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Combinatorial Optimization Under Uncertainty, Arora, Ritu


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Автор: Arora, Ritu
Название:  Combinatorial Optimization Under Uncertainty
ISBN: 9781032316581
Издательство: Taylor&Francis
Классификация:










ISBN-10: 1032316586
Обложка/Формат: Hardback
Страницы: 206
Вес: 0.57 кг.
Дата издания: 12.05.2023
Серия: Advances in metaheuristics
Иллюстрации: 31 tables, black and white; 20 line drawings, color; 16 line drawings, black and white; 1 halftones, color; 21 illustrations, color; 16 illustrations, black and white
Размер: 159 x 242 x 23
Читательская аудитория: Tertiary education (us: college)
Подзаголовок: Real-life scenarios in allocation problems
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Поставляется из: Европейский союз


Combinatorial Optimization / Polyhedra and Efficiency

Автор: Schrijver Alexander
Название: Combinatorial Optimization / Polyhedra and Efficiency
ISBN: 3540443894 ISBN-13(EAN): 9783540443896
Издательство: Springer
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Цена: 17049.00 р.
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Описание: This book offers an in-depth overview of polyhedral methods and efficient algorithms in combinatorial optimization.These methods form a broad, coherent and powerful kernel in combinatorial optimization, with strong links to discrete mathematics, mathematical programming and computer science. In eight parts, various areas are treated, each starting with an elementary introduction to the area, with short, elegant proofs of the principal results, and each evolving to the more advanced methods and results, with full proofs of some of the deepest theorems in the area. Over 4000 references to further research are given, and historical surveys on the basic subjects are presented.

Shape Optimization under Uncertainty from a Stochastic Programming Point of View

Автор: Harald Held
Название: Shape Optimization under Uncertainty from a Stochastic Programming Point of View
ISBN: 3834809098 ISBN-13(EAN): 9783834809094
Издательство: Springer
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Цена: 14673.00 р.
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Описание: Optimization problems whose constraints involve partial differential equations (PDEs) are relevant in many areas of technical, industrial, and economic app- cations. At the same time, they pose challenging mathematical research problems in numerical analysis and optimization. The present text is among the ?rst in the research literature addressing stochastic uncertainty in the context of PDE constrained optimization. The focus is on shape optimization for elastic bodies under stochastic loading. Analogies to ?nite dim- sional two-stage stochastic programming drive the treatment, with shapes taking the role of nonanticipative decisions.The main results concern level set-based s- chastic shape optimization with gradient methods involving shape and topological derivatives. The special structure of the elasticity PDE enables the numerical - lution of stochastic shape optimization problems with an arbitrary number of s- narios without increasing the computational effort signi?cantly. Both risk neutral and risk averse models are investigated. This monograph is based on a doctoral dissertation prepared during 2004-2008 at the Chair of Discrete Mathematics and Optimization in the Department of Ma- ematics of the University of Duisburg-Essen. The work was supported by the Deutsche Forschungsgemeinschaft (DFG) within the Priority Program "Optimi- tion with Partial Differential Equations." Rudiger Schultz Acknowledgments I owe a great deal to my supervisors, colleagues, and friends who have always supported, encouraged, andenlightenedmethroughtheirownresearch, comments, and questions.

Optimization of Temporal Networks under Uncertainty

Автор: Wolfram Wiesemann
Название: Optimization of Temporal Networks under Uncertainty
ISBN: 3642437230 ISBN-13(EAN): 9783642437236
Издательство: Springer
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Цена: 18167.00 р.
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Описание: Many decision problems in Operations Research are defined on temporal networks, that is, workflows of time-consuming tasks whose processing order is constrained by precedence relations.

Optimization Under Stochastic Uncertainty: Methods, Control and Random Search Methods

Автор: Marti Kurt
Название: Optimization Under Stochastic Uncertainty: Methods, Control and Random Search Methods
ISBN: 3030556611 ISBN-13(EAN): 9783030556617
Издательство: Springer
Цена: 11179.00 р.
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Описание: 1. Optimal Control under Stochastic Uncertainty.- 2. Stochastic Optimization of Regulators.- 3. Optimal Open-Loop Control of Dynamic Systems under Stochastic Uncertainty.- 4. Construction of feedback control by means of homotopy methods.- 5. Constructions of Limit State Functions.- 6. Random Search Procedures for Global Optimization.- 7. Controlled Random Search under Uncertainty.- 8. Controlled Random Search Procedures for Global Optimization.- 9. Mathematical Model of Random Search Methods and Elementary Properties.- 10. Special Random Search Methods.- 11. Accessibility Theorems.- 12. Convergence Theorems.- 13. Convergence of Stationary Random Search Methods for Positive Success Probability.- 14. Random Search Methods of convergence order U(n-").- 15. Random Search Methods with a Linear Rate of Convergence.- 16. Success/Failure-driven Random Direction Procedures.- 17. Hybrid Methods.- 18. Solving optimization problems under stochastic uncertainty by Random Search Methods(RSM).

Optimization Under Stochastic Uncertainty: Methods, Control and Random Search Methods

Автор: Marti Kurt
Название: Optimization Under Stochastic Uncertainty: Methods, Control and Random Search Methods
ISBN: 3030556646 ISBN-13(EAN): 9783030556648
Издательство: Springer
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Цена: 11179.00 р.
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Описание: 1. Optimal Control under Stochastic Uncertainty.- 2. Stochastic Optimization of Regulators.- 3. Optimal Open-Loop Control of Dynamic Systems under Stochastic Uncertainty.- 4. Construction of feedback control by means of homotopy methods.- 5. Constructions of Limit State Functions.- 6. Random Search Procedures for Global Optimization.- 7. Controlled Random Search under Uncertainty.- 8. Controlled Random Search Procedures for Global Optimization.- 9. Mathematical Model of Random Search Methods and Elementary Properties.- 10. Special Random Search Methods.- 11. Accessibility Theorems.- 12. Convergence Theorems.- 13. Convergence of Stationary Random Search Methods for Positive Success Probability.- 14. Random Search Methods of convergence order U(n-").- 15. Random Search Methods with a Linear Rate of Convergence.- 16. Success/Failure-driven Random Direction Procedures.- 17. Hybrid Methods.- 18. Solving optimization problems under stochastic uncertainty by Random Search Methods(RSM).

Recent Advances in Evolutionary Computation for Combinatorial Optimization

Автор: Carlos Cotta; Jano van Hemert
Название: Recent Advances in Evolutionary Computation for Combinatorial Optimization
ISBN: 3642089739 ISBN-13(EAN): 9783642089732
Издательство: Springer
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Цена: 27251.00 р.
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Описание: This cutting-edge volume presents recent advances in the area of metaheuristic combinatorial optimisation, with a special focus on evolutionary computation methods. Moreover, it addresses local search methods and hybrid approaches.

Evolutionary Computation in Combinatorial Optimization

Автор: Carlos Cotta; Peter I. Cowling
Название: Evolutionary Computation in Combinatorial Optimization
ISBN: 3642010083 ISBN-13(EAN): 9783642010088
Издательство: Springer
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Цена: 9781.00 р.
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Описание: Constitutes the refereed proceedings of the 9th European Conference on Evolutionary Computation in Combinatorial Optimization, EvoCOP 2009, held in Tubingen, Germany, in April 2009. This work contains papers that discuss developments and applications in metaheuristics.

Combinatorial optimization :

Автор: Bernhard Korte, Jens Vygen
Название: Combinatorial optimization :
ISBN: 3662560380 ISBN-13(EAN): 9783662560389
Издательство: Springer
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Цена: 9083.00 р.
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Описание: This comprehensive textbook on combinatorial optimization emphasizes theoretical results and algorithms with provably good performance, in contrast to heuristics. The text contains complete but concise proofs, and also provides numerous exercises and references.

Supply Chain Optimization under Uncertainty

Автор: Barrie Michael Cole
Название: Supply Chain Optimization under Uncertainty
ISBN: 1622730321 ISBN-13(EAN): 9781622730322
Издательство: Неизвестно
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Цена: 13426.00 р.
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Supply Chain Optimization Under Uncertainty

Автор: Cole Barrie Michael
Название: Supply Chain Optimization Under Uncertainty
ISBN: 162273016X ISBN-13(EAN): 9781622730162
Издательство: Неизвестно
Цена: 20047.00 р.
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Optimization Under Uncertainty with Applications to Aerospace Engineering

Автор: Vasile Massimiliano
Название: Optimization Under Uncertainty with Applications to Aerospace Engineering
ISBN: 303060165X ISBN-13(EAN): 9783030601652
Издательство: Springer
Цена: 22359.00 р.
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Описание: - Introduction to Spectral Methods for Uncertainty Quantification. - Introduction to Imprecise Probabilities. - Uncertainty Quantification in Lasso-Type Regularization Problems. - Reliability Theory. - An Introduction to Imprecise Markov Chains. - Fundamentals of Filtering. - Introduction to Optimisation. - An Introduction to Many-Objective Evolutionary Optimization. - Multilevel Optimisation. - Sequential Parameter Optimization for Mixed-Discrete Problems. - Parameter Control in Evolutionary Optimisation. - Response Surface Methodology. - Risk Measures in the Context of Robust and Reliability Based Optimization. - Best Practices for Surrogate Based Uncertainty Quantification in Aerodynamics and Application to Robust Shape Optimization. - In-flight Icing: Modeling, Prediction, and Uncertainty. - Uncertainty Treatment Applications: High-Enthalpy Flow Ground Testing. - Introduction to Evidence-Based Robust Optimisation.

Advances in Uncertainty Quantification and Optimization Under Uncertainty with Aerospace Applications: Proceedings of the 2020 Uqop International Conf

Автор: Vasile Massimiliano, Quagliarella Domenico
Название: Advances in Uncertainty Quantification and Optimization Under Uncertainty with Aerospace Applications: Proceedings of the 2020 Uqop International Conf
ISBN: 3030805417 ISBN-13(EAN): 9783030805418
Издательство: Springer
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Цена: 34937.00 р.
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Описание: The 2020 International Conference on Uncertainty Quantification & Optimization gathered together internationally renowned researchers in the fields of optimization and uncertainty quantification.


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