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38
Learning the Empirical Hardness of Optimization Problems: The case of combinatorial auctions
 In CP
, 2002
"... We propose a new approach to understanding the algorithmspecific empirical hardness of optimization problems. In this work we focus on the empirical hardness of the winner determination probleman optimization problem arising in combinatorial auctionswhen solved by ILOG's CPLEX software. ..."
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Cited by 78 (23 self)
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We propose a new approach to understanding the algorithmspecific empirical hardness of optimization problems. In this work we focus on the empirical hardness of the winner determination probleman optimization problem arising in combinatorial auctionswhen solved by ILOG's CPLEX software. We consider nine widelyused problem distributions and sample randomly from a continuum of parameter settings for each distribution. First, we contrast the overall empirical hardness of the different distributions. Second, we identify a large number of distributionnonspecific features of data instances and use statistical regression techniques to learn, evaluate and interpret a function from these features to the predicted hardness of an instance.
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 50 (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,
Backbones and backdoors in satisfiability
 In Proceedings of the National Conference on Artificial Intelligence (AAAI
, 2005
"... We study the backbone and the backdoors of propositional satisfiability problems. We make a number of theoretical, algorithmic and experimental contributions. From a theoretical perspective, we prove that backbones are hard even to approximate. From an algorithmic perspective, we present a number of ..."
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Cited by 42 (1 self)
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We study the backbone and the backdoors of propositional satisfiability problems. We make a number of theoretical, algorithmic and experimental contributions. From a theoretical perspective, we prove that backbones are hard even to approximate. From an algorithmic perspective, we present a number of different procedures for computing backdoors. From an empirical perspective, we study the correlation between being in the backbone and in a backdoor. Experiments show that there tends to be very little overlap between backbones and backdoors. We also study problem hardness for the Davis Putnam procedure. Problem hardness appears to be correlated with the size of strong backdoors, and weakly correlated with the size of the backbone, but does not appear to be correlated to the size of weak backdoors nor their number. Finally, to isolate the effect of backdoors, we look at problems with no backbone.
Solving binary constraint satisfaction problems using evolutionary algorithms with an adaptive tness function
 In Eiben et al
"... Abstract. This paper presents a comparative study of Evolutionary Algorithms (EAs) for Constraint Satisfaction Problems (CSPs). We focus on EAs where fitness is based on penalization of constraint violations and the penalties are adapted during the execution. Three different EAs based on this approa ..."
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Cited by 32 (14 self)
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Abstract. This paper presents a comparative study of Evolutionary Algorithms (EAs) for Constraint Satisfaction Problems (CSPs). We focus on EAs where fitness is based on penalization of constraint violations and the penalties are adapted during the execution. Three different EAs based on this approach are implemented. For highly connected constraint networks, the results provide further empirical support to the theoretical prediction of the phase transition in binary CSPs. 1
Empirical Hardness Models: Methodology and a Case Study on Combinatorial Auctions
"... Is it possible to predict how long an algorithm will take to solve a previouslyunseen instance of an NPcomplete problem? If so, what uses can be found for models that make such predictions? This paper provides answers to these questions and evaluates the answers experimentally. We propose the use ..."
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Cited by 26 (9 self)
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Is it possible to predict how long an algorithm will take to solve a previouslyunseen instance of an NPcomplete problem? If so, what uses can be found for models that make such predictions? This paper provides answers to these questions and evaluates the answers experimentally. We propose the use of supervised machine learning to build models that predict an algorithm’s runtime given a problem instance. We discuss the construction of these models and describe techniques for interpreting them to gain understanding of the characteristics that cause instances to be hard or easy. We also present two applications of our models: building algorithm portfolios that outperform their constituent algorithms, and generating test distributions that emphasize hard problems. We demonstrate the effectiveness of our techniques in a case study of the combinatorial auction winner determination problem. Our experimental results show that we can build very accurate models of an algorithm’s running time, interpret our models, build an algorithm portfolio that strongly outperforms the best single algorithm, and tune a standard benchmark suite to generate much harder problem instances.
Problem Difficulty for Tabu Search in JobShop Scheduling
 Artificial Intelligence
, 2002
"... Tabu search algorithms are among the most effective approaches for solving the jobshop scheduling problem (JSP). Yet, we have little understanding of why these algorithms work so well, and under what conditions. We develop a model of problem difficulty for tabu search in the JSP, borrowing from sim ..."
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Cited by 23 (9 self)
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Tabu search algorithms are among the most effective approaches for solving the jobshop scheduling problem (JSP). Yet, we have little understanding of why these algorithms work so well, and under what conditions. We develop a model of problem difficulty for tabu search in the JSP, borrowing from similar models developed for SAT and other NP  complete problems. We show that the mean distance between random local optima and the nearest optimal solution is highly correlated with the cost of locating optimal solutions to typical, random JSPs. Additionally, this model accounts for the cost of locating suboptimal solutions, and provides an explanation for differences in the relative difficulty of square versus rectangular JSPs. We also identify two important limitations of our model. First, model accuracy is inversely correlated with problem difficulty, and is exceptionally poor for rare, very highcost problem instances. Second, the model is significantly less accurate for structured, nonrandom JSPs. Our results are also likely to be useful in future research on difficulty models of local search in SAT, as local search cost in both SAT and the JSP is largely dictated by the same search space features. Similarly, our research represents the first attempt to quantitatively model the cost of tabu search for any NP complete problem, and may possibly be leveraged in an effort to understand tabu search in problems other than jobshop scheduling.
The backdoor key: A path to understanding problem hardness
 In AAAI
, 2004
"... We introduce our work on the backdoor key, a concept that shows promise for characterizing problem hardness in backtracking search algorithms. The general notion of backdoors was recently introduced to explain the source of heavytailed behaviors in backtracking algorithms (Williams, Gomes, & S ..."
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Cited by 21 (0 self)
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We introduce our work on the backdoor key, a concept that shows promise for characterizing problem hardness in backtracking search algorithms. The general notion of backdoors was recently introduced to explain the source of heavytailed behaviors in backtracking algorithms (Williams, Gomes, & Selman 2003a; 2003b). We describe empirical studies that show that the key faction,i.e., the ratio of the key size to the corresponding backdoor size, is a good predictor of problem hardness of ensembles and individual instances within an ensemble for structure domains with large key fraction.
The backbone of the travelling salesperson
 IN: PROCEEDINGS OF THE 19TH INTERNATIONAL JOINT CONFERENCE ON ARTIFICIAL INTELLIGENCE (IJCAI’05
"... We study the backbone of the travelling salesperson optimization problem. We prove that it is intractable to approximate the backbone with any performance guarantee, assuming that P!=NP and there is a limit on the number of edges falsely returned. Nevertheless, in practice, it appears that much of t ..."
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Cited by 15 (0 self)
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We study the backbone of the travelling salesperson optimization problem. We prove that it is intractable to approximate the backbone with any performance guarantee, assuming that P!=NP and there is a limit on the number of edges falsely returned. Nevertheless, in practice, it appears that much of the backbone is present in close to optimal solutions. We can therefore often find much of the backbone using approximation methods based on good heuristics. We demonstrate that such backbone information can be used to guide the search for an optimal solution. However, the variance in runtimes when using a backbone guided heuristic is large. This suggests that we may need to combine such heuristics with randomization and restarts. In addition, though backbone guided heuristics are useful for finding optimal solutions, they are less help in proving optimality.
Phase Transitions and Backbones of the Asymmetric Traveling Salesman Problem
 Journal of Artificial Intelligence Research
, 2004
"... In recent years, there has been much interest in phase transitions of combinatorial problems. ..."
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Cited by 13 (2 self)
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In recent years, there has been much interest in phase transitions of combinatorial problems.