Nonmonotone strategy for minimization of quadratics with simple constraints

被引：1

作者：

Diniz-Ehrhardt M.A. ^{[1
]}

Dostál Z. ^{[2
]}

Gomes-Ruggiero M.A. ^{[1
]}

Martínez J.M. ^{[1
]}

Santos S.A. ^{[1
]}

机构：

[1] Inst. Math., Stat. Sci. Computation, State University of Campinas, 13083-970 Campinas SP

[2] Department of Applied Mathematics, VŠB - Tech. Univ. of Ostrava, 708 33 Ostrava

来源：

Applications of Mathematics | 2001年 / 46卷 / 5期

基金：

巴西圣保罗研究基金会;

关键词：

Active set methods; Conjugate gradients; Quadratic programming;

D O I：

10.1023/A:1013752209845

中图分类号：

学科分类号：

摘要：

An algorithm for quadratic minimization with simple bounds is introduced, combining, as many well-known methods do, active set strategies and projection steps. The novelty is that here the criterion for acceptance of a projected trial point is weaker than the usual ones, which are based on monotone decrease of the objective function. It is proved that convergence follows as in the monotone case. Numerical experiments with bound-constrained quadratic problems from CUTE collection show that the modified method is in practice slightly more efficient than its monotone counterpart and has a performance superior to the well-known code LANCELOT for this class of problems.

引用

页码：321 / 338

页数：17

共 32 条

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