Features

• Handles constrained problems and problems with over 20,000 variables.
• Solves problems with non-real regions.
• Solves constrained nonlinear regression problems using Chi-square, L1, or L2 norms.
• Solves maximum-likelihood statistical problems.
• Solves very complex optimization problems.
• Optimizes financial returns.
• Solves enterprise-critical problems with high reliability.
• Hill-climbing algorithms can solve nonlinear functions with analytic equality and inequality constraints. Can also solve constrained (including bound-constrained) and unconstrained nonlinear functions.
• Solves problems using interval methods.
• Solves 0-1 integer problems with a linear or nonlinear objective function.
• Solves smaller constrained or unconstrained global nonlinear models.
• A feasible starting point is not required in order to solve a problem.