High-performance mathematical programming solver for linear programming, mixed integer programming and quadratic programming
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 optimisation for improving efficiency, reducing costs, and increasing profitability.
CPLEX Optimizer provides flexible, high-performance mathematical programming solvers for linear programming, mixed integer programming, quadratic programming, and quadratically constrained programming problems. These solvers include a distributed parallel algorithm for mixed integer programming to leverage multiple computers to solve difficult problems.
Find the best solution from among billions of alternatives for business decisions such as:
- What is the best plan for my factory to meet the demand for finished products while minimising machine setup costs and factoring in scheduled arrivals of raw materials?
- How to optimally assign marketing campaigns to customers by considering predictions around customers’ propensity to respond and maximise expected purchases while fitting the budget constraints?
CPLEX CP Optimizer
Solve detailed scheduling problems and combinatorial optimisation problems using CPLEX CP Optimizer.
CPLEX Optimization Studio interfaces
Build and deploy optimisation applications using interfaces like ILOG Concert Technology and CPLEX Callable Library.
IBM ILOG CPLEX Optimizer performance benchmarks
Read about performance improvements with CPLEX mathematical programming and constraint programming solvers.
IBM ILOG CPLEX Optimizer delivers the power needed to solve very large, real-world optimisation problems, as well as the speed required for today’s interactive decision optimisation applications. IBM ILOG CPLEX Optimizer has solved optimisation models with millions of constraints and variables.
IBM ILOG CPLEX Optimizer gives developers a variety of ways to interact with it during the development and deployment of their applications.
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.