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Relaxed Survey Propagation for The Weighted Maximum Satisfiability Problem
"... The survey propagation (SP) algorithm has been shown to work well on large instances of the random 3SAT problem near its phase transition. It was shown that SP estimates marginals over covers that represent clusters of solutions. The SPy algorithm generalizes SP to work on the maximum satisfiabili ..."
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Cited by 1 (0 self)
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satisfiability (MaxSAT) problem, but the cover interpretation of SP does not generalize to SPy. In this paper, we formulate the relaxed survey propagation (RSP) algorithm, which extends the SP algorithm to apply to the weighted MaxSAT problem. We show that RSP has an interpretation of estimating marginals
Backbone Guided Local Search for the Weighted Maximum Satisfiability Problem
"... The Satisfiability problem (SAT) is a famous NPComplete problem, which consists of an assignment of Boolean variables (true or false) and some clauses formed of these variables. A clause is a disjunction of some Boolean literals and can be true if and only if any of them is true. A SAT instance is ..."
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The Satisfiability problem (SAT) is a famous NPComplete problem, which consists of an assignment of Boolean variables (true or false) and some clauses formed of these variables. A clause is a disjunction of some Boolean literals and can be true if and only if any of them is true. A SAT instance
Improved Approximation Algorithms for Maximum Cut and Satisfiability Problems Using Semidefinite Programming
 Journal of the ACM
, 1995
"... We present randomized approximation algorithms for the maximum cut (MAX CUT) and maximum 2satisfiability (MAX 2SAT) problems that always deliver solutions of expected value at least .87856 times the optimal value. These algorithms use a simple and elegant technique that randomly rounds the solution ..."
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Cited by 1211 (13 self)
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We present randomized approximation algorithms for the maximum cut (MAX CUT) and maximum 2satisfiability (MAX 2SAT) problems that always deliver solutions of expected value at least .87856 times the optimal value. These algorithms use a simple and elegant technique that randomly rounds
Proof verification and hardness of approximation problems
 IN PROC. 33RD ANN. IEEE SYMP. ON FOUND. OF COMP. SCI
, 1992
"... We show that every language in NP has a probablistic verifier that checks membership proofs for it using logarithmic number of random bits and by examining a constant number of bits in the proof. If a string is in the language, then there exists a proof such that the verifier accepts with probabilit ..."
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Cited by 797 (39 self)
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vertex cover, maximum satisfiability, maximum cut, metric TSP, Steiner trees and shortest superstring. We also improve upon the clique hardness results of Feige, Goldwasser, Lovász, Safra and Szegedy [42], and Arora and Safra [6] and shows that there exists a positive ɛ such that approximating
Theoretical improvements in algorithmic efficiency for network flow problems

, 1972
"... This paper presents new algorithms for the maximum flow problem, the Hitchcock transportation problem, and the general minimumcost flow problem. Upper bounds on ... the numbers of steps in these algorithms are derived, and are shown to compale favorably with upper bounds on the numbers of steps req ..."
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Cited by 560 (0 self)
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This paper presents new algorithms for the maximum flow problem, the Hitchcock transportation problem, and the general minimumcost flow problem. Upper bounds on ... the numbers of steps in these algorithms are derived, and are shown to compale favorably with upper bounds on the numbers of steps
Additive Logistic Regression: a Statistical View of Boosting
 Annals of Statistics
, 1998
"... Boosting (Freund & Schapire 1996, Schapire & Singer 1998) is one of the most important recent developments in classification methodology. The performance of many classification algorithms can often be dramatically improved by sequentially applying them to reweighted versions of the input dat ..."
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Cited by 1750 (25 self)
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data, and taking a weighted majority vote of the sequence of classifiers thereby produced. We show that this seemingly mysterious phenomenon can be understood in terms of well known statistical principles, namely additive modeling and maximum likelihood. For the twoclass problem, boosting can
A general approximation technique for constrained forest problems
 SIAM J. COMPUT.
, 1995
"... We present a general approximation technique for a large class of graph problems. Our technique mostly applies to problems of covering, at minimum cost, the vertices of a graph with trees, cycles, or paths satisfying certain requirements. In particular, many basic combinatorial optimization proble ..."
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Cited by 414 (21 self)
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We present a general approximation technique for a large class of graph problems. Our technique mostly applies to problems of covering, at minimum cost, the vertices of a graph with trees, cycles, or paths satisfying certain requirements. In particular, many basic combinatorial optimization
Sparse Message Passing Algorithms for Weighted Maximum Satisfiability
"... Weighted maximum satisfiability is a wellstudied problem that has important applicability to artificial intelligence (for instance, MAP inference in Bayesian networks). Generalpurpose stochastic search algorithms have proven to be accurate and efficient for large problem instances; however, these a ..."
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Cited by 3 (0 self)
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Weighted maximum satisfiability is a wellstudied problem that has important applicability to artificial intelligence (for instance, MAP inference in Bayesian networks). Generalpurpose stochastic search algorithms have proven to be accurate and efficient for large problem instances; however
Robust object recognition with cortexlike mechanisms
 IEEE Trans. Pattern Analysis and Machine Intelligence
, 2007
"... Abstract—We introduce a new general framework for the recognition of complex visual scenes, which is motivated by biology: We describe a hierarchical system that closely follows the organization of visual cortex and builds an increasingly complex and invariant feature representation by alternating b ..."
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Cited by 389 (47 self)
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between a template matching and a maximum pooling operation. We demonstrate the strength of the approach on a range of recognition tasks: From invariant single object recognition in clutter to multiclass categorization problems and complex scene understanding tasks that rely on the recognition of both
Results 1  10
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