The Solver SDK Platform includes built-in LP/Quadratic, SOCP Barrier, GRG Nonlinear, 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 the Solver SDK Platform with field-installable Solver engines.
- Side-by-side comparison of Plug-in Solver Engines [PDF]
- Register to Download a free two-week trial
- Download optional Solver engines
Each Solver Engine also "plugs in" to our Platform products for Excel -- Risk Solver Platform and Premium Solver Platform -- and is licensed for Dual use in both Solver SDK Platform and one of the Platform products.
- Linear and Quadratic Programming Problems
- Conic Optimization Problems
- Integer and Constraint Programming Problems
- Smooth Nonlinear Optimization Problems
- Global Optimization Problems
- Nonsmooth Optimization Problems
The following Solver Engines are currently available. Check back frequently, since Frontline Systems is constantly upgrading these Solver Engines to extend the flexibility and power of the Solver SDK Platform product line!
Linear and Quadratic Programming Problems
Solver Engine | Variables | Constraints | Features |
32,000 | 32,000 | Sparsity, factorization | |
Unlimited | Unlimited | Sparsity, factorization | |
Unlimited | Unlimited | Also handles QCP, SOCP, convex NLP models | |
Unlimited | Unlimited | Also handles NLP, NSP models | |
Unlimited | Unlimited | Sparsity, dual Simplex, Barrier method, MIP performance | |
Unlimited | Unlimited | Sparsity, dual Simplex, best MIP performance |
You can also use large-scale nonlinear Solver engines with the Premium Solver Platform 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 |
12,000 | 12,000 | Sparsity, degeneracy handling | |
Unlimited | Unlimited | Also handles smooth convex NLP models | |
Unlimited | Unlimited | Also handles smooth NLP models | |
Unlimited | Unlimited | Also handles smooth NLP models | |
Unlimited | Unlimited | Also handles QP and QCP models (not 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 |
32,000 | 32,000 | Cut generation, heuristics, dual Simplex, many others; up to 32,000 integer variables | |
Unlimited | Unlimited | Cut generation, heuristics, dual Simplex, many others; unlimited integer variables | |
Unlimited | Unlimited | Basic cut generation, 0-1 probing, heuristics, QCP and SOCP models | |
Unlimited | Unlimited | Basic cut generation, 0-1 probing, heuristics | |
Unlimited | Unlimited | Advanced cut generation, 0-1 probing, heuristics, dual Simplex, many others | |
Unlimited | Unlimited | Advanced cut generation and heuristics; highest performance on LP/MIP problems |
Every Solver engine for the Premium Solver Platform 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 XPRESS Solver Engine or Gurobi Solver Engine may be fastest on these problems.
Smooth Nonlinear Optimization Problems
Solver Engine | Variables | Constraints | Features |
12,000 | 12,000 | Sparsity, degeneracy handling | |
Unlimited | Unlimited | Sparsity, capacity, speed on convex NLP models | |
Unlimited | Unlimited | Sparsity, capacity, speed | |
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 the Premium Solver Platform to solve smooth NLP problems, but the above Solver engines offers better performance on such problems.
Global Optimization Problems
Solver Engine | Variables | Constraints | Features |
5,000 | 1,000 | Tabu search, scatter search | |
See below | See below | Multistart methods | |
See below | See below | Multistart methods | |
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 |
5,000 | 1,000 | Tabu search, scatter search | |
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.