Convergence rates of a global optimization algorithm
Received: 05 June 1987 Revised: 02 February 1990 DOI:
Cite this article as: Mladineo, R.H. Mathematical Programming (1992) 54: 223. doi:10.1007/BF01586051 Abstract
This paper presents a best and worst case analysis of convergence rates for a deterministic global optimization algorithm. Superlinear convergence is proved for Lipschitz functions which are convex in the direction of the global maximum (concave in the direction of the global minimum). Computer results are given, which confirm the theoretical convergence rates.
Key words Global optimization Lipschitz functions References
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© The Mathematical Programming Society, Inc. 1992