arrow
Return

Constraint programming based Lagrangian relaxation for the automatic recording problem

delete2003-01-01
delete22
PRE
AI
M
Meinolf Sellmann *
T
Torsten Fahle
DOI:10.1023/A:1021845304798delete
deleteOriginal
deleteOriginal request for help
deleteShare
deleteSave
Abstract

Abstract

En 中文
Whereas CP methods are strong with respect to the detection of local infeasibilities, OR approaches have powerful optimization abilities that ground on tight global bounds on the objective. An integration of propagation ideas from CP and Lagrangian relaxation techniques from OR combines the merits of both approaches. We introduce a general way of how linear optimization constraints can strengthen their propagation abilities via Lagrangian relaxation. The method is evaluated on a set of benchmark problems stemming from a multimedia application. The experiments show the superiority of the combined method compared with a pure OR approach and an algorithm based on two independent optimization constraints.
Keywords:
optimization constraint
maximum weighted stable set constraint
knapsack constraint
Lagrangian relaxation
AI Summary

AI Summary

Key information extracted from the uploaded paper, including a brief overview, abstract, background, key highlights, visual analysis, and future outlook.

Journal

Annals of Operations Research cover
Annals of Operations Research
IF:
4.5
Papers:
8.0K
Citations:
2.1W

Organization

No organization information available