The Constraint Consensus Method for Finding Approximately Feasible Points in Nonlinear Programs

Authors:
John W. Chinneck
Affiliations:
-
Venue:
INFORMS Journal on Computing
Year:
2004

Citing 0
Cited 7

Algorithms for the quasiconvex feasibility problem

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Multistart algorithms for seeking feasibility

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Improving solver success in reaching feasibility for sets of nonlinear constraints

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Algorithms for the quasiconvex feasibility problem

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Feasibility and Constraint Analysis of Sets of Linear Matrix Inequalities

INFORMS Journal on Computing
Improved constraint consensus methods for seeking feasibility in nonlinear programs

Computational Optimization and Applications
Differential evolution with multi-constraint consensus methods for constrained optimization

Journal of Global Optimization

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Abstract

This paper develops a method for moving quickly and cheaply from an arbitrary initial point at an extreme distance from the feasible region to a point that is relatively near the feasible region of a nonlinearly constrained model. The method is a variant of a projection algorithm that is shown to be robust, even in the presence of nonconvex constraints and infeasibility. Empirical results are presented.