Constraint-Based Lagrangian Relaxation

被引：0

作者：

Fontaine, Daniel ^{[1
]}

Michel, Laurent ^{[1
]}

Van Hentenryck, Pascal ^{[2
]}

机构：

[1] Univ Connecticut, Storrs, CT 06269 USA

[2] Australian Natl Univ, NICTA, Canberra, ACT 0200, Australia

来源：

PRINCIPLES AND PRACTICE OF CONSTRAINT PROGRAMMING, CP 2014 | 2014年 / 8656卷

关键词：

SURROGATE GRADIENT ALGORITHM;

D O I：

暂无

中图分类号：

TP18 [人工智能理论];

学科分类号：

081104 ; 0812 ; 0835 ; 1405 ;

摘要：

This paper studies how to generalize Lagrangian relaxation to high-level optimization models, including constraint-programming and local search models. It exploits the concepts of constraint violation (typically used in constraint programming and local search) and constraint satisfiability (typically exploited in mathematical programming). The paper considers dual and primal methods, studies their properties, and discusses how they can be implemented in terms of high-level model combinators and algorithmic templates. Experimental results suggest the potential benefits of Lagrangian methods for improving high-level constraint programming and local search models.

引用

页码：324 / 339

页数：16

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