Effective Lower Bounding Techniques for Pseudo-Boolean Optimization

Linear Pseudo-Boolean Optimization (PBO) is a widely used modeling framework in Electronic Design Automation (EDA). Due to significant advances in Boolean Satisfiability (SAT), new algorithms for PBO have emerged, which are effective on highly constrained instances. However, these algorithms fail to...

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Veröffentlicht in:Design, Automation and Test in Europe S. 660 - 665
Hauptverfasser: Manquinho, Vasco M., Marques-Silva, Joao
Format: Tagungsbericht
Sprache:Englisch
Veröffentlicht: Washington, DC, USA IEEE Computer Society 07.03.2005
IEEE
Schriftenreihe:ACM Conferences
Schlagworte:
Constraint optimization
Cost function
Design automation
Design optimization
Electronic design automation and methodology
High performance computing
Lagrangian functions
Learning systems
Mathematics of computing > Mathematical analysis > Mathematical optimization > Continuous optimization > Linear programming
Optimization methods
Theory of computation > Design and analysis of algorithms > Algorithm design techniques > Backtracking
Theory of computation > Design and analysis of algorithms > Mathematical optimization > Continuous optimization > Linear programming
Theory of computation > Logic
Tree data structures
ISBN:9780769522883, 0769522882
ISSN:1530-1591
Online-Zugang:Volltext
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Zusammenfassung:Linear Pseudo-Boolean Optimization (PBO) is a widely used modeling framework in Electronic Design Automation (EDA). Due to significant advances in Boolean Satisfiability (SAT), new algorithms for PBO have emerged, which are effective on highly constrained instances. However, these algorithms fail to handle effectively the information provided by the cost function of PBO. This paper addresses the integration of lower bound estimation methods with SAT-related techniques in PBO solvers. Moreover, the paper shows that the utilization of lower bound estimates can dramatically improve the overall performance of PBO solvers for most existing benchmarks from EDA.
Bibliographie:SourceType-Conference Papers & Proceedings-1
ObjectType-Conference Paper-1
content type line 25
ISBN:9780769522883
0769522882
ISSN:1530-1591
DOI:10.1109/DATE.2005.126