Model business issues mathematically and solve them with IBM ILOG CPLEX Optimizer's powerful algorithms to produce precise and logical decisions.
IBM ILOG CPLEX Optimizer's mathematical programming technology enables decision optimization for improving efficiency, reducing costs, and increasing profitability.
 Fundamental algorithms: IBM ILOG CPLEX Optimizer provides flexible, highperformance mathematical programming solvers for linear programming, mixed integer programming, quadratic programming, and quadratically constrained programming problems. These include a distributed parallel algorithm for mixed integer programming to leverage multiple computers to solve difficult problems.
 Robust algorithms for demanding problems: IBM ILOG CPLEX Optimizer has solved problems with millions of constraints and variables.
 Industryleading support: IBM has an impressive rate of product improvement and ample support resources to serve you.
 High performance: IBM ILOG CPLEX Optimizer delivers the power needed to solve very large, realworld optimization problems, as well as the speed required for today's interactive decision optimization applications.
 Robust and reliable: A large installed base helps us make IBM ILOG CPLEX Optimizer better with each release. Every new feature is tested on the biggest, most diverse model library in the world.
 Flexible interfaces: IBM ILOG CPLEX Optimizer gives developers a variety of ways to interact with it during the development and deployment of their applications.
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 Automatic and dynamic algorithm parameter control
IBM ILOG CPLEX Optimizer automatically determines "smart" settings for a wide range of algorithm parameters, usually resulting in optimal mathematical programming solution performance. However, for a more handson approach, dozens of parameters may be manually adjusted, including algorithmic strategy controls, output information controls, optimization duration limits, and numerical tolerances. An automated tuning tool evaluates different parameter settings, recommending the best. Callbacks allow for even more control.  Fast, automatic restarts
 Linear programs can be modified, and then solved again in a fraction of the original solution time.
 Mixed integer programs can be modified and solved starting from a pool of prior solutions  A variety of problem modification options, such as:
 The ability to add and delete variables
 The ability to add and delete constraints
 The ability to modify objective, righthand side, bound and matrix coefficients
 The ability to change constraint types  A wide variety of input/output options, such as:
 Problem files: read/write MPS files, IBM ILOG CPLEX Optimizer LP files, MPS basis files, binary problem/basis files
 Log files: session information and various solution reports
 Solution files in XML format
 Each message type (such as RESULTS, WARNINGS or ERRORS) can be directed to specified files, or completely suppressed.  Post solution information and analysis, including:
 Objective function value
 Solution variable and slack values
 Constraint dual values (shadow prices)
 Variable reduced costs
 Righthand side, objective function, and bound sensitivity ranges
 Basic variables and constraints
 Solution infeasibilities (if any exist)
 Iteration/node count, solution time, process data
 Conflict finder for diagnosing problem infeasibilities
 Feasibility optimizer for automatic correction of infeasible models
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