Features

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