Global Optimization
Global Optimization
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New features in Global Optimization 10

Global Optimization 10 features three new regression functions and improved performance.

  • Multi-model regression for comparing model fit to data in an AIC and adjusted goodness-of-fit context
  • Multistep regression to find the model with the best fit from a candidate set of terms
  • Logistic regression

New features in Global Optimization 8

Global Optimization 8 has enhanced performance and reliability, including improved parallel computing, module handling, and bug fixes.

  • Utilizes parallel computing to speed up execution
  • Solves problems when initial values or part of the search space are complex
  • Accepts subscripted variable names
  • General nonlinear constrained optimization solvers
  • Accepts black box models (non-analytic)
  • Can solve problems in parallel mode
  • Built-in nonlinear regression (with confidence intervals and sensitivity analysis)
  • Maximum likelihood estimation
  • Detailed user manual with examples

General 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