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Optimal Anytime Search For Constrained Nonlinear Programming (2001)
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@TECHREPORT{Chen01optimalanytime,
author = {Yixin Chen},
title = {Optimal Anytime Search For Constrained Nonlinear Programming},
institution = {},
year = {2001}
}
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Abstract
In this thesis, we study optimal anytime stochastic search algorithms (SSAs) for solving general constrained nonlinear programming problems (NLPs) in discrete, continuous and mixed-integer space. The algorithms are general in the sense that they do not assume di#erentiability or convexity of functions. Based on the search algorithms, we develop the theory of SSAs and propose optimal SSAs with iterative deepening in order to minimize their expected search time. Based on the optimal SSAs, we then develop optimal anytime SSAs that generate improved solutions as more search time is allowed. Our SSAs
