Proceedings Article10.1145/240518.240555
On solving covering problems
Olivier Coudert
- 01 Jun 1996
- pp 197-202
139
TL;DR: This paper investigates the complexity and approximation ratio of two lower bound computation algorithms from both a theoretical and practical point of view and presents a new pruning technique that takes advantage of the partitioning.
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Abstract: The set covering problem and the minimum cost assignment problem (respectively known as unate and binate covering problem) arise throughout the logic synthesis flow. This paper investigates the complexity and approximation ratio of two lower bound computation algorithms from both a theoretical and practical point of view. It also presents a new pruning technique that takes advantage of the partitioning.
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References
A Greedy Heuristic for the Set-Covering Problem
TL;DR: It turns out that the ratio between the two grows at most logarithmically in the largest column sum of A when all the components of cT are the same, which reduces to a theorem established previously by Johnson and Lovasz.
2.8K
•Book
Synthesis and optimization of digital circuits
Giovanni De Micheli
- 01 Jan 1994
TL;DR: This book covers techniques for synthesis and optimization of digital circuits at the architectural and logic levels, i.e., the generation of performance-and-or area-optimal circuits representations from models in hardware description languages.
Approximation algorithms for combinatorial problems
TL;DR: For the problem of finding the maximum clique in a graph, no algorithm has been found for which the ratio does not grow at least as fast as n^@e, where n is the problem size and @e>0 depends on the algorithm.
2.5K
•Book
Logic Minimization Algorithms for VLSI Synthesis
Robert K. Brayton,Alberto Sangiovanni-Vincentelli,Curtis T. McMullen,Gary D. Hachtel +3 more
- 31 Aug 1984
TL;DR: The ESPRESSO-IIAPL as discussed by the authors is an extension of the ESPRSO-IIC with the purpose of improving the efficiency of Tautology and reducing the number of blocks and covers.
1.8K
Approximation algorithms for combinatorial problems
David S. Johnson
- 30 Apr 1973
TL;DR: For the problem of finding the maximum clique in a graph, no algorithm has been found for which the ratio does not grow at least as fast as 0(nε), where n is the problem size and ε> 0 depends on the algorithm.
1.6K
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