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, high-performance 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.
- Industry-leading 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, real-world 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.
- 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 hands-on 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, right-hand 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
- Right-hand 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