The Strength of CPLEX, Brought to You by AMPL
IBM ILOG CPLEX has been a well known and widely used large-scale solver for over three decades. Its efficiency and robustness have been demonstrated through varied applications in thousands of commercial installations worldwide.
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IBM CPLEX is a renowned product from IBM, a global technology leader with a rich history dating back to the early 20th century. CPLEX, standing as an acronym for ‘Complex Linear Programming Expert’, is a high-performance mathematical programming solver specializing in linear programming (LP), mixed integer programming (MIP), and quadratic programming (QP). It was developed to address the complex optimization needs in various industries, ranging from logistics and finance to manufacturing and energy. The genesis of CPLEX traces back to the late 1980s when it was initially created by CPLEX Optimization Inc., a company formed to commercialize the dual simplex algorithm. IBM acquired CPLEX in 2009 as part of its purchase of ILOG, the then-owner of CPLEX. This acquisition not only expanded IBM’s portfolio in business analytics but also reinforced CPLEX’s position as a leading tool for optimization problems, known for its robustness, efficiency, and advanced algorithms. Today, CPLEX remains at the forefront of solving large-scale, complex optimization problems, helping businesses and researchers make better decisions and optimize their operations more effectively.
Linear and quadratic optimization in continuous and integer variables. Support is provided for both convex and nonconvex quadratic objectives, and for convex quadratic constraints.
Logical implications in the form of “indicator” constraints. Convex quadratic expressions in objectives, and convex quadratic constraints of elliptic and conic types.
For continuous problems, primal and dual simplex, interior-point (barrier); for integer problems, advanced branch-and-bound with presolve, feasibility heuristics, and cut generators. For continuous problems comprised mostly or entirely of linear network flow constraints, network simplex.
Shared-memory parallel processing for barrier, branch-and-bound. Concurrent optimization by several methods to determine best choice. Special facilities for parameter tuning and infeasibility diagnosis.
ILOG CP for AMPL, covered by the CPLEX for AMPL license, for access to constraint programming and more general CPLEX handling of logical constraints.
