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

被引：68

作者：

Conn, AR

Gould, NIM

Orban, D

Toint, PL

机构：

[1] IBM Corp, Thomas J Watson Res Ctr, Yorktown Heights, NY 10598 USA

[2] Rutherford Appleton Lab, Computat Sci & Engn Dept, Chilton, Oxon, England

[3] CERFACS, F-31057 Toulouse 1, France

[4] Fac Univ Notre Dame Paix, B-5000 Namur, Belgium

来源：

MATHEMATICAL PROGRAMMING | 2000年 / 87卷 / 02期

关键词：

D O I：

10.1007/s101070050112

中图分类号：

TP31 [计算机软件];

学科分类号：

081202 ; 0835 ;

摘要：

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.

引用

页码：215 / 249

页数：35

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[2]

[Anonymous], 1981, PRACTICAL METHODS OP

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