settings of parameters are most appropriate for your model. If the default settings are not ideal for your model, the tuning tool suggests other settings.

Part III, Continuous Optimization

Solving LPs: Simplex Optimizers goes deeper into aspects of linear programming with ILOG CPLEX. It explains how to tune performance and how to diagnose infeasibility in a model. It also offers an example showing you how to start optimizing from an advanced basis.

Solving LPs: Barrier Optimizer continues the exploration of optimizers for linear programming problems. It tells how to use the primal-dual logarithmic barrier algorithm implemented in the ILOG CPLEX Barrier Optimizer to solve large, sparse linear programming problems.

Solving Network-Flow Problems shows how to use the ILOG CPLEX Network Optimizer on linear programming problems based on a network model.

Solving Problems with a Quadratic Objective (QP) takes up programming problems in which the objective function may be quadratic. It, too, includes examples.

Solving Problems with Quadratic Constraints (QCP) introduces problems where the constraints are not strictly linear but may also include convex quadratic constraints and shows how to use the barrier optimizer to solve them.

Part IV, Discrete Optimization

Solving Mixed Integer Programming Problems (MIP) shows you how to handle MIPs. It particularly emphasizes performance tuning and offers a series of examples.

Solution Pool: Generating and Keeping Multiple Solutions explains how to accumulate and use multiple solutions to your MIP models.

Using Special Ordered Sets (SOS) sketches how to declare and use special ordered sets in formulating your model.

Using Semi-Continuous Variables: a Rates Example demonstrates how to use semi-continuous variables in a rate-setting problem.

Using Piecewise Linear Functions in Optimization: a Transport Example applies piecewise linear functions to model a transportation problem.

Logical Constraints in Optimization introduces logical constraints as they are implemented in Concert Technology and in the Callable Library.

Using Indicator Constraints explains a technique for expressing relations among constraints in a model by means of binary variables that turn on or off enforcement of a given constraint.

Using Logical Constraints: Food Manufacture 2 follows up that introduction to logical constraints with an example borrowed from a well known textbook about modeling.

Early Tardy Scheduling demonstrates logical constraints, piecewise linear functions in optimization, and aggressive MIP emphasis in a production planning example that includes penalties for earliness and tardiness.

Using Column Generation: a Cutting Stock Example shows how to formulate a model by generating columns one by one. It uses a cutting stock example to illustrate the technique.

Part V, Infeasibility and Unboundedness

Preprocessing and Feasibility introduces you to the effects of preprocessing on feasibility and infeasibility.

Managing Unboundedness explains what a report of unbounded means, suggests ways to avoid an unbounded outcome, and outlines means to diagnose the cause of unboundedness in your model.

Diagnosing Infeasibility by Refining Conflicts describes the conflict refiner, a feature of ILOG CPLEX that helps you identify contradictory constraints and bounds within your model.

Repairing Infeasibilities with FeasOpt documents a feature of ILOG CPLEX that may enable you to repair detected infeasibilities in your model.

Part VI, Advanced Programming Techniques

User-Cut and Lazy-Constraint Pools explains in greater detail how to manage your own pools of cuts and lazy constraints.

Using Goals shows how to use goals to control a MIP search.

Using Optimization Callbacks shows how to use callbacks to control a MIP search.

Goals and Callbacks: a Comparison compares the two different approaches.

Advanced Presolve Routines documents advanced aspects of presolve and aggregation more fully.

Advanced MIP Control Interface shows you how to exploit advanced features of MIP. It provides important additional information if you are using callbacks in your application.

Parallel Optimizers explains how to exploit parallel optimizers in case your hardware supports parallel execution.

The Index completes this manual.