Plug-in Solver Engines for Analytic Solver, RASON, Solver SDK

These Solver engines "plug into" Analytic Solver (Desktop and Cloud versions). 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. They can be controlled by your VBA code in Analytic Solver Desktop. And the same Solver Engines can be used with our Solver SDK Platform product, and controlled by your code in C++, C#, Java, R, Python, and JavaScript.

Linear and Quadratic Programming Problems

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

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

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 XPRESS Solver Engine or Gurobi Solver Engine may be fastest on these problems.

These Solver engines "plug into" Analytic Solver (Desktop and Cloud versions). 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. They can be controlled by your VBA code in Analytic Solver Desktop. And the same Solver Engines can be used with our Solver SDK Platform product, and controlled by your code in C++, C#, Java, R, Python, and JavaScript.

Smooth Nonlinear Optimization Problems

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

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

The OptQuest Solver is designed for nonsmooth optimization, and offers very good performance on arbitrary Excel models, especially if they include integer variables.  The Large-Scale SQP Solver integrates the Evolutionary Solver (which uses both genetic algorithms, and tabu search/scatter search methods employed by the OptQuest 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.