Feasible-side global convergence in experimental optimization

2014/06/16 by Gene A. Bunin, Bunin, Gene A., Grégory François +3 · 1 citation
Computer Science · Engineering · Mathematics · #Advanced Multi-Objective Optimization Algorithms #Advanced Optimization Algorithms Research #Control Systems and Identification #math.OC

paper · pdf · doi:10.48550/arxiv.1406.4063

35 pages, 3 figures, submitted to the SIAM Journal of Optimization

arxiv created 2014/06/16 · arxiv updated 2014/06/17

Abstract

We propose a canonical form of the experimental optimization problem and review the state-of-the-art methods to solve it. As guarantees of global convergence to an optimal point via only feasible iterates are absent in these methods, we present a set of sufficient conditions to enforce this behavior.

Citations

• An Automatic Method for Finding the Greatest or Least Value of a Function
• A Simplex Method for Function Minimization

Cited by

• Lipschitz constants in experimental optimization

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