F. Hutter, D. Tompkins, and H. Hoos. Scaling and probabilistic smoothing: Efficient dynamic local search for SAT. In CP '02: Principles and Practice of Constraint Programming, pages 233--248. Springer Verlag, 2002. Functions for use in Contention Heuristics InUnsatisfied Move  Home/Search Document Details and Download
Summary Related Articles Check |
This paper is cited in the following contexts:
Warped Landscapes and Random Acts of SAT Solving - Dave Tompkins And (2004) (1 citation) Self-citation (Tompkins Hoos) (Correct)
No context found.
Frank Hutter, Dave A.D. Tompkins, and Holger H. Hoos. Scaling and probabilistic smoothing: Efficient dynamic local search for SAT. In LNCS 2470: Proceedings of the Eighth International Conference on Principles and Practice of Constraint Programming, pages 233--248. Springer Verlag, 2002.
Warped Landscapes and Random Acts of SAT Solving - Dave Tompkins And (2004) (1 citation) Self-citation (Tompkins Hoos) (Correct)
No context found.
Frank Hutter, Dave A.D. Tompkins, and Holger H. Hoos. Scaling and probabilistic smoothing: Efficient dynamic local search for SAT. In LNCS 2470: Proceedings of the Eighth International Conference on Principles and Practice of Constraint Programming, pages 233--248. Springer Verlag, 2002.
Scaling and Probabilistic Smoothing: Dynamic Local Search for .. - Tompkins, Hoos (2003) Self-citation (Tompkins Hoos) (Correct)
....penalties by a constant amount. The Smoothed Descent and Flood (SDF) approach [11] introduced a more complex smoothing method, and the concept of multiplicative penalty updates, which evolved into the Exponentiated Sub Gradient (ESG) method [12] Our Scaling and Probabilistic Smoothing (SAPS) [7] method improved upon the ESG approach; SAPS will be described in detail in Section 2. With the SAT problem, both complete solvers and SLS solvers have had a large amount of success. However, complete solvers often have difficulty with MAX SAT problems, whereas SLS methods have been extremely ....
....In Section 4 we discuss how SAPS can be extended from unweighted to weighted MAX SAT. Finally, Section 5 contains conclusions and points out directions for future work. 2 Scaling and Probabilistic Smoothing In this section, we describe the Scaling and Probabilistic Smoothing (SAPS) algorithm [7]. SAPS is a Dynamic Local Search (DLS) algorithm, developed as a variant of the ESG algorithm of Schuurmans et al. 12] Like most DLS algorithms, SAPS associates a clause penalty with each clause , which is dynamically changed throughout the search process. The clause penalties help ....
[Article contains additional citation context not shown here]
F. Hutter, D.A.D. Tompkins, and H.H. Hoos. Scaling and Probabilistic Smoothing: Efficient Dynamic Local Search for SAT. In LNCS 2470:Proc. CP-02, pp. 233--248, Springer Verlag, 2002.
Evolving Algorithms for Constraint Satisfaction - Bain, Thornton, Sattar (2004) (Correct)
No context found.
F. Hutter, D. Tompkins, and H. Hoos. Scaling and probabilistic smoothing: Efficient dynamic local search for SAT. In CP '02: Principles and Practice of Constraint Programming, pages 233--248. Springer Verlag, 2002. Functions for use in Contention Heuristics InUnsatisfied Move
Online articles have much greater impact More about CiteSeer.IST Add search form to your site Submit documents Feedback
CiteSeer.IST - Copyright Penn State and NEC