Hamadi Youssef (93 results)

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Condition: Good+. Hardcover, [xxi], 377 pages. Good+ condition. Size 10"x7". "Classical computer science textbooks tell us that some problems are 'hard'. Yet many areas, from machine learning and computer vision to theorem proving and software verification, have defined their own set of tools for effectively solving complex problems. Tractability provides an overview of these different techniques, and of the fundamental concepts and properties used to tame intractability. This book will help you understand what to do when facing a hard computational problem. Can the problem be modelled by convex, or submodular functions? Will the instances arising in practice be of low treewidth, or exhibit another specific graph structure that makes them easy? Is it acceptable to use scalable, but approximate algorithms? A wide range of approaches is presented through self-contained chapters written by authoritative researchers on each topic." Book has moderate shelfwear, boards are bumped on extremities. Interior text is Near Fine, clean and unmarked.

Hardcover. Condition: Very Good. Tractability: Practical Approaches to Hard Problems This book is in very good condition and will be shipped within 24 hours of ordering. The cover may have some limited signs of wear but the pages are clean, intact and the spine remains undamaged. This book has clearly been well maintained and looked after thus far. Money back guarantee if you are not satisfied. See all our books here, order more than 1 book and get discounted shipping.

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Hardcover. Condition: Very Good. Shipped within 24 hours from our UK warehouse. Clean, undamaged book with no damage to pages and minimal wear to the cover. Spine still tight, in very good condition. Remember if you are not happy, you are covered by our 100% money back guarantee.

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Paperback or Softback. Condition: New. Combinatorial Search: From Algorithms to Systems. Book.

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Hardcover. Condition: Good. Hardcover, xv + 305 pages, NOT ex-library. Front blank endpaper stuck to the front board; neatly removed title page. Book is clean and bright with unmarked text, free of inscriptions and stamps, firmly bound. Issued without a dust jacket. -- Contents: 1 An Introduction to Autonomous Search [What Is an Autonomous Solver? (Architecture & Configuration of the Solver); Outline and Overview of the Book; Guideline for Readers]; Part I Off-line Configuration 2 Evolutionary Algorithm Parameters and Methods to Tune Them [Background and Objectives; Evolutionary Algorithms, Parameters, Algorithm Instances; Algorithm Design and Parameter Tuning; Utility, Algorithm Performance, Test Functions; Algorithmic Approaches to Parameter Tuning; Successful Case Studies on Tuning Evolutionary Algorithms; Considerations for Tuning EAs; Conclusions and Outlook] 3 Automated Algorithm Configuration and Parameter lining [Racing Procedures ; ParamlLS; Sequential Model-Based Optimisation; Other Approaches; Conclusions and Future Work] 4 Case-Based Reasoning for Autonomous Constraint Solving [Case-Based Reasoning; Case-Based Reasoning and Search; CPHYDRA: A Case-Based Portfolio Constraint Solver; Concluding Remarks] 5 Learning a Mixture of Search Heuristics [Machine Learning and Mixtures of Experts; Constraint Satisfaction and Heuristic Search; Search with More than One Heuristic; ACE; Techniques that Improve Learning; Results; Conclusions and Future Work]; Part II On-line Control 6 An Investigation of Reinforcement Learning for Reactive Search Optimization [Reinforcement Learning for Optimization; Reinforcement Learning and Dynamic Programming Basics; Reactive SAT/MAX-SAT Solvers; RL-Based Approach for Reactive SAT/MAX-SAT Solvers; Experimental Results] 7 Adaptive Operator Selection and Management in Evolutionary Algorithms [Parameter Setting in Evolutionary Algorithms; Adaptive Operator Selection; Adaptive Operator Management] 8 Parameter Adaptation in Ant Colony Optimization [Ant Colony Optimization; Overview of Parameter Adaptation Approaches; Parameter Adaptation in ACO; Experimental Investigation of Fixed Parameter Settings; Prescheduled Parameter Variation for MMAS]; Part III New Directions and Applications 9 Continuous Search in Constraint Programming [Background and Notations; Continuous Search in Constraint Programming; Dynamic Continuous Search; Experimental Validation; Related Works; Discussion and Perspectives] 10 Control-Based Clause Sharing in Parallel SAT Solving [Introduction; Previous Works; Technical Background; Control-Based Clause Sharing in Parallel SAT Solving; Evaluation; Conclusion] 11 Learning Feature-Based Heuristic Functions [Introduction; Search Framework; Learning Heuristic Functions; Feature Combination as a Linear Program; Approximation Bounds; Empirical Results; Conclusion; References].

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Condition: New. pp. 540.

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Hardcover. Condition: Brand New. 2013 edition. 139 pages. 9.25x6.25x0.75 inches. In Stock.

Paperback. Condition: Brand New. 2012 edition. 538 pages. 9.00x6.00x1.25 inches. In Stock.

Paperback. Condition: Brand New. reprint edition. 156 pages. 9.30x6.20x0.37 inches. In Stock.

Taschenbuch. Condition: Neu. Druck auf Anfrage Neuware - Printed after ordering - Although they are believed to be unsolvable in general, tractability results suggest that some practical NP-hard problems can be efficiently solved. Combinatorial search algorithms are designed to efficiently explore the usually large solution space of these instances by reducing the search space to feasible regions and using heuristics to efficiently explore these regions. Various mathematical formalisms may be used to express and tackle combinatorial problems, among them the constraint satisfaction problem (CSP) and the propositional satisfiability problem (SAT). These algorithms, or constraint solvers, apply search space reduction through inference techniques, use activity-based heuristics to guide exploration, diversify the searches through frequent restarts, and often learn from their mistakes.In this book the author focuses on knowledge sharing in combinatorial search, the capacity to generate and exploit meaningful information, such as redundant constraints, heuristic hints, and performance measures, during search, which can dramatically improve the performance of a constraint solver. Information can be shared between multiple constraint solvers simultaneously working on the same instance, or information can help achieve good performance while solving a large set of related instances. In the first case, information sharing has to be performed at the expense of the underlying search effort, since a solver has to stop its main effort to prepare and communicate the information to other solvers; on the other hand, not sharing information can incur a cost for the whole system, with solvers potentially exploring unfeasible spaces discovered by other solvers. In the second case, sharing performance measures can be done with little overhead, and the goal is to be able to tune a constraint solver in relation to the characteristics of a new instance - this corresponds to the selection of the most suitable algorithm for solving a given instance. The book is suitable for researchers, practitioners, and graduate students working in the areas of optimization, search, constraints, and computational complexity.

Buch. Condition: Neu. Druck auf Anfrage Neuware - Printed after ordering - Although they are believed to be unsolvable in general, tractability results suggest that some practical NP-hard problems can be efficiently solved. Combinatorial search algorithms are designed to efficiently explore the usually large solution space of these instances by reducing the search space to feasible regions and using heuristics to efficiently explore these regions. Various mathematical formalisms may be used to express and tackle combinatorial problems, among them the constraint satisfaction problem (CSP) and the propositional satisfiability problem (SAT). These algorithms, or constraint solvers, apply search space reduction through inference techniques, use activity-based heuristics to guide exploration, diversify the searches through frequent restarts, and often learn from their mistakes.In this book the author focuses on knowledge sharing in combinatorial search, the capacity to generate and exploit meaningful information, such as redundant constraints, heuristic hints, and performance measures, during search, which can dramatically improve the performance of a constraint solver. Information can be shared between multiple constraint solvers simultaneously working on the same instance, or information can help achieve good performance while solving a large set of related instances. In the first case, information sharing has to be performed at the expense of the underlying search effort, since a solver has to stop its main effort to prepare and communicate the information to other solvers; on the other hand, not sharing information can incur a cost for the whole system, with solvers potentially exploring unfeasible spaces discovered by other solvers. In the second case, sharing performance measures can be done with little overhead, and the goal is to be able to tune a constraint solver in relation to the characteristics of a new instance - this corresponds to the selection of the most suitable algorithm for solving a given instance. The book is suitable for researchers, practitioners, and graduate students working in the areas of optimization, search, constraints, and computational complexity.

Buch. Condition: Neu. Neuware -Although they are believed to be unsolvable in general, tractability results suggest that some practical NP-hard problems can be efficiently solved. Combinatorial search algorithms are designed to efficiently explore the usually large solution space of these instances by reducing the search space to feasible regions and using heuristics to efficiently explore these regions. Various mathematical formalisms may be used to express and tackle combinatorial problems, among them the constraint satisfaction problem (CSP) and the propositional satisfiability problem (SAT). These algorithms, or constraint solvers, apply search space reduction through inference techniques, use activity-based heuristics to guide exploration, diversify the searches through frequent restarts, and often learn from their mistakes.In this book the author focuses on knowledge sharing in combinatorial search, the capacity to generate and exploit meaningful information, such as redundant constraints, heuristic hints, and performance measures, during search, which can dramatically improve the performance of a constraint solver. Information can be shared between multiple constraint solvers simultaneously working on the same instance, or information can help achieve good performance while solving a large set of related instances. In the first case, information sharing has to be performed at the expense of the underlying search effort, since a solver has to stop its main effort to prepare and communicate the information to other solvers; on the other hand, not sharing information can incur a cost for the whole system, with solvers potentially exploring unfeasible spaces discovered by other solvers. In the second case, sharing performance measures can be done with little overhead, and the goal is to be able to tune a constraint solver in relation to the characteristics of a new instance ¿ this corresponds to the selection of the most suitable algorithm for solving a given instance.The book is suitable for researchers, practitioners, and graduate students working in the areas of optimization, search, constraints, and computational complexity.Springer Verlag GmbH, Tiergartenstr. 17, 69121 Heidelberg 156 pp. Englisch.

Taschenbuch. Condition: Neu. Neuware -Although they are believed to be unsolvable in general, tractability results suggest that some practical NP-hard problems can be efficiently solved. Combinatorial search algorithms are designed to efficiently explore the usually large solution space of these instances by reducing the search space to feasible regions and using heuristics to efficiently explore these regions. Various mathematical formalisms may be used to express and tackle combinatorial problems, among them the constraint satisfaction problem (CSP) and the propositional satisfiability problem (SAT). These algorithms, or constraint solvers, apply search space reduction through inference techniques, use activity-based heuristics to guide exploration, diversify the searches through frequent restarts, and often learn from their mistakes.In this book the author focuses on knowledge sharing in combinatorial search, the capacity to generate and exploit meaningful information, such as redundant constraints, heuristic hints, and performance measures, during search, which can dramatically improve the performance of a constraint solver. Information can be shared between multiple constraint solvers simultaneously working on the same instance, or information can help achieve good performance while solving a large set of related instances. In the first case, information sharing has to be performed at the expense of the underlying search effort, since a solver has to stop its main effort to prepare and communicate the information to other solvers; on the other hand, not sharing information can incur a cost for the whole system, with solvers potentially exploring unfeasible spaces discovered by other solvers. In the second case, sharing performance measures can be done with little overhead, and the goal is to be able to tune a constraint solver in relation to the characteristics of a new instance ¿ this corresponds to the selection of the most suitable algorithm for solving a given instance.The book is suitable for researchers, practitioners, and graduate students working in the areas of optimization, search, constraints, and computational complexity.Springer Verlag GmbH, Tiergartenstr. 17, 69121 Heidelberg 156 pp. Englisch.

Taschenbuch. Condition: Neu. Druck auf Anfrage Neuware - Printed after ordering - This book constitutes the thoroughly refereed post-conference proceedings of the 6th International Conference on Learning and Intelligent Optimization, LION 6, held in Paris, France, in January 2012. The 23 long and 30 short revised papers were carefully reviewed and selected from a total of 99 submissions. The papers focus on the intersections and uncharted territories between machine learning, artificial intelligence, mathematical programming and algorithms for hard optimization problems. In addition to the paper contributions the conference also included 3 invited speakers, who presented forefront research results and frontiers, and 3 tutorial talks, which were crucial in bringing together the different components of LION community.

Condition: New. In.

Taschenbuch. Condition: Neu. Learning and Intelligent Optimization | 6th International Conference, LION 6, Paris, France, January 16-20, 2012, Revised Selected Papers | Youssef Hamadi (u. a.) | Taschenbuch | xxiv | Englisch | 2012 | Springer | EAN 9783642344121 | Verantwortliche Person für die EU: Springer Verlag GmbH, Tiergartenstr. 17, 69121 Heidelberg, juergen[dot]hartmann[at]springer[dot]com | Anbieter: preigu.