Evaluation Complexity of Algorithms for Nonconvex Optimization: Theory, Computation, and Perspectives, Coralia Cartis, Nicholas I. M. Gould, Philippe L. Toint

Автор: Tor Lattimore, Csaba Szepesvari
Название: Bandit Algorithms
ISBN: 1108486827 ISBN-13(EAN): 9781108486828
Издательство: Cambridge Academ
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Цена: 46470.00 T
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Описание: Decision-making in the face of uncertainty is a challenge in machine learning, and the multi-armed bandit model is a common framework to address it. This comprehensive introduction is an excellent reference for established researchers and a resource for graduate students interested in exploring stochastic, adversarial and Bayesian frameworks.

Автор: Jeffrey Humpherys, Tyler J. Jarvis
Название: Foundations of Applied Mathematics, Volume 2: Algorithms, Approximation, Optimization
ISBN: 1611976057 ISBN-13(EAN): 9781611976052
Издательство: Mare Nostrum (Eurospan)
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Цена: 81090.00 T
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Описание: In this second book of what will be a four-volume series, the authors present, in a mathematically rigorous way, the essential foundations of both the theory and practice of algorithms, approximation, and optimization—essential topics in modern applied and computational mathematics. This material is the introductory framework upon which algorithm analysis, optimization, probability, statistics, machine learning, and control theory are built. This text gives a unified treatment of several topics that do not usually appear together: the theory and analysis of algorithms for mathematicians and data science students; probability and its applications; the theory and applications of approximation, including Fourier series, wavelets, and polynomial approximation; and the theory and practice of optimization, including dynamic optimization.When used in concert with the free supplemental lab materials, Foundations of Applied Mathematics, Volume 2: Algorithms, Approximation, Optimization teaches not only the theory but also the computational practice of modern mathematical methods. Exercises and examples build upon each other in a way that continually reinforces previous ideas, allowing students to retain learned concepts while achieving a greater depth. The mathematically rigorous lab content guides students to technical proficiency and answers the age-old question “When am I going to use this?”This textbook is geared toward advanced undergraduate and beginning graduate students in mathematics, data science, or machine learning.

Автор: Hiroshi Konno; Phan Thien Thach; Hoang Tuy
Название: Optimization on Low Rank Nonconvex Structures
ISBN: 0792343085 ISBN-13(EAN): 9780792343080
Издательство: Springer
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Цена: 241310.00 T
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Описание: This work is devoted to global optimization problems with special structures. Most of these problems, though highly nonconvex, can be characterized by the property that they reduce to convex minimization problems when some of the variables are fixed.

Автор: Shashi K. Mishra
Название: Topics in Nonconvex Optimization
ISBN: 1461428890 ISBN-13(EAN): 9781461428893
Издательство: Springer
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Цена: 130430.00 T
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Описание: This contributed volume consists of papers in the area of nonconvex optimization from researchers practicing in India. It aims to bring together new concepts, theoretical developments, and applications from these researchers.

Автор: Hiroshi Konno; Phan Thien Thach; Hoang Tuy
Название: Optimization on Low Rank Nonconvex Structures
ISBN: 1461368359 ISBN-13(EAN): 9781461368359
Издательство: Springer
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Цена: 186330.00 T
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Автор: David Yang Gao
Название: Duality Principles in Nonconvex Systems
ISBN: 1441948252 ISBN-13(EAN): 9781441948250
Издательство: Springer
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Цена: 167660.00 T
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Автор: David Yang Gao; Raymond W. Ogden; Georgios E. Stav
Название: Nonsmooth/Nonconvex Mechanics
ISBN: 1461379733 ISBN-13(EAN): 9781461379737
Издательство: Springer
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Цена: 111790.00 T
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Автор: Alexander J. Zaslavski
Название: Nonconvex Optimal Control and Variational Problems
ISBN: 1461473772 ISBN-13(EAN): 9781461473770
Издательство: Springer
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Цена: 93160.00 T
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Описание: This volume contains results concerning well-posedness of optimal control and variational problems, nonoccurrence of the Lavrentiev phenomenon for optimal control and variational problems, and turnpike properties of approximate solutions.

Автор: Hanif D. Sherali; W. P. Adams
Название: A Reformulation-Linearization Technique for Solving Discrete and Continuous Nonconvex Problems
ISBN: 1441948082 ISBN-13(EAN): 9781441948083
Издательство: Springer
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Цена: 231990.00 T
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Описание: In the Reformulation Phase, certain types of additional implied polynomial constraints, that include the aforementioned constraints in the case of binary variables, are appended to the problem.

Автор: Hanif D. Sherali; W. P. Adams
Название: A Reformulation-Linearization Technique for Solving Discrete and Continuous Nonconvex Problems
ISBN: 0792354877 ISBN-13(EAN): 9780792354871
Издательство: Springer
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Цена: 231990.00 T
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Описание: Addresses a method for generating tight linear or convex programming relaxations for discrete and continuous nonconvex programming problems. This book is intended for researchers and practitioners who work in the area of discrete or continuous nonlinear, nonconvex optimization problems.

Автор: Johannes Brehmer
Название: Utility Maximization in Nonconvex Wireless Systems
ISBN: 3642443974 ISBN-13(EAN): 9783642443978
Издательство: Springer
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Цена: 104480.00 T
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Описание: This book offers a framework for modeling and solving utility maximization problems in nonconvex wireless systems, presenting a general model for utility optimization in wireless systems, methods for solving problems, and case studies showing the model in use.

Автор: Demetriou Ioannis C., Pardalos Panos M.
Название: Approximation and Optimization: Algorithms, Complexity and Applications
ISBN: 3030127699 ISBN-13(EAN): 9783030127695
Издательство: Springer
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Цена: 55890.00 T
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Описание: Introduction.- Evaluation Complexity Bounds for Smooth Constrained Nonlinear Optimization using Scaled KKT Conditions and High-order Models.- Data-Dependent Approximation in Social Computing.- Multi-Objective Evolutionary Optimization Algorithms for Machine Learning: a Recent Survey.- No Free Lunch Theorem, a Review.- Piecewise Convex-Concave Approximation in the Minimax Norm.- A Decomposition Theorem for the Least Squares Piecewise Monotonic Data Approximation Problem.- Recent Progress in Optimization of Multiband Electrical Filters.- Impact of Error in Parameter Estimations on Large Scale Portfolio Optimization.- Optimal Design of Smart Composites.- Tax Evasion as an Optimal Solution to a Partially Observable Markov Decision Process.