An interior point algorithm for large-scale nonlinear programming

被引：1239

作者：

Byrd, RH ^{[1
]}

Hribar, ME

Nocedal, J

机构：

[1] Univ Colorado, Dept Comp Sci, Boulder, CO 80309 USA

[2] Rice Univ, CAAM Dept, Houston, TX 77005 USA

[3] Northwestern Univ, ECE Dept, Evanston, IL 60208 USA

来源：

SIAM JOURNAL ON OPTIMIZATION | 1999年 / 9卷 / 04期

关键词：

constrained optimization; interior point method; large-scale optimization; nonlinear programming; primal method; primal-dual method; sequential quadratic programming; barrier method; trust region method;

D O I：

10.1137/S1052623497325107

中图分类号：

O29 [应用数学];

学科分类号：

070104 ;

摘要：

The design and implementation of a new algorithm for solving large nonlinear programming problems is described. It follows a barrier approach that employs sequential quadratic programming and trust regions to solve the subproblems occurring in the iteration. Both primal and primal-dual versions of the algorithm are developed, and their performance is illustrated in a set of numerical tests.

引用

页码：877 / 900

页数：24

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