-PDF Download- Black Box Optimization Machine Learning And No Free Lunch Theorems EBOOK

Black Box Optimization, Machine Learning, and No-Free Lunch Theorems

Author: Panos M. Pardalos

Publisher: Springer Nature

Published: 2021-05-27

Total Pages: 388

ISBN-13: 3030665151

This edited volume illustrates the connections between machine learning techniques, black box optimization, and no-free lunch theorems. Each of the thirteen contributions focuses on the commonality and interdisciplinary concepts as well as the fundamentals needed to fully comprehend the impact of individual applications and problems. Current theoretical, algorithmic, and practical methods used are provided to stimulate a new effort towards innovative and efficient solutions. The book is intended for beginners who wish to achieve a broad overview of optimization methods and also for more experienced researchers as well as researchers in mathematics, optimization, operations research, quantitative logistics, data analysis, and statistics, who will benefit from access to a quick reference to key topics and methods. The coverage ranges from mathematically rigorous methods to heuristic and evolutionary approaches in an attempt to equip the reader with different viewpoints of the same problem.

Optimization for Machine Learning

Author: Jason Brownlee

Publisher: Machine Learning Mastery

Published: 2021-09-22

Total Pages: 412

ISBN-13:

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Optimization happens everywhere. Machine learning is one example of such and gradient descent is probably the most famous algorithm for performing optimization. Optimization means to find the best value of some function or model. That can be the maximum or the minimum according to some metric. Using clear explanations, standard Python libraries, and step-by-step tutorial lessons, you will learn how to find the optimum point to numerical functions confidently using modern optimization algorithms.

Optimization Methods and Applications

Author: Sergiy Butenko

Publisher: Springer

Published: 2018-02-20

Total Pages: 639

ISBN-13: 3319686402

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Researchers and practitioners in computer science, optimization, operations research and mathematics will find this book useful as it illustrates optimization models and solution methods in discrete, non-differentiable, stochastic, and nonlinear optimization. Contributions from experts in optimization are showcased in this book showcase a broad range of applications and topics detailed in this volume, including pattern and image recognition, computer vision, robust network design, and process control in nonlinear distributed systems. This book is dedicated to the 80th birthday of Ivan V. Sergienko, who is a member of the National Academy of Sciences (NAS) of Ukraine and the director of the V.M. Glushkov Institute of Cybernetics. His work has had a significant impact on several theoretical and applied aspects of discrete optimization, computational mathematics, systems analysis and mathematical modeling.

Machine Learning, Optimization, and Data Science

Author: Giuseppe Nicosia

Publisher: Springer Nature

Published: 2021-01-06

Total Pages: 701

ISBN-13: 3030645800

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This two-volume set, LNCS 12565 and 12566, constitutes the refereed proceedings of the 6th International Conference on Machine Learning, Optimization, and Data Science, LOD 2020, held in Siena, Italy, in July 2020. The total of 116 full papers presented in this two-volume post-conference proceedings set was carefully reviewed and selected from 209 submissions. These research articles were written by leading scientists in the fields of machine learning, artificial intelligence, reinforcement learning, computational optimization, and data science presenting a substantial array of ideas, technologies, algorithms, methods, and applications.

Nature-Inspired Algorithms and Applied Optimization

Author: Xin-She Yang

Publisher: Springer

Published: 2017-10-08

Total Pages: 330

ISBN-13: 3319676695

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This book reviews the state-of-the-art developments in nature-inspired algorithms and their applications in various disciplines, ranging from feature selection and engineering design optimization to scheduling and vehicle routing. It introduces each algorithm and its implementation with case studies as well as extensive literature reviews, and also includes self-contained chapters featuring theoretical analyses, such as convergence analysis and no-free-lunch theorems so as to provide insights into the current nature-inspired optimization algorithms. Topics include ant colony optimization, the bat algorithm, B-spline curve fitting, cuckoo search, feature selection, economic load dispatch, the firefly algorithm, the flower pollination algorithm, knapsack problem, octonian and quaternion representations, particle swarm optimization, scheduling, wireless networks, vehicle routing with time windows, and maximally different alternatives. This timely book serves as a practical guide and reference resource for students, researchers and professionals.

Parallel Computational Technologies

Author: Leonid Sokolinsky

Publisher: Springer Nature

Published: 2022-07-18

Total Pages: 342

ISBN-13: 3031116232

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This book constitutes the refereed proceedings of the 16th International Conference on Parallel Computational Technologies, PCT 2022, held in Dubna, Russia, during March 29–31, 2022. The 22 full papers included in this book were carefully reviewed and selected from 60 submissions. They were organized in topical sections as follows: high performance architectures, tools and technologies; parallel numerical algorithms; supercomputer simulation.

Machine Learning for Econometrics and Related Topics

Author: Vladik Kreinovich

Publisher: Springer Nature

Published:

Total Pages: 491

ISBN-13: 3031436016

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Optimization and Applications

Author: Nicholas Olenev

Publisher: Springer Nature

Published: 2023-12-11

Total Pages: 401

ISBN-13: 3031478592

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This book constitutes the refereed proceedings of the 14th International Conference on Optimization and Applications, OPTIMA 2023, held in Petrovac, Montenegro, during September 18–22, 2023. The 27 full papers included in this book were carefully reviewed and selected from 68 submissions. They were organized in topical sections as follows: mathematical programming; global optimization; discrete and combinatorial optimization; game theory and mathematical economics; optimization in economics and finance; and applications.

Learning and Intelligent Optimization

Author: Dimitris E. Simos

Publisher: Springer Nature

Published: 2023-02-04

Total Pages: 576

ISBN-13: 303124866X

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This book constitutes the refereed proceedings of the 16th International Conference on Learning and Intelligent Optimization, LION 16, which took place in Milos Island, Greece, in June 2022. The 36 full papers and 3 short papers presented in this volume were carefully reviewed and selected from 60 submissions. LION deals with automatic solver configuration, parallel methods, intelligent optimization, nature-inspired algorithms, hard combinatorial optimization problems, DC learning, computational intelligence, and others. The contributions were organized in topical sections as follows: Invited Papers; Contributed Papers.

Learning and Intelligent Optimization

Author: Meinolf Sellmann

Publisher: Springer Nature

Published: 2023-11-25

Total Pages: 628

ISBN-13: 3031445058

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This book constitutes the refereed proceedings of the 17th International Conference on Learning and Intelligent Optimization, LION-17, held in Nice, France, during June 4–8, 2023. The 40 full papers presented have been carefully reviewed and selected from 83 submissions. They focus on all aspects of unleashing the potential of integrating machine learning and optimization approaches, including automatic heuristic selection, intelligent restart strategies, predict-then-optimize, Bayesian optimization, and learning to optimize.