Software for global optimization
The interactive graphical software that implements numeric methods and other techniques to solve global optimization problems is presented. The Bayesian approach to the optimization is the underlying idea of numeric methods used. Software is designed to solve deterministic and stochastic problems of different complexity and with many variables. It includes global and local optimization methods for differentiable and nondifferentiable functions. Implemented numerical techniques for global optimization vary from simple Monte-Carlo simulation to Bayesian methods by J. Mockus and extrapolation theory based methods by Zilinskas. Local optimization techniques includes simplex method of Nelder and Mead method of nonlinear programming by Shitkowski, and method of stochastic approximation with Bayesian step size control by J. Mockus. Software is interactive, it allows user to start and stop chosen method of global or local optimization, define and change its parameters and examine the solution process. Out-put from solution process is both numerical and graphical. Currently available graphical features are the projection of the objective function on a chosen plane and convergence plot. Both these features let the user easily observe solution process and interactively modify it. More features can be added in a standard way. It is up to the user how many graphical and numerical output features activate or deactivate at any given time. Software is implemented in C++ using X Windows as graphical platform.
- OSTI ID:
- 36633
- Report Number(s):
- CONF-9408161-; TRN: 94:009753-0624
- Resource Relation:
- Conference: 15. international symposium on mathematical programming, Ann Arbor, MI (United States), 15-19 Aug 1994; Other Information: PBD: 1994; Related Information: Is Part Of Mathematical programming: State of the art 1994; Birge, J.R.; Murty, K.G. [eds.]; PB: 312 p.
- Country of Publication:
- United States
- Language:
- English
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