Mathematical Programming

, Volume 110, Issue 3, pp 615–639

Constraint incorporation in optimization

Authors

    • Department of MathematicsBowdoin College
Full Lenght Paper

DOI: 10.1007/s10107-006-0016-1

Cite this article as:
Levy, A.B. Math. Program. (2007) 110: 615. doi:10.1007/s10107-006-0016-1
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Abstract

Numerical methods for solving constrained optimization problems need to incorporate the constraints in a manner that satisfies essentially competing interests; the incorporation needs to be simple enough that the solution method is tractable, yet complex enough to ensure the validity of the ultimate solution. We introduce a framework for constraint incorporation that identifies a minimal acceptable level of complexity and defines two basic types of constraint incorporation which (with combinations) cover nearly all popular numerical methods for constrained optimization, including trust region methods, penalty methods, barrier methods, penalty-multiplier methods, and sequential quadratic programming methods. The broad application of our framework relies on addition and chain rules for constraint incorporation which we develop here.

Keywords

Constrained optimizationNumerical optimizationPenalty methodsBarrier methodsTrust region methodsPenalty-multiplier methodsSequential quadratic programmingVariational analysis

Copyright information

© Springer-Verlag 2006