Solver Manuals

A large number of solvers for mathematical programming models have been hooked up to GAMS. The tables below provide a brief description of each solver, the model types each solver is cabable of solving, and the platforms supported by each solver. For general information on using GAMS solvers, see Solver Usage.

Solver Vendor Description
ALPHAECP Abo University MINLP solver based on the extended cutting plane (ECP) method
AMPL GAMS Development Corp A link to solve GAMS models using solvers within the AMPL modeling system
ANTIGONE 1.1 Princeton University Deterministic global optimization for MINLP
BARON The Optimization Firm, LLC Branch-And-Reduce Optimization Navigator for proven global solutions
BDMLP GAMS Development Corp LP and MIP solver that comes with any GAMS system
BENCH GAMS Development Corp A utility to facilitate benchmarking of GAMS solvers and solution verification
BONMIN 1.8 COIN-OR Foundation COIN-OR MINLP solver implementing various branch-and-bound and outer approximation algorithms
CBC 2.9 COIN-OR Foundation High-performance LP/MIP solver
CONOPT 3 ARKI Consulting and Development Large scale NLP solver
CONOPT 4 ARKI Consulting and Development Large scale NLP solver
CONVERT GAMS Development Corp Framework for translating models into scalar models of other languages
COUENNE 0.5 COIN-OR Foundation Deterministic global optimization for (MI)NLP
CPLEX 12.8 IBM ILOG High-performance LP/MIP solver
DE GAMS Development Corp Generates and solves the deterministic equivalent of a stochastic program, included in EMP/SP
DECIS G. Infanger, Inc. Large scale stochastic programming solver
DICOPT EDRC, Carnegie Mellon University Framework for solving MINLP models
EXAMINER GAMS Development Corp A tool for examining solution points and assessing their merit
GAMSCHK Bruce McCarl A System for Examining the Structure and Solution Properties of Linear Programming Problems Solved using GAMS
GLOMIQO 2.3 Princeton University Branch-and-bound global optimization for mixed-integer quadratic models
GUROBI 8.1 Gurobi Optimization High performance LP/MIP solver
GUSS GAMS Development Corp A framework for solving many instances of related models efficiently (Gather-Update-Solver-Scatter)
IPOPT 3.12 COIN-OR Foundation Interior Point Optimizer for large scale nonlinear programming
JAMS GAMS Development Corp Solver to reformulate extended mathematical programs (incl. LogMIP)
KESTREL NEOS Framework for using remote NEOS solvers with a local GAMS system
KNITRO 11.1 Artelys Large scale NLP solver
LGO Pinter Consulting Services A global-local nonlinear optimization solver suite
LINDO 12.0 Lindo Systems Inc. A stochastic solver from Lindo Systems, Inc. Includes an unrestricted version of LINDOGLOBAL
LINDOGLOBAL 12.0 Lindo Systems Inc. MINLP solver for proven global solutions
LINGO GAMS Development Corp A link to solve GAMS models using solvers within the LINGO modeling system
LOCALSOLVER 8.0 Innovation 24 Hybrid neighborhood local search solver
LS Least Square Solver A Linear Regression Solver for GAMS
MILES University of Colorado at Boulder MCP solver
MINOS Stanford University NLP solver
MOSEK 8 MOSEK ApS Large scale LP/MIP plus conic and convex non-linear programming system
MSNLP OptTek Systems and Optimal Methods Multi-start method for global optimization
NLPEC GAMS Development Corp MPEC to NLP translator that uses other GAMS NLP solvers
ODHCPLEX 4 Optimization Direct Inc ODHeuristic on top of Cplex
OQNLP OptTek Systems and Optimal Methods Multi-start method for global optimization
OsiCplex COIN-OR Foundation Bare-Bone link to CPLEX
OsiGurobi COIN-OR Foundation Bare-Bone link to Gurobi
OsiMosek COIN-OR Foundation Bare-Bone link to Mosek
OsiXpress COIN-OR Foundation Bare-Bone link to Xpress
PATHNLP University of Wisconsin - Madison Large scale NLP solver for convex problems
PATH University of Wisconsin - Madison Large scale MCP solver
PYOMO GAMS Development Corp A link to solve GAMS models using solvers within the PYOMO modeling system
SBB ARKI Consulting and Development Branch-and-Bound algorithm for solving MINLP models
SCIP 6.0 Zuse Institute Berlin et.al. High-performance Constraint Integer Programming solver
SELKIE University of Wisconsin - Madison Decomposition and parallel solution for EMP
SNOPT Stanford University Large scale SQP based NLP solver
SOLVEENGINE Satalia Link to use solvers of the Satalia SolveEngine with a local GAMS system
SOPLEX 4.0 Zuse Institute Berlin High-performance LP solver
XA Sunset Software Large scale LP/MIP solver
XPRESS 33.01 FICO High performance LP/MIP solver

Model Types

GAMS is able to formulate models in many different types of problem classes or model types. Typically, a solver will be capable of solving (i.e. will accept as input) more than one model type. The solver/model type matrix shows which solver is capable of which model type:

LP MIP NLP MCP MPEC CNS DNLP MINLP QCP MIQCP Stoch. Global
ALPHAECP
ANTIGONE 1.1 ✔ *
BARON ✔ *
BDMLP
BONMIN 1.8
CBC 2.9
CONOPT 3
CONOPT 4
COUENNE 0.5 ✔ *
CPLEX 12.8
DECIS
DICOPT
GLOMIQO 2.3 ✔ *
GUROBI 8.1
GUSS
IPOPT 3.12
KESTREL
KNITRO 11.1
LGO
LINDO 12.0 ✔ *
LINDOGLOBAL 12.0 ✔ *
LOCALSOLVER 8.0
MILES
MINOS
MOSEK 8
MSNLP
NLPEC
ODHCPLEX 4
OQNLP
PATH
SBB
SCIP 6.0 ✔ *
SNOPT
SOLVEENGINE
SOPLEX 4.0
XA
XPRESS 33.01

* deterministic global solver

When choosing a solver, some judgement should be applied when considering the listed model type capabilities for the solver - the same capability "check boxes" does not imply equality in capacity or suitability. For example, take a hypothetical solver WeOpt designed to solve MINLP models. Since the problem class MINLP includes NLP, MIP, and LP as subclasses, solver WeOpt could include these capabilities also. If WeOpt is also a good performer on NLP models, it would include that capability. But if it does not shine at all as a MIP or LP solver, we would choose not to include MIP and LP in the capability list for WeOpt. In such a case one can always solve using a more general model type (e.g. solve an LP model as NLP so WeOpt can be used), but WeOpt will not advertise itself as an LP solver. Since the WeOpt solver does not even recognize MCP or MPEC models, we don't include those capabilities.

There are other differences in solvers that are difficult to quantify or cannot be captured by a capability table like the one shown. For example, for nonconvex NLP or QCP models, one solver could look only for first-order stationary points, another for local solutions, a third for local solutions using a scatter search or similar search heuristic, and a fourth could do a true global search for the global optimum. The relative merits (measured typically by speed alone) of solvers is the subject of considerable benchmarking activity and discussion.

The GAMS sales team can help answer questions you may have about solver capability. We also offer free evaluation licenses to help you decide what solvers are most suitable for your models.

Supported Platforms

The solver/platform matrix shows which platforms each solver is supported on. In addition, where a vendor has discontinued solver support for a particular platform and we continue to ship the last available supported version, this version number is indicated as well.

x86 32bit
MS Windows
x86 64bit
MS Windows
x86 64bit
Linux
x86 64bit
Mac OS X
Sparc 64bit
SOLARIS
IBM Power 64bit
AIX
ALPHAECP
ANTIGONE 1.1
BARON 18.5.8
BDMLP
BONMIN 1.8
CBC 2.9
CONOPT 3
CONOPT 4
COUENNE 0.5
CPLEX 12.8 12.6 12.6
DECIS
DICOPT
GLOMIQO 2.3
GUROBI 8.1 7.5
GUSS
IPOPT 3.12
KESTREL
KNITRO 11.1 11.0
LGO
LINDO 12.0
LINDOGLOBAL 12.0
LOCALSOLVER 8.0
MILES
MINOS
MOSEK 8
MSNLP
NLPEC
ODHCPLEX 4
OQNLP 32bit
PATH
SBB
SCIP 6.0
SNOPT
SOLVEENGINE
SOPLEX 4.0
XA
XPRESS 33.01 32.01 29.01