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DCSSAT: a divideandconquer approach to solving stochastic satisfiability problems efficiently
 In AAAI
, 2005
"... We present DCSSAT, a sound and complete divideandconquer algorithm for solving stochastic satisfiability (SSAT) problems that outperforms the best existing algorithm for solving such problems (ZANDER) by several orders of magnitude with respect to both time and space. DCSSAT achieves this perform ..."
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Cited by 2 (0 self)
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We present DCSSAT, a sound and complete divideandconquer algorithm for solving stochastic satisfiability (SSAT) problems that outperforms the best existing algorithm for solving such problems (ZANDER) by several orders of magnitude with respect to both time and space. DCSSAT achieves
A New Method for Solving Hard Satisfiability Problems
 AAAI
, 1992
"... We introduce a greedy local search procedure called GSAT for solving propositional satisfiability problems. Our experiments show that this procedure can be used to solve hard, randomly generated problems that are an order of magnitude larger than those that can be handled by more traditional approac ..."
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Cited by 734 (21 self)
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We introduce a greedy local search procedure called GSAT for solving propositional satisfiability problems. Our experiments show that this procedure can be used to solve hard, randomly generated problems that are an order of magnitude larger than those that can be handled by more traditional
The DivideandConquer Manifesto
 Proceedings of the Eleventh International Conference on Algorithmic Learning Theory
, 2000
"... . Existing machine learning theory and algorithms have focused on learning an unknown function from training examples, where the unknown function maps from a feature vector to one of a small number of classes. Emerging applications in science and industry require learning much more complex funct ..."
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Cited by 4 (0 self)
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applications. These systems all employ some form of divideandconquer, where the inputs and outputs are divided into smaller pieces (e.g., "windows"), classified, and then the results are merged to produce an overall solution. This paper defines the problem of divideandconquer learning
DivideandConquer Methods for Solving MDPs
"... The Markov Decision Process (MDP) is the principal theoretical formalism in the area of Reinforcement Learning (RL). An import from optimal control in operations research, this construct is generic enough to represent problems comprising almost all of AI research, but consequently, it suffers from t ..."
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of the classic divideandconquer approach to problemsolving in computer science – breaking up a large, unwieldy problem into smaller components and solving the parts. This paper reviews most of the methods proposed in the associated literature, weighing their pros and cons, and their applicability
Cut Problems And Their Application To DivideAndConquer
, 1996
"... INTRODUCTION 5.1 One of the most important paradigms in the design and analysis of algorithms is the notion of a divideandconquer algorithm. Every undergraduate course on algorithms teaches this method as one of its staples: to solve a problem quickly, one carefully splits the problem into two s ..."
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Cited by 84 (0 self)
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INTRODUCTION 5.1 One of the most important paradigms in the design and analysis of algorithms is the notion of a divideandconquer algorithm. Every undergraduate course on algorithms teaches this method as one of its staples: to solve a problem quickly, one carefully splits the problem into two
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 1231 (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
Cognitive load during problem solving: effects on learning
 COGNITIVE SCIENCE
, 1988
"... Considerable evidence indicates that domain specific knowledge in the form of schemes is the primary factor distinguishing experts from novices in problemsolving skill. Evidence that conventional problemsolving activity is not effective in schema acquisition is also accumulating. It is suggested t ..."
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Cited by 603 (13 self)
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that a major reason for the ineffectiveness of problem solving as a learning device, is that the cognitive processes required by the two activities overlap insufficiently, and that conventional problem solving in the form of meansends analysis requires a relatively large amount of cognitive processing
The Valuation of Options for Alternative Stochastic Processes
 Journal of Financial Economics
, 1976
"... This paper examines the structure of option valuation problems and develops a new technique for their solution. It also introduces several jump and diffusion processes which have nol been used in previous models. The technique is applied lo these processes to find explicit option valuation formulas, ..."
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Cited by 661 (4 self)
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This paper examines the structure of option valuation problems and develops a new technique for their solution. It also introduces several jump and diffusion processes which have nol been used in previous models. The technique is applied lo these processes to find explicit option valuation formulas
Stochastic Perturbation Theory
, 1988
"... . In this paper classical matrix perturbation theory is approached from a probabilistic point of view. The perturbed quantity is approximated by a firstorder perturbation expansion, in which the perturbation is assumed to be random. This permits the computation of statistics estimating the variatio ..."
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Cited by 886 (35 self)
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. In this paper classical matrix perturbation theory is approached from a probabilistic point of view. The perturbed quantity is approximated by a firstorder perturbation expansion, in which the perturbation is assumed to be random. This permits the computation of statistics estimating
Results 1  10
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