Effective Lower Bounding Techniques for Pseudo-Boolean Optimization

• Published in: Design, Automation and Test in Europe pp. 660 - 665
• Main Authors: Manquinho, Vasco M., Marques-Silva, Joao
• Format: Conference Proceeding
• Language: English
• Published: Washington, DC, USA IEEE Computer Society 07.03.2005; IEEE
• Series: ACM Conferences
• Subjects: 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; 1558-1101
• DOI: 10.1109/DATE.2005.126

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.