A Pattern Search Filter Method for Nonlinear Programming without Derivatives

By Charles Audet, John E. Dennis Jr.. SIAM Journal on Optimization, 2001

326 citations recorded by the source corpus.

Subfields: Civil and Structural Engineering, Computational Theory and Mathematics, Numerical Analysis

View Paper: https://www.semanticscholar.org/paper/b5bede11db62a942f083228ed0ee7bc5f82f6fa1

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In Civil and Structural Engineering

In Computational Theory and Mathematics

In Numerical Analysis

Citations in the corpus

A derivative-free methodology with local and global search for the constrained joint optimization of well locations and controls (2014, methodology): “We now describe the use of the filter method with MADS (this discussion follows that given by Audet and Dennis [3] for GPS).”
Surrogate-Based Optimization Using Multifidelity Models with Variable Parameterization and Corrected Space Mapping (2008, unknown): “extended that framework to handle general nonlinear constraints using a filter method for step acceptance [22].”
Optimization by Direct Search: New Perspectives on Some Classical and Modern Methods (2003, methodology): “A strategy similar in spirit is used in Audet and Dennis [12], where a filter method inspired by that of Fletcher and Leyffer [108] is applied.”
Mixed Variable Optimization of a Load-Bearing Thermal Insulation System Using a Filter Pattern Search Algorithm (2004, methodology): “This required an extension of the theory (Audet and Dennis, 2001) to be able to deal with categorical variables.”
Mesh Adaptive Direct Search Algorithms for Constrained Optimization (2006, methodology): “The class of algorithms presented here differs significantly from previous GPS extensions [4, 22] to nonlinear constraints.”
Sourcebook of parallel computing (2003, background): “Audet and Dennis [56] extend the algorithms and analysis to nonlinear problems with discrete as well as continuous variables.”
Survey of modeling and optimization strategies to solve high-dimensional design problems with computationally-expensive black-box functions (2010, methodology): “This algorithm is applied to unconstrained, constrained, and black-box function optimization (Audet and Dennis 2004).”
Surrogate-based Analysis and Optimization (2005, methodology): “, SLP, SQP) the method has been extended to derivative-free methods [96] and holds promise to be useful in industrial applications (see, for example [97,98]).”
Line Search Filter Methods for Nonlinear Programming: Motivation and Global Convergence (2005, background): “Other researchers have also proposed global convergence results for different trust region based filter methods, such as for an interior point (IP) approach [20], a bundle method for non-smooth optimization [7], and a pattern search algorithm for derivative-free optimization [1].”
Surrogate-based methods for black-box optimization (2017, background): “A diﬀerent idea is to aggregate all these nonlinear constraints into a single constraint violation function and treats the optimization problem as an unconstrained bi-objective problem [4] or treat it as a single constraint [5].”