Results 1  10
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15
An online algorithm for maximizing submodular functions
, 2007
"... We present an algorithm for solving a broad class of online resource allocation jobs arrive one at a time, and one can complete the jobs by investing time in a number of abstract activities, according to some schedule. We assume that the fraction of jobs completed by a schedule is a monotone, submod ..."
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Cited by 59 (12 self)
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We present an algorithm for solving a broad class of online resource allocation jobs arrive one at a time, and one can complete the jobs by investing time in a number of abstract activities, according to some schedule. We assume that the fraction of jobs completed by a schedule is a monotone, submodular function of a set of pairs (v, τ), where τ is the time invested in activity v. Under this assumption, our online algorithm performs nearoptimally according to two natural metrics: (i) the fraction of jobs completed within time T, for some fixed deadline T> 0, and (ii) the average time required to complete each job. We evaluate our algorithm experimentally by using it to learn, online, a schedule for allocating CPU time among solvers entered in the 2007 SAT solver competition. 1
A selfadaptive multiengine solver for quantified Boolean formulas
"... In this paper we study the problem of engineering a robust solver for quantified Boolean formulas (QBFs), i.e., a tool that can efficiently solve formulas across different problem domains without the need for domainspecific tuning. The paper presents two main empirical results along this line of re ..."
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Cited by 28 (8 self)
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In this paper we study the problem of engineering a robust solver for quantified Boolean formulas (QBFs), i.e., a tool that can efficiently solve formulas across different problem domains without the need for domainspecific tuning. The paper presents two main empirical results along this line of research. Our first result is the development of a multiengine solver, i.e., a tool that selects among its reasoning engines the one which is more likely to yield optimal results. In particular, we show that syntactic QBF features can be correlated to the performances of existing QBF engines across a variety of domains. We also show how a multiengine solver can be obtained by carefully picking stateoftheart QBF solvers as basic engines, and by harnessing inductive reasoning techniques to learn engineselection policies. Our second result is the improvement of our multiengine solver with the capability of updating the learned policies when they fail to give good predictions. In this way the solver becomes also selfadaptive, i.e., able to adjust its internal models when the usage scenario changes substantially. The rewarding results obtained in our experiments show that our solver AQME – Adaptive QBF MultiEngine – can be more robust and efficient than stateoftheart singleengine solvers, even when it is confronted with previously uncharted formulas and competitors. 1
Algorithm Selection for Combinatorial Search Problems: A Survey
, 2012
"... The Algorithm Selection Problem is concerned with selecting the best algorithm to solve a given problem on a casebycase basis. It has become especially relevant in the last decade, as researchers are increasingly investigating how to identify the most suitable existing algorithm for solving a prob ..."
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Cited by 20 (5 self)
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The Algorithm Selection Problem is concerned with selecting the best algorithm to solve a given problem on a casebycase basis. It has become especially relevant in the last decade, as researchers are increasingly investigating how to identify the most suitable existing algorithm for solving a problem instead of developing new algorithms. This survey presents an overview of this work focusing on the contributions made in the area of combinatorial search problems, where Algorithm Selection techniques have achieved significant performance improvements. We unify and organise the vast literature according to criteria that determine Algorithm Selection systems in practice. The comprehensive classification of approaches identifies and analyses the different directions from which Algorithm Selection has been approached. This paper contrasts and compares different methods for solving the problem as well as ways of using these solutions. It closes by identifying directions of current and future research.
Submodular Function Maximization
, 2012
"... Submodularity is a property of set functions with deep theoretical consequences and far–reaching applications. At first glance it appears very similar to concavity, in other ways it resembles convexity. It appears in a wide variety of applications: in Computer Science it has recently been identifie ..."
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Cited by 19 (5 self)
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Submodularity is a property of set functions with deep theoretical consequences and far–reaching applications. At first glance it appears very similar to concavity, in other ways it resembles convexity. It appears in a wide variety of applications: in Computer Science it has recently been identified and utilized in domains such as viral marketing (Kempe et al., 2003), information gathering (Krause and Guestrin, 2007), image segmentation (Boykov and
New Techniques for Algorithm Portfolio Design
"... We present and evaluate new techniques for designing algorithm portfolios. In our view, the problem has both a scheduling aspect and a machine learning aspect. Prior work has largely addressed one of the two aspects in isolation. Building on recent work on the scheduling aspect of the problem, we pr ..."
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Cited by 11 (0 self)
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We present and evaluate new techniques for designing algorithm portfolios. In our view, the problem has both a scheduling aspect and a machine learning aspect. Prior work has largely addressed one of the two aspects in isolation. Building on recent work on the scheduling aspect of the problem, we present a technique that addresses both aspects simultaneously and has attractive theoretical guarantees. Experimentally, we show that this technique can be used to improve the performance of stateoftheart algorithms for Boolean satisfiability, zeroone integer programming, and A.I. planning. 1
Using Online Algorithms to Solve NPHard Problems More Efficiently in Practice
, 2007
"... as representing the official policies of the U.S. Government. ..."
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Cited by 6 (2 self)
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as representing the official policies of the U.S. Government.
Strategies for solving SAT in grids by randomized search
 IN: 9TH INTERNATIONAL CONFERENCE ON ARTIFICIAL INTELLIGENCE AND SYMBOLIC COMPUTATION (AISC
, 2008
"... Grid computing offers a promising approach to solving challenging computational problems in an environment consisting of a large number of easily accessible resources. In this paper we develop strategies for solving collections of hard instances of the propositional satisfiability problem (SAT) wit ..."
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Cited by 6 (4 self)
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Grid computing offers a promising approach to solving challenging computational problems in an environment consisting of a large number of easily accessible resources. In this paper we develop strategies for solving collections of hard instances of the propositional satisfiability problem (SAT) with a randomized SAT solver run in a Grid. We study alternative strategies by using a simulation framework which is composed of (i) a grid model capturing the communication and management delays, and (ii) runtime distributions of a randomized solver, obtained by running a stateoftheart SAT solver on a collection of hard instances. The results are experimentally validated in a production level Grid. When solving a single hard SAT instance, the results show that in practice only a relatively small amount of parallelism can be efficiently used; the speedup obtained by increasing parallelism thereafter is negligible. This observation leads to a novel strategy of using grid to solve collections of hard instances. Instead of solving instances onebyone, the strategy aims at decreasing the overall solution time by applying an alternating distribution schedule.
Learning Algorithm Portfolios for Parallel Execution
 IN PROC. OF LION
"... Portfoliobased solvers are both effective and robust, but their promise for parallel execution with constraint satisfaction solvers has received relatively little attention. This paper proposes an approach that constructs algorithm portfolios intended for parallel execution based on a combination ..."
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Cited by 4 (2 self)
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Portfoliobased solvers are both effective and robust, but their promise for parallel execution with constraint satisfaction solvers has received relatively little attention. This paper proposes an approach that constructs algorithm portfolios intended for parallel execution based on a combination of casebased reasoning, a greedy algorithm, and three heuristics. Empirical results show that this method is efficient, and can significantly improve performance with only a few additional processors. On problems from solver competitions, the resultant algorithm portfolios perform nearly as well as an oracle.
On Heavytailed Runtimes and Restarts in Rapidlyexploring Random Trees
, 2008
"... Randomized, samplingbased planning has a long history of success, and although the benefits associated with this use of randomization are widelyrecognized, its costs are not wellunderstood. We examine a variety of problem instances solved with the Rapidlyexploring Random Tree algorithm, demonstr ..."
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Cited by 4 (0 self)
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Randomized, samplingbased planning has a long history of success, and although the benefits associated with this use of randomization are widelyrecognized, its costs are not wellunderstood. We examine a variety of problem instances solved with the Rapidlyexploring Random Tree algorithm, demonstrating that heavytailed runtime distributions are both common and potentially exploitable. We show that runtime mean and variability can be reduced simultaneously by a straightforward strategy such as restarts and that such a strategy can apply broadly across sets of queries. Our experimental results indicate that severalfold improvements can be achieved in the mean and variance for restrictive problem environments.
PORTFOLIOS WITH DEADLINES FOR BACKTRACKING SEARCH
 INTERNATIONAL JOURNAL ON ARTIFICIAL INTELLIGENCE TOOLS
, 2008
"... Backtracking search is often the method of choice for solving constraint satisfaction and propositional satisfiability problems. Previous studies have shown that portfolios of backtracking algorithms—a selection of one or more algorithms plus a schedule for executing the algorithms—can dramatically ..."
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Cited by 4 (0 self)
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Backtracking search is often the method of choice for solving constraint satisfaction and propositional satisfiability problems. Previous studies have shown that portfolios of backtracking algorithms—a selection of one or more algorithms plus a schedule for executing the algorithms—can dramatically improve performance on some instances. In this paper, we consider a setting that often arises in practice where the instances to be solved arise over time, the instances all belong to some class of problem instances, and a limit or deadline is placed on the computational resources that can be consumed in solving any instance. For such a scenario, we present a simple scheme for learning a good portfolio of backtracking algorithms from a small sample of instances. We demonstrate the effectiveness of our approach through an extensive empirical evaluation using two testbeds: realworld instruction scheduling problems and the widely used quasigroup completion problems.