A primal-dual trust-region algorithm for non-convex nonlinear programming

Andy Conn; Nick Gould; Dominique Orban; Philippe Toint

A primal-dual trust-region algorithm for non-convex nonlinear programming

Andy Conn, Nick Gould, Dominique Orban, Philippe Toint

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Abstract

A new primal-dual algorithm is proposed for the minimization of non-convex objective functions subject to general inequality and linear equality constraints. The method uses a primal-dual trust-region model to ensure descent on a suitable merit function. Convergence is proved to second-order critical points from arbitrary starting points. Numerical results are presented for general quadratic programs.

Original language	English
Pages (from-to)	215-249
Number of pages	35
Journal	Mathematical Programming Series B
Volume	87
Issue number	2
Publication status	Published - 1 Apr 2000

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title = "A primal-dual trust-region algorithm for non-convex nonlinear programming",

abstract = "A new primal-dual algorithm is proposed for the minimization of non-convex objective functions subject to general inequality and linear equality constraints. The method uses a primal-dual trust-region model to ensure descent on a suitable merit function. Convergence is proved to second-order critical points from arbitrary starting points. Numerical results are presented for general quadratic programs.",

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N2 - A new primal-dual algorithm is proposed for the minimization of non-convex objective functions subject to general inequality and linear equality constraints. The method uses a primal-dual trust-region model to ensure descent on a suitable merit function. Convergence is proved to second-order critical points from arbitrary starting points. Numerical results are presented for general quadratic programs.

AB - A new primal-dual algorithm is proposed for the minimization of non-convex objective functions subject to general inequality and linear equality constraints. The method uses a primal-dual trust-region model to ensure descent on a suitable merit function. Convergence is proved to second-order critical points from arbitrary starting points. Numerical results are presented for general quadratic programs.

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A primal-dual trust-region algorithm for non-convex nonlinear programming

Abstract

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ADALGOPT: ADALGOPT - Advanced algorithms in nonlinear optimization

Interior point algorithms

Cite this

A primal-dual trust-region algorithm for non-convex nonlinear programming

Abstract

Access to Document

Other files and links

Fingerprint

Projects

ADALGOPT: ADALGOPT - Advanced algorithms in nonlinear optimization

Interior point algorithms

Cite this