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Anytime AND/OR Depth-First Search for Combinatorial Optimization [chapter]

Lars Otten, Rina Dechter
2014 Lecture Notes in Computer Science  
One popular and efficient scheme for solving exactly combinatorial optimization problems over graphical models is depth-first Branch and Bound.  ...  However, when the algorithm exploits problem decomposition using AND/OR search spaces, its anytime behavior breaks down.  ...  Anytime versus AND/OR We will use AOBB to denote the algorithm above in its specific graphical models context as well as a generic name for any depth-first Branch and Bound scheme over an AND/OR search  ... 
doi:10.1007/978-3-319-10428-7_70 fatcat:zwxaf7ik4rhszg77vookg5437a

Anytime graph matching

Zeina Abu-Aisheh, Romain Raveaux, Jean-Yves Ramel
2016 Pattern Recognition Letters  
It finds the first suboptimal solution quickly, and then keeps on searching for a list of improved solutions. The algorithm is well suited for conversion into an anytime algorithm.  ...  We describe how to convert an error-tolerant GM method into an anytime one. A depth-first GM method has been recently proposed in the literature.  ...  The reason for having chosen such datasets is to have a variety of graph attributes (i.e., numeric and/or symbolic attributes on vertices and/or edges or non-attributed vertices and/or edges) and densities  ... 
doi:10.1016/j.patrec.2016.10.004 fatcat:fvnej4veizfxbmyb4ghdxzi2zi

Anytime pack search

Satya Gautam Vadlamudi, Sandip Aine, Partha Pratim Chakrabarti
2015 Natural Computing  
Furthermore, we present a parallel formulation of the proposed algorithm, called Parallel Anytime Pack Search (PAPS), which is applicable for searching tree state-spaces.  ...  in planning and optimization.  ...  Acknowledgment Partha Pratim Chakrabarti acknowledges Department of Science and Technology (DST), India for partial support of the work.  ... 
doi:10.1007/s11047-015-9490-9 fatcat:65mcio7x2zej5aqx3cl535agcu

Stochastic Anytime Search for Bounding Marginal MAP

Radu Marinescu, Rina Dechter, Alexander Ihler
2018 Proceedings of the Twenty-Seventh International Joint Conference on Artificial Intelligence  
In this paper, we develop new search-based bounding schemes for Marginal MAP that produce anytime upper and lower bounds without performing exact likelihood computations.  ...  For this reason, most recent state-of-the-art solvers that focus on computing anytime upper and lower bounds on the optimal value are limited to solving instances with tractable conditioned summation subproblems  ...  We are grateful to the reviewers for their helpful comments.  ... 
doi:10.24963/ijcai.2018/704 dblp:conf/ijcai/0002DI18 fatcat:xnx47pkymrg37fqbktpqx34yie

A complete anytime algorithm for number partitioning

Richard E. Korf
1998 Artificial Intelligence  
For numbers with greater precision, CKK first returns the Karmarkx-Karp solution, then continues to find better solutions as time allows.  ...  For numbers with twelve significant digits or less, CKK can optimally solve twoway partitioning problems of arbitrary size in practice.  ...  Thanks to Wheeler, Ken Boese, Alex Fukunaga and Andrew Kahng for helpful discussions concerning this research, and to Pierre Hasenfratz for comments on an earlier draft.  ... 
doi:10.1016/s0004-3702(98)00086-1 fatcat:5bvt6n76nbf57mektzyo4cltfa

AND/OR Search for Marginal MAP

Radu Marinescu, Junkyu Lee, Rina Dechter, Alexander Ihler
2018 The Journal of Artificial Intelligence Research  
Specifically, we explore the complementary properties of best-first search for reducing the number of conditional sums and providing time-improving upper bounds, with depth-first search for rapidly generating  ...  We show empirically that a class of solvers that interleaves depth-first with best-first schemes emerges as the most competitive anytime scheme.  ...  Weighted Best-First AND/OR Search Weighted best-first AND/OR search was introduced recently as an effective alternative to anytime depth-first search schemes for pure MAP tasks (Flerova et al., 2017)  ... 
doi:10.1613/jair.1.11265 fatcat:e4jncyaggncxpo42sgrwz7tpaq

Anytime learning for the NoSLLiP tracker

Karel Zimmermann, Tomáš Svoboda, Jiří Matas
2009 Image and Vision Computing  
We propose an anytime learning procedure for the Sequence of Learned Linear Predictors (SLLiP) tracker.  ...  Since learning might be time-consuming for large problems, we present an anytime learning algorithm which, after a very short initialization period, provides a solution with defined precision.  ...  Anytime learning of SLLiP We define learning as a search for the optimal SLLiP subject to a predefined prediction error (Definition 6).  ... 
doi:10.1016/j.imavis.2009.03.005 fatcat:rv4ahpksxbcbxg2wpbfa5d6f5y

Anytime approximation in probabilistic databases

Robert Fink, Jiewen Huang, Dan Olteanu
2013 The VLDB journal  
This article describes an approximation algorithm for computing the probability of propositional formulas over discrete random variables.  ...  Acknowledgments We would like to thank the anonymous reviewers and Peter Haas for their insightful comments that helped improve this article.  ...  We also thank Christoph Koch and Swaroop Rath for their collaboration on earlier work on which this article is partially based. Jiewen Huang's work was done while at Oxford.  ... 
doi:10.1007/s00778-013-0310-5 fatcat:lzweb674avfcphps7jamzomw2q

Anytime Decision Making Based on Unconstrained Influence Diagrams

Manuel Luque, Thomas D. Nielsen, Finn V. Jensen
2015 International Journal of Intelligent Systems  
This paper addresses this problem by proposing an anytime algorithm that at any time provides a qualified recommendation for the first decisions of the problem.  ...  Thus, when solving an unconstrained influence diagram we not only look for an optimal policy for each decision, but also for a so-called step-policy specifying the next decision given the observations  ...  Acknowledgments The first author has been supported by the Department of Education of the autonomous region of Madrid and the European Social Fund (ESF).  ... 
doi:10.1002/int.21780 fatcat:brpbrp7rwrctpoc32575ynkrmu

Anytime Hybrid Best-First Search with Tree Decomposition for Weighted CSP [chapter]

David Allouche, Simon de Givry, George Katsirelos, Thomas Schiex, Matthias Zytnicki
2015 Lecture Notes in Computer Science  
We propose Hybrid Best First Search (HBFS), a search strategy for optimization problems that combines Best First Search (BFS) and Depth First Search (DFS).  ...  Hence, it provides feedback on the progress of search and solution quality in the form of an optimality gap.  ...  Acknowledgements We are grateful to the Genotoul (Toulouse) Bioinformatic platform for providing us computational support for this work.  ... 
doi:10.1007/978-3-319-23219-5_2 fatcat:ddtrz6o77fasje2n2xiadt3kdq

Anytimeness avoids parameters in detecting closed convex polygons

Michael Zillich, Markus Vincze
2008 2008 IEEE Computer Society Conference on Computer Vision and Pattern Recognition Workshops  
longer, for finding closed convex polygons eliminates the need for parameter tuning.  ...  Especially certain thresholds often seem unavoidable to limit search spaces in order to obtain reasonable runtime complexity.  ...  A measure of affinity between pairs of lines is based on the quality of intersections and the uncertainty of endpoints and guides a depth-first search.  ... 
doi:10.1109/cvprw.2008.4562981 dblp:conf/cvpr/ZillichV08 fatcat:wtrv72dxm5gjbgm7d34sctblwa

Anytime Coalition Structure Generation with Worst Case Guarantees [article]

Tuomas Sandholm, Kate Larson, Martin Andersson, Onn Shehory, Fernando Tohme
1998 arXiv   pre-print
One would prefer a coalition structure that maximizes the sum of the values of the coalitions, but often the number of coalition structures is too large to allow exhaustive search for the optimal one.  ...  If additional time remains, our anytime algorithm searches further, and establishes a progressively lower tight bound. Surprisingly, just searching one more node drops the bound in half.  ...  This paper discusses coalition structure generation in settings where there are too many coalition structures to enumerate and evaluate due to, for example, costly or bounded computation and/or limited  ... 
arXiv:cs/9810005v1 fatcat:qwyyoabdfvefxdpippwcy23llq

TAIP: an anytime algorithm for allocating student teams to internship programs [article]

Athina Georgara, Carles Sierra, Juan A. Rodríguez-Aguilar
2020 arXiv   pre-print
First we provide a formalization of the Team Allocation for Internship Programs Problem, and show the computational hardness of solving it optimally.  ...  Moreover, we conduct a systematic evaluation to show that TAIP reaches optimality, and outperforms CPLEX in terms of time.  ...  Thereafter, we iteratively improve the current team assignment either (i) by employing crossovers of students between two programs, and/or (ii) by swapping assigned students with available ones if they  ... 
arXiv:2005.09331v1 fatcat:gjlbnve64ja6daj4sad2lvd7lm

An anytime tree search algorithm for two-dimensional two- and three-staged guillotine packing problems [article]

Florian Fontan, Luc Libralesso
2020 arXiv   pre-print
[libralesso_anytime_2020] proposed an anytime tree search algorithm for the 2018 ROADEF/EURO challenge glass cutting problem (https://www.roadef.org/challenge/2018/en/index.php).  ...  The resulting program was ranked first among 64 participants.  ...  Pureza, A heuristic approach based on dynamic programming and and/or-graph search for the constrained two-dimensional guillotine cutting problem, Annals of Operations Research 179 (1) (2010) 297-315, ISSN  ... 
arXiv:2004.02603v2 fatcat:2jq3glvgqbbsdk63i5lflrrf3q

4D trajectory planning in ATM with an anytime stochastic approach

José Antonio Cobano, David Alejo, Guillermo Heredia, Aníbal Ollero
2013 Proceedings of the 3rd International Conference on Application and Theory of Automation in Command and Control Systems - ATACCS '13  
Moreover, an anytime approach using PSO is applied because determining optimal trajectories with short time intervals in the flight phase is not feasible.  ...  technique named Particle Swarm Optimization (PSO) to modify the 4D initial trajectories of the aircraft with an overall minimum cost.  ...  In first place, the swarm is initialized by assigning random initial locations and velocities. A uniform distribution in the search space has been chosen for this step.  ... 
doi:10.1145/2494493.2494494 dblp:conf/atacss/CobanoAHO13 fatcat:f5sianv5pfhgnoo7of5cu5uilq
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