Solving covering problems using LPR-based lower bounds

Authors:
Farzan Fallah;Stan Liao;Srinivas Devadas
Affiliations:
Futitsu Labs of America Inc., Sunnyvale, CA;Synopsys Inc., Mountain View, CA;Massachusetts Institute of Technology, Cambridge
Venue:
IEEE Transactions on Very Large Scale Integration (VLSI) Systems
Year:
2000

Citing 0
Cited 3

Instance-Specific Accelerators for Minimum Covering

The Journal of Supercomputing
Efficient Static Compaction of Test Sequence Sets through the Application of Set Covering Techniques

Proceedings of the conference on Design, automation and test in Europe - Volume 1
Finding two disjoint paths in a network with normalized α+-MIN-SUM objective function

ISAAC'05 Proceedings of the 16th international conference on Algorithms and Computation

Quantified Score

Hi-index	0.00

Visualization

Abstract

Unate and binate covering problems are a subclass of general integer linear programming problems with which several problems in logic synthesis, such as two-level logic minimization and technology mapping, are formulated. Previous branch-and-bound methods for solving these problems exactly use lower bounding techniques based on finding maximal independent sets. In this paper, we examine lower bounding techniques based on linear programming relaxation (LPR) for the covering problem. We show that a combination of traditional reductions (essentiality and dominance) and incremental computation of LPR-based lower bounds can exactly solve difficult covering problems orders of magnitude faster than traditional methods.