Solver Platforms for Excel - Field-Installable Solver Engines

Field-Installable Solver Engines

Risk Solver Platform and Premium Solver Platform both include built-in LP/Quadratic, SOCP Barrier, GRG Nonlinear, Interval Global, and Evolutionary Solver engines that can solve:

• Linear programming and quadratic programming problems up to 8,000 variables (with up to 2,000 integer variables)
• Quadratically constrained and conic (second order cone) optimization problems up to 2,000 variables
• Smooth nonlinear optimization, global optimization, and nonsmooth optimization problems up to 500 variables

If you want to solve optimization problems larger than these limits, or if you want even faster solution times on problems within these limits, you can expand Risk Solver Platform or Premium Solver Platform with field-installable Solver engines.

These Solver engines "plug into" either Platform product.  You select them from the Solver engine dropdown list, just as you do for the built-in Solvers, and you can display and change their options and parameters in the same way as built-in Solver engines.  They can be interrupted during the solution process by pressing the ESC key, and they produce reports in spreadsheet form, just like the built-in Solver engines.  And they can be controlled by VBA programs using the APIs supported by both Risk Solver Platform and Premium Solver Platform.

• Linear and Quadratic Programming Problems
• Conic Optimization Problems
• Integer and Constraint Programming Problems
• Smooth Nonlinear Optimization Problems
• Global Optimization Problems
• Nonsmooth Optimization Problems

If you upgrade from Premium Solver Platform to Risk Solver Platform, your plug-in Solver engines will continue to work.  And there's more -- each Solver engine also "plugs into" Solver Platform SDK -- our flagship product for software developers who are integrating optimization and simulation capabilities into custom applications -- and is licensed for dual use with both Risk Solver Platform / Premium Solver Platform and Solver Platform SDK.

The following Solver engines are currently available.  Check back frequently, since Frontline Systems is adding even more Solver engines to extend the flexibility and power of our Solver Platforms!

Linear and Quadratic Programming Problems

Solver Engine	Variables	Constraints	Features
Std. Large-Scale LP/QP Solver	32,000	32,000	Sparsity, factorization
Ext. Large-Scale LP/QP Solver	Unlimited	Unlimited	Sparsity, factorization
Std. MOSEK Solver	32,000	32,000	Also handles QCP, SOCP models
Ext. MOSEK Solver	Unlimited	Unlimited	Also handles QCP, SOCP, convex NLP models
Large-Scale SQP Solver	Unlimited	Unlimited	Also handles NLP, NSP models
XPRESS Solver Engine	Unlimited	Unlimited	Sparsity, dual Simplex, Barrier method, MIP performance
Gurobi Solver Engine	Unlimited	Unlimited	Sparsity, dual Simplex, best MIP performance

You can also use large-scale nonlinear Solver engines with our Solver Platforms to solve large-scale LP problems, but the above Solver engines offer much better performance on such problems.  All of the above Solver Engines are typically very fast on large-scale LP problems.  Note: Solution of large-scale QP problems using any of these Solver Engines may be limited by available memory.

Conic Optimization Problems

Solver Engine	Variables	Constraints	Features
Large-Scale GRG Solver	4,000	4,000	Sparsity, degeneracy handling
Ext. Large-Scale GRG Solver	12,000	12,000	Sparsity, degeneracy handling
Std. MOSEK Solver	32,000	32,000	Best performance on SOCPs
Ext. MOSEK Solver	Unlimited	Unlimited	Also handles smooth convex NLP models
Large-Scale SQP Solver	Unlimited	Unlimited	Also handles smooth NLP models
KNITRO Solver	Unlimited	Unlimited	Also handles smooth NLP models

The MOSEK Solver is designed for conic optimization, and offers the best performance on SOCP problems.  The Large-Scale GRG Solver, Large-Scale SQP Solver and KNITRO Solver are designed to solve both convex and non-convex NLP problems, but they also handle second order cone (SOC) constraints.

Integer and Constraint Programming Problems

Solver Engine	Variables	Constraints	Features
Std. Large-Scale LP/QP Solver	32,000	32,000	Cut generation, heuristics, dual Simplex, many others; up to 32,000 integer variables
Ext. Large-Scale LP/QP Solver	Unlimited	Unlimited	Cut generation, heuristics, dual Simplex, many others; unlimited integer variables
Std. MOSEK Solver	32,000	32,000	Cut generation, heuristics, dual Simplex, 32,000 integer variables, QCP and SOCP models
Ext. MOSEK Solver	Unlimited	Unlimited	Cut generation, heuristics, dual Simplex, unlimited integer variables, QCP, SOCP, convex NLP models
Large-Scale SQP Solver	Unlimited	Unlimited	Basic cut generation, 0-1 probing, heuristics
XPRESS Solver Engine	Unlimited	Unlimited	Advanced cut generation, 0-1 probing, heuristics, dual Simplex, many others
Gurobi Solver Engine	Unlimited	Unlimited	Advanced cut generation and heuristics; highest performance on LP/MIP problems

Every Solver engine for our Solver Platforms will handle problems with integer variables, including variables subject to the "alldifferent" constraint.  If you have a large or challenging mixed-integer or constraint programming problem, however, the Large-Scale LP Solver may be faster, and the XPRESS Solver may be fastest on these problems.

Smooth Nonlinear Optimization Problems

Solver Engine	Variables	Constraints	Features
Large-Scale GRG Solver	4,000	4,000	Sparsity, degeneracy handling
Ext. Large-Scale GRG Solver	12,000	12,000	Sparsity, degeneracy handling
Ext. MOSEK Solver	Unlimited	Unlimited	Sparsity, capacity, speed on convex NLP models
Large-Scale SQP Solver	Unlimited	Unlimited	Sparsity, capacity, speed
KNITRO Solver	Unlimited	Unlimited	Sparsity, capacity, speed

The MOSEK Solver can handle very large smooth convex NLP problems, but it does not support non-convex problems.  The Large-Scale SQP Solver can solve very large smooth convex and non-convex NLP problems, but its practical upper limit on the degrees of freedom (i.e. the number of variables minus the number of constraints that are binding at the solution) is about 2,000. Thanks to both interior point methods and active-set methods, the KNITRO Solver can handle the largest smooth convex and non-convex NLP models, and the number of degrees of freedom can be much larger than 2,000.

You can also use Solver engines designed for global and nonsmooth optimization with our Solver Platforms to solve smooth NLP problems, but the above Solver engines offers better performance on such problems.

Global Optimization Problems

Solver Engine	Variables	Constraints	Features
OptQuest Solver	5,000	1,000	Tabu search, scatter search
Large-Scale GRG Solver	See below	See below	Multistart methods
Ext. Large-Scale GRG Solver	See below	See below	Multistart methods
Large-Scale SQP Solver	See below	See below	Multistart methods
KNITRO Solver	See below	See below	Multistart methods

The OptQuest Solver finds global solutions and also handles nonsmooth problems, but it has no test for global optimality.  The other Solver Engines use the Premium Solver Platform's multistart or clustering methods to seek all locally optimal solutions, and select the best of these as the probable globally optimal solution. 

Although these Solver Engines accept large or unlimited size problems, the practical limit for global optimization problems is much lower -- comparable to the OptQuest Solver.

Nonsmooth Optimization Problems

Solver Engine	Variables	Constraints	Features
OptQuest Solver	5,000	1,000	Tabu search, scatter search
Large-Scale SQP Solver	Unlimited	Unlimited	Evolutionary Solver plus SQP

The OptQuest Solver is designed for nonsmooth optimization, and usually offers the best performance on arbitrary Excel models, especially if they include integer variables.  The Large-Scale SQP Solver integrates the Evolutionary Solver and is very effective for problems with some nonsmooth, and other smooth and linear, variable occurrences -- but the practical limit on nonsmooth variables and constraints is much lower than for smooth problems.

You can try Solver engines designed for smooth non-convex nonlinear optimization on nonsmooth problems, but they may not successfully deal with nonsmooth or discontinuous functions that are important to the model.

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