Negative thinking by incremental problem solving: application to unate covering
Goldberg,Carloni,Villa,Sangiovanni-Vincentelli +3 more
- 01 Jan 1997
- pp 91-99
TL;DR: In this paper, a negative thinking search is used to solve the unate covering problem, where a good solution is reached quickly and then improved only a few times before the optimum is found, but it does not yield any improvement of the cost function.
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Abstract: We introduce a new technique to solve exactly a discrete optimization problem, based on the paradigm of "negative" thinking. The motivation is that when searching the space of solutions, often a good solution is reached quickly and then improved only a few times before the optimum is found: hence most of the solution space is explored to certify optimality, but it does not yield any improvement of the cost function. So it is quite natural for an algorithm to be "skeptical" about the chance to improve the current best solution. For illustration we have applied our approach to the unate covering problem. We designed a procedure, raiser, implementing a negative thinking search, which is incorporated into a common branch-and-bound procedure. Experiments show that our program, AURA, outperforms both ESPRESSO and our enhancement of ESPRESSO using Coudert's limit lower bound. It is always faster and in the most difficult examples either has a running time better by up to two orders of magnitude, or the other programs fail to finish due to timeout or spaceout. The package SCHERZO is faster on some examples and loses on others, due to a less powerful pruning strategy of the search space, partially mitigated by a more effective computation of the maximal independent set.
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Citations
Two-level logic minimization
Olivier Coudert,Tsutomu Sasao +1 more
- 01 Nov 2001
TL;DR: This chapter presents both exact and heuristic two-level logic minimization algorithms, and shows various techniques to reduce the complexity of covering problems and discusses branching heuristics.
55
Search pruning techniques in SAT-based branch-and-bound algorithms for the binate covering problem
TL;DR: Experimental results indicate that the proposed search pruning techniques are effective and can provide significant performance gains for specific classes of instances of the UCP/BCP.
Logic Synthesis Meets Machine Learning: Trading Exactness for Generalization
Shubham Rai,Walter Lau Neto,Yukio Miyasaka,Xinpei Zhang,Mingfei Yu,Qingyang Yi,Masahiro Fujita,Guilherme B. Manske,Matheus F. Pontes,Leomar S. da Rosa,Marilton Sanchotene de Aguiar,Paulo F. Butzen,Po-Chun Chien,Yu-Shan Huang,Hoa-Ren Wang,Jie-Hong R. Jiang,Jiaqi Gu,Zheng Zhao,Zixuan Jiang,David Z. Pan,Brunno Abreu,Isac de Souza Campos,Augusto Berndt,Cristina Meinhardt,Jonata Tyska Carvalho,Mateus Grellert,Sergio Bampi,Aditya Lohana,Akash Kumar,Wei Zeng,Azadeh Davoodi,Rasit O. Topaloglu,Yuan Zhou,Jordan Dotzel,Yichi Zhang,Hanyu Wang,Zhiru Zhang,Valerio Tenace,Pierre-Emmanuel Gaillardon,Alan Mishchenko,Satrajit Chatterjee +40 more
- 01 Feb 2021
TL;DR: In this article, the authors present learning incompletely-specified functions based on the results of a competition conducted at IWLS 2020, where the goal of the competition was to implement 100 functions given by a set of care minterms for training, while testing the implementation using a subset of validation minterms sampled from the same function.
24
Negative thinking in branch-and-bound: the case of unate covering
Eugene Goldberg,Luca P. Carloni,Tiziano Villa,Robert K. Brayton,Alberto Sangiovanni-Vincentelli +4 more
TL;DR: The motivation is that when searching the space of solutions by a standard branch-and-bound technique, often a good solution is reached quickly and then improved only a few times before the optimum is found: hence, most of the solution space is explored to certify optimality, with no improvement in the cost function.
On using satisfiability-based pruning techniques in covering algorithms
Vasco M. Manquinho,Joao Marques-Silva +1 more
- 01 Jan 2000
TL;DR: Experimental results indicate that the proposed search pruning techniques provide significant performance gains for different classes of instances, particularly the ability to backtrack non-chronologically to exploit the actual formulation of covering problems.
References
Multiple-Valued Minimization for PLA Optimization
TL;DR: Results show that the heuristic algorithm Espresso-MV comes very close to producing optimum solutions for most of the examples, and shows how important multiple-valued minimization can be for PLA optimization.
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On solving covering problems
Olivier Coudert
- 01 Jun 1996
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.
139
Solving covering problems using LPR-based lower bounds
Stan Liao,Srinivas Devadas +1 more
- 13 Jun 1997
TL;DR: It is shown 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.
Encoding problems in logic synthesis
Tiziano Villa
- 01 Jan 1996
TL;DR: Encoding Problems in Logic Synthesis as discussed by the authors ) is a technique for encoding problems in logic synthesis, which can be found in Section 3.1.1] and 3.2.
17
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