E-mail this pageCPLEX Solver Information
CPLEX is a high performance Linear Programming (LP) and Mixed Integer Programming (MIP) solver from ILOG.
For solving LP problems you can choose between the primal or dual simplex algorithm, the barrier algorithm and the network algorithm. The barrier (or interior point) algorithm offers an approach particulary efficient on large sparse problems. CPLEX can handle sparse matrices very efficiently. A presolver is used to reduce the size of the problem before it is solved, sometimes by an order of magnitude. CPLEX is very robust and reliable. It is capable of solving huge, real-world optimization problems. Almost always the default option settings of CPLEX are sufficient to solve a problem with excellent solution times.
The CPLEX branch-and-bound algorithm for solving MIP problems uses modern features like cutting planes and heuristics to find integer solutions. Combined with the state-of-the-art presolver it makes CPLEX a very powerful tool for solving large and difficult MIP problems.
Quadratic Programming
CPLEX can also be used to solve Quadratic Program (QP) and Quadratic Constraint Program (QCP) problems. Version 8.1 and higher can be used to solve Mixed Integer Quadratic Program (MIQP) problems, version 9.0 and higher can be used to solve Quadratic Constraint Programs, including the Mixed Integer Quadratic Constraint Programs (MIQCP).
Parallel CPLEX
The release of AIMMS 3.6, CPLEX 9.1 and higher provides a parallel CPLEX option allowing you to take advantage of the availability of additional CPUs to speed up performance while solves a specific model. CPLEX 11.0 extends the functionality of the parallel MIP optimizer to include two modes of operation. In deterministic mode, a newly implemented search algorithm exploits parallelism in solving nodes of the branch-and-cut tree, but produces a repeatable, invariant solution path. In opportunistic mode, the search algorithm, takes full advantage of parallelism; it performs less synchronization between threads and allows random tie breaking, which may result in different solution paths but potentially faster performance.
Indicator constraints
The release of AIMMS 3.7, CPLEX 10.1 and higher provides indicators. Indicators are new constraint types that enable the user to express particular modeling constructs among variables by identifying a binary variable to control whether or not a specified linear constraint is active. Formulations using indicator constraints are more numerically robust and accurate than conventional formulations involving so-called Big M data.
Solution Polishing
Also the new solution polishing heuristic of CPLEX is available in AIMMS 3.7 to boost performance on certain types of models. Solution Polishing is appropriate for finding the best solutions to complex and difficult MIP models within a specified time and is used to improve the best solution at the end of the branch-and-cut process if optimality has not been proven. It can also be used instead of the branch-and-cut algorithm if an initial solution can be found in the root node.
Performance improvements CPLEX 11
ILOG CPLEX 11.0 introduces a new search algorithm—dynamic search. It is innovative in its integration and sequencing of the usual branching, nodes and cuts in branch-and-cut algorithms. CPLEX 11.0 retains its conventional branch-and-cut algorithm, but with advances in branching, cuts and heuristics. By selecting the more efficient of the two search strategies, CPLEX 11.0 improves the time to optimality by 15% on average for models solved in less than one minute; and it solves models in the range of one minute to one hour an average of three times faster. For hard models requiring more than one hour to solve, the speed up is a factor of ten on average.
Website: http://www.ilog.com/products/cplex
Version(s):
- AIMMS supports CPLEX 8.1 - 11.0. Also available in free trial license of AIMMS.
