Features

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

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

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

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