Schuurmans, D., Southey, F., Holte, R.C.: The exponentiated subgradient algorithm for heuristic Boolean programming. In: Proceedings of the International Joint Conference on Artificial Intelligence (IJCAI'01), Seattle, Washington, Morgan Kaufmann (2001) 334--341 Home/Search Document Details and Download
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Scaling and Probabilistic Smoothing: Dynamic Local Search for .. - Tompkins, Hoos (2003) (Correct)
....invokes a smoothing mechanism that decreases all clause 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 ....
....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 to direct the search, ideally away from local minima and toward a global optimum. There are two distinct stages involved ....
D. Schuurmans, F. Southey, and R.C. Holte. The exponentiated subgradient algorithm for heuristic boolean programming. In Proc. IJCAI-01, pp. 334-341, Morgan Kaufmann Publishers, 2001.
Scaling and Probabilistic Smoothing: Efficient Dynamic.. - Hutter, Tompkins, Hoos (2002) (1 citation) (Correct)
....all clause weights by a constant amount. The Smoothed Descent and Flood (SDF) approach [10] introduced a more complex smoothing method, and the concept of multiplicative weight updates. The most recent and best performing DLS algorithm for SAT is the Exponentiated Sub Gradient (ESG) method [11]. ESG, described in more detail in the next section, reaches or exceeds the performance of the best known WalkSAT algorithms in many cases. In this paper we introduce Scaling and Probabilistic Smoothing (SAPS) a new algorithm that is conceptually closely related to ESG, but di ers in the way ....
....RSAPS which illustrate the performance improvements these algorithms achieve as compared to ESG and Novelty . Finally, Section 6 contains conclusions and points out directions for future work. 2 The ESG algorithm The Exponentiated Subgradient (ESG) algorithm by Schuurmans, Southey, and Holte [11] is motivated by established methods in the operations research literature. Subgradient optimisation is a method for minimising Lagrangian functions that is often used for generating good lower bounds for branch and bound techniques or as a heuristic in incomplete local search algorithms. ESG for ....
[Article contains additional citation context not shown here]
D. Schuurmans, F. Southey, and R.C. Holte. The exponentiated subgradient algorithm for heuristic boolean programming. In Proc. IJCAI-01, pp. 334-341, Morgan Kaufmann Publishers, 2001.
Constraint Satisfaction By Local Search - Bohlin (2002) (Correct)
....developed by Selman and Kautz [50] Frank et al. 11] and Wah et al. [53, 64, 65] Dynamic weighting for general constraint satisfaction is used by Galinier and Hao [13] with tabu search. Note that this method has many things in common with the exponentiated subgradient method by Schuurmans et al. [49], described in Section 3.4.1. Modifying the basic cost function C basic with constraint weights one yields the basic weighted cost function Cweight Cweight (v; C(v) basic i c i (v) where is a vector of weights, i being the weight for constraint c i . The notation here for the ....
....fastest for SAT. Contrary to this result, Shang and Wah [53] comes to the conclusion that the weights should be updated only at plateaus and local minima. The weights are either updated additively (as in the articles by Wu et al. 65, 63, 64] or multiplicatively (as done by Schuurmans et al. [48, 49]) Also, to hinder from becoming too unbalanced, some normalization mechanism is necessary. 2.4 Constraint satisfaction using local search During the last ten years, several frameworks for solving constraint satisfaction and optimization problems using local search has been proposed. In this ....
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Schuurmans, D., Southey, F., and Holte, R. C. The exponentiated subgradient algorithm for heuristic boolean programming. In IJCAI (2001), pp. 334-341.
SLS Algorithms for SAT: Irregular Instances, Search Stagnation.. - Hoos (2002) (Correct)
.... For the past decade, various types of stochastic local search (SLS) methods have been applied very successfully to the propositional satisfiability problem (SAT) These include the GSAT and WalkSAT families of algorithms [19, 4, 14] as well as several other algorithms based on similar ideas [7, 20, 21, 16, 17]. GSAT and WalkSAT algorithms have been extensively studied in the literature, and include some of the best performing SAT algorithms known to date [11, 17] Compared to other state of the art SAT algorithms (e.g. Satz [13] these methods are rather simplistic and it is not well understood how ....
.... These include the GSAT and WalkSAT families of algorithms [19, 4, 14] as well as several other algorithms based on similar ideas [7, 20, 21, 16, 17] GSAT and WalkSAT algorithms have been extensively studied in the literature, and include some of the best performing SAT algorithms known to date [11, 17]. Compared to other state of the art SAT algorithms (e.g. Satz [13] these methods are rather simplistic and it is not well understood how they can solve many classes of large and difficult SAT instances surprisingly efficiently. It is also largely unclear under which conditions (i.e. on which ....
D. Schuurmans, F. Southy, and R. Holte. The exponentiated subgradient algorithm for heuristic boolean programming. In Proc. IJCAI-01, 2001.
Learning the Empirical Hardness of Optimization.. - Leyton-Brown.. (2002) (2 citations) (Correct)
....are not empirically hard. We attempted to address the first group of problems in previous work [15] proposing the Combinatorial Auction Test Suite (CATS) Indeed, the CATS distributions have been widely used in the last year, by many of the authors cited above and also for example by [10, 23, 13]. In this paper we consider four CATS distributions: regions, arbitrary, matching, and scheduling. Although the available space does not permit the enumeration of each distribution s parameters, we give a high level description of each distribution. Regions models an auction of real estate, or ....
Dale Schuurmans, Finnegan Southey, and Robert C. Holte. The exponentiated subgradient algorithm for heuristic boolean programming. In IJCAI-01, 2001.
Backbone Guided Dynamic Local Search for Propositional.. - Ferreira Jr. (Correct)
No context found.
Schuurmans, D., Southey, F., Holte, R.C.: The exponentiated subgradient algorithm for heuristic Boolean programming. In: Proceedings of the International Joint Conference on Artificial Intelligence (IJCAI'01), Seattle, Washington, Morgan Kaufmann (2001) 334--341
Additive versus Multiplicative Clause Weighting for SAT - John Thornton And (2004) (Correct)
No context found.
Schuurmans, D.; Southey, F.; and Holte, R. 2001. The exponentiated subgradient algorithm for heuristic Boolean programming. In Proceedings of the Seventeenth International Joint Conference on Artificial Intelligence (IJCAI01) , 334--341.
Warped Landscapes and Random Acts of SAT Solving - Dave Tompkins And (2004) (1 citation) (Correct)
No context found.
Dale Schuurmans, Finnegan Southey, and Robert C. Holte. The exponentiated subgradient algorithm for heuristic boolean programming. In Proceedings of the Seventeenth International Joint Conference on Artificial Intelligence (IJCAI'01), pages 334--341, 2001.
Scaling and Probabilistic Smoothing: Efficient Dynamic.. - Hutter, Tompkins, Hoos (2002) (1 citation) (Correct)
No context found.
D. Schuurmans, F. Southey, and R.C. Holte. The exponentiated subgradient algorithm for heuristic boolean programming. In Proc. IJCAI-01, pp. 334-341, Morgan Kaufmann Publishers, 2001.
Warped Landscapes and Random Acts of SAT Solving - Dave Tompkins And (2004) (1 citation) (Correct)
No context found.
Dale Schuurmans, Finnegan Southey, and Robert C. Holte. The exponentiated subgradient algorithm for heuristic boolean programming. In Proceedings of the Seventeenth International Joint Conference on Artificial Intelligence (IJCAI'01), pages 334--341, 2001.
Learning the Empirical Hardness of Optimization.. - Leyton-Brown.. (2002) (2 citations) (Correct)
No context found.
Dale Schuurmans, Finnegan Southey, and Robert C. Holte. The exponentiated subgradient algorithm for heuristic boolean programming. In IJCAI-01, 2001.
Solving Concisely Expressed Combinatorial Auction Problems - Boutilier (2002) (4 citations) (Correct)
No context found.
Dale Schuurmans, Finnegan Southey, and Robert C. Holte. The exponentiated subgradient algorithm for heuristic boolean programming. In Proceedings of the Seventeenth International Joint Conference on Artificial Intelligence, pages 334--341, Seattle, 2001.
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