Sequential equality-constrained optimization for nonlinear programming

被引：10

作者：

Birgin, E. G. ^{[1
]}

Bueno, L. F. ^{[2
]}

Martinez, J. M. ^{[3
]}

机构：

[1] Univ Sao Paulo, Inst Math & Stat, Dept Comp Sci, Sao Paulo, SP, Brazil

[2] Univ Fed Sao Paulo, Inst Sci & Technol, Sao Jose Dos Campos, SP, Brazil

[3] Univ Estadual Campinas, Inst Math Stat & Sci Comp, Dept Appl Math, Campinas, SP, Brazil

来源：

COMPUTATIONAL OPTIMIZATION AND APPLICATIONS | 2016年 / 65卷 / 03期

基金：

巴西圣保罗研究基金会;

关键词：

Nonlinear programming; Sequential equality-constrained optimization; Augmented Lagrangian; Numerical experiments; LINEAR-DEPENDENCE CONDITION; INEXACT-RESTORATION; EULER DISCRETIZATION; MERIT FUNCTION; CONVERGENCE; ALGORITHM;

D O I：

10.1007/s10589-016-9849-6

中图分类号：

C93 [管理学]; O22 [运筹学];

学科分类号：

070105 ; 12 ; 1201 ; 1202 ; 120202 ;

摘要：

A novel idea is proposed for solving optimization problems with equality constraints and bounds on the variables. In the spirit of sequential quadratic programming and sequential linearly-constrained programming, the new proposed approach approximately solves, at each iteration, an equality-constrained optimization problem. The bound constraints are handled in outer iterations by means of an augmented Lagrangian scheme. Global convergence of the method follows from well-established nonlinear programming theories. Numerical experiments are presented.

引用

页码：699 / 721

页数：23

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