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Pattern search algorithms for mixed variable programming

Charles Audet (charlesa***at***crt.umontreal.ca)
J.E.Jr Dennis (dennis***at***caam.rice.edu)

Abstract: Many engineering optimization problems involve a special kind of discrete variable that {\em can} be represented by a number, but this representation has no significance. Such variables arise when a decision involves some situation like a choice from an unordered list of categories. This has two implications: The standard approach of solving problems with continuous relaxations of discrete variables is not available, and the notion of local optimality must be defined through a user-specified set of neighboring points. We present a class of direct search algorithms to provide limit points that satisfy some appropriate necessary conditions for local optimality for such problems. We give a more expensive, version of the algorithm that guarantees additional necessary optimality conditions. A small example illustrates the differences between the two versions. A real thermal insulation system design problem illustrates the efficacy of the user controls for this class of algorithms.

Keywords: Pattern search algorithm, convergence analysis, bound constrained optimization, mixed variable programming, derivative-free optimization.

Category 1: Integer Programming ((Mixed) Integer Nonlinear Programming )

Category 2: Nonlinear Optimization (Bound-constrained Optimization )

Citation: SIAM Journal on Optimization, Vol.11 No.3, 573-594, 2000

Download: [Postscript][PDF]

Entry Submitted: 03/16/2001
Entry Accepted: 03/16/2001
Entry Last Modified: 03/16/2001

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