Results 1  10
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184
Spectral Partitioning of Random Graphs
, 2001
"... Problems such as bisection, graph coloring, and clique are generally believed hard in the worst case. However, they can be solved if the input data is drawn randomly from a distribution over graphs containing acceptable solutions. In this paper we show that a simple spectral algorithm can solve all ..."
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Cited by 156 (2 self)
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Problems such as bisection, graph coloring, and clique are generally believed hard in the worst case. However, they can be solved if the input data is drawn randomly from a distribution over graphs containing acceptable solutions. In this paper we show that a simple spectral algorithm can solve all three problems above in the average case, as well as a more general problem of partitioning graphs based on edge density. In nearly all cases our approach meets or exceeds previous parameters, while introducing substantial generality. We apply spectral techniques, using foremost the observation that in all of these problems, the expected adjacency matrix is a low rank matrix wherein the structure of the solution is evident.
Composing Mappings among Data Sources
 In VLDB
, 2003
"... Semantic mappings between data sources play a key role in several data sharing architectures. Mappings provide the relationships between data stored in different sources, and therefore enable answering queries that require data from other nodes in a data sharing network. Composing mappings is one of ..."
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Cited by 138 (9 self)
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Semantic mappings between data sources play a key role in several data sharing architectures. Mappings provide the relationships between data stored in different sources, and therefore enable answering queries that require data from other nodes in a data sharing network. Composing mappings is one of the core problems that lies at the heart of several optimization methods in data sharing networks, such as caching frequently traversed paths and redundancy analysis.
Inferring Link Weights using EndtoEnd Measurements
 In ACM SIGCOMM Internet Measurement Workshop
, 2002
"... We describe a novel constraintbased approach to approximate ISP link weights using only endtoend measurements. Common routing protocols such as OSPF and ISIS choose leastcost paths using link weights, so inferred weights provide a simple, concise, and useful model of intradomain routing. Our ap ..."
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Cited by 127 (19 self)
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We describe a novel constraintbased approach to approximate ISP link weights using only endtoend measurements. Common routing protocols such as OSPF and ISIS choose leastcost paths using link weights, so inferred weights provide a simple, concise, and useful model of intradomain routing. Our approach extends routerlevel ISP maps, which include only connectivity, with link weights that are consistent with routing. Our inferred weights agree well with observed routing: while our inferred weights fully characterize the set of shortest paths between 8499% of the routerpairs, alternative models based on hop count and latency do so for only 4781% of the pairs.
Global Optimization Algorithms  Theory and Application
, 2011
"... This ebook is devoted to Global Optimization algorithms, which are methods for finding solutions of high quality for an incredible wide range of problems. We introduce the basic concepts of optimization and discuss features which make optimization problems difficult and thus, should be considered ..."
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Cited by 94 (26 self)
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This ebook is devoted to Global Optimization algorithms, which are methods for finding solutions of high quality for an incredible wide range of problems. We introduce the basic concepts of optimization and discuss features which make optimization problems difficult and thus, should be considered when trying to solve them. In this book, we focus on
Satisfiability Solvers
, 2008
"... The past few years have seen an enormous progress in the performance of Boolean satisfiability (SAT) solvers. Despite the worstcase exponential run time of all known algorithms, satisfiability solvers are increasingly leaving their mark as a generalpurpose tool in areas as diverse as software and h ..."
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Cited by 48 (0 self)
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The past few years have seen an enormous progress in the performance of Boolean satisfiability (SAT) solvers. Despite the worstcase exponential run time of all known algorithms, satisfiability solvers are increasingly leaving their mark as a generalpurpose tool in areas as diverse as software and hardware verification [29–31, 228], automatic test pattern generation [138, 221], planning [129, 197], scheduling [103], and even challenging problems from algebra [238]. Annual SAT competitions have led to the development of dozens of clever implementations of such solvers [e.g. 13,
Model counting: A new strategy for obtaining good bounds
 In 21st AAAI
, 2006
"... Model counting is the classical problem of computing the number of solutions of a given propositional formula. It vastly generalizes the NPcomplete problem of propositional satisfiability, and hence is both highly useful and extremely expensive to solve in practice. We present a new approach to mod ..."
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Cited by 45 (16 self)
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Model counting is the classical problem of computing the number of solutions of a given propositional formula. It vastly generalizes the NPcomplete problem of propositional satisfiability, and hence is both highly useful and extremely expensive to solve in practice. We present a new approach to model counting that is based on adding a carefully chosen number of socalled streamlining constraints to the input formula in order to cut down the size of its solution space in a controlled manner. Each of the additional constraints is a randomly chosen XOR or parity constraint on the problem variables, represented either directly or in the standard CNF form. Inspired by a related yet quite different theoretical study of the properties of XOR constraints, we provide a formal proof that with high probability, the number of XOR constraints added in order to bring the formula to the boundary of being unsatisfiable determines with high precision its model count. Experimentally, we demonstrate that this approach can be used to obtain good bounds on the model counts for formulas that are far beyond the reach of exact counting methods. In fact, we obtain the first nontrivial solution counts for very hard, highly structured combinatorial problem instances. Note that unlike other counting techniques, such as Markov Chain Monte Carlo methods, we are able to provide highconfidence guarantees on the quality of the counts obtained.
From sampling to model counting
 In Proc. IJCAI’07
, 2007
"... We introduce a new technique for counting models of Boolean satisfiability problems. Our approach incorporates information obtained from sampling the solution space. Unlike previous approaches, our method does not require uniform or nearuniform samples. It instead converts local search sampling wit ..."
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Cited by 39 (8 self)
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We introduce a new technique for counting models of Boolean satisfiability problems. Our approach incorporates information obtained from sampling the solution space. Unlike previous approaches, our method does not require uniform or nearuniform samples. It instead converts local search sampling without any guarantees into very good bounds on the model count with guarantees. We give a formal analysis and provide experimental results showing the effectiveness of our approach. 1
Algorithm Selection and Scheduling
"... Abstract. Algorithm portfolios aim to increase the robustness of our ability to solve problems efficiently. While recently proposed algorithm selection methods come ever closer to identifying the most appropriate solver given an input instance, they are bound to make wrong and, at times, costly deci ..."
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Cited by 30 (8 self)
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Abstract. Algorithm portfolios aim to increase the robustness of our ability to solve problems efficiently. While recently proposed algorithm selection methods come ever closer to identifying the most appropriate solver given an input instance, they are bound to make wrong and, at times, costly decisions. Solver scheduling has been proposed to boost the performance of algorithm selection. Scheduling tries to allocate time slots to the given solvers in a portfolio so as to maximize, say, the number of solved instances within a given time limit. We show how to solve the corresponding optimization problem at a low computational cost using column generation, resulting in fast and high quality solutions. We integrate this approach with a recently introduced algorithm selector, which we also extend using other techniques. We propose various static as well as dynamic scheduling strategies, and demonstrate that in comparison to pure algorithm selection, our novel combination of scheduling and solver selection can significantly boost performance. 1
Approximate counting by sampling the backtrackfree search space
 In AAAI
, 2007
"... We present a new estimator for counting the number of solutions of a Boolean satisfiability problem as a part of an importance sampling framework. The estimator uses the recently introduced SampleSearch scheme that is designed to overcome the rejection problem associated with distributions having a ..."
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Cited by 26 (6 self)
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We present a new estimator for counting the number of solutions of a Boolean satisfiability problem as a part of an importance sampling framework. The estimator uses the recently introduced SampleSearch scheme that is designed to overcome the rejection problem associated with distributions having a substantial amount of determinism. We show here that the sampling distribution of SampleSearch can be characterized as the backtrackfree distribution and propose several schemes for its computation. This allows integrating SampleSearch into the importance sampling framework for approximating the number of solutions and also allows using SampleSearch for computing a lower bound measure on the number of solutions. Our empirical evaluation demonstrates the superiority of our new approximate counting schemes against recent competing approaches.
Understanding the power of clause learning
 In: Proceedings of the 18th International Joint Conference on Artificial Intelligence
, 2003
"... Efficient implementations of DPLL with the addition of clause learning are the fastest complete satisfiability solvers and can handle many significant realworld problems, such as verification, planning, and design. Despite its importance, little is known of the ultimate strengths and limitations of ..."
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Cited by 25 (4 self)
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Efficient implementations of DPLL with the addition of clause learning are the fastest complete satisfiability solvers and can handle many significant realworld problems, such as verification, planning, and design. Despite its importance, little is known of the ultimate strengths and limitations of the technique. This paper presents the first precise characterization of clause learning as a proof system, and begins the task of understanding its power. In particular, we show that clause learning using any nonredundant scheme and unlimited restarts is equivalent to general resolution. We also show that without restarts but with a new learning scheme, clause learning can provide exponentially smaller proofs than regular resolution, which itself is known to be much stronger than ordinary DPLL. 1
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