Методы оптимизации

Morgan Kaufmann/Elsevier, 2024. — 250 p. — ISBN: 978-0-443-13925-3. Metaheuristic Optimization Algorithms: Optimizers, Analysis, and Applications presents the most recent optimization algorithms and their applications across a wide range of scientific and engineering research fields. Metaheuristic Optimization Algorithms have become indispensable tools, with applications in...

John Wiley & Sons, Inc., 2024. — 528 p. — ISBN-13: 978-1394235964. Detailed review of optimization from first principles, supported by rigorous math and computer science explanations and various learning aids Supported by rigorous math and computer science foundations, Combinatorial and Algorithmic Mathematics: From Foundation to Optimization provides a from-scratch...

Erik Cuevas, Jorge Galvez, Omar Avalos, Fernando Wario. — Wiley-IEEE Press, 2024. — 432 p. — ISBN: 978-1394229642/ Learn to bridge the gap between machine learning and metaheuristic methods to solve problems in optimization approaches Few areas of technology have greater potential to revolutionize the globe than artificial intelligence. Two key areas of artificial intelligence,...

Editors: Deepak Gupta, Nhu Gia Nguyen, Ashish Khanna, Siddhartha Bhattacharyya. — Walter de Gruyter GmbH, 2021. — 170 p. — (Intelligent Biomedical Data Analysis, 4). — ISBN 978-3-11-067606-8, 978-3-11-067611-2, 978-3-11-067615-0. This book will focus on the involvement of data mining and intelligent computing methods for recent advances in Biomedical applications and algorithms...

Springer, 2023. — 274 p. — ISBN-13: 978-981-99-1521-7. This edited book provides a platform to discuss the state-of-the-art developments associated with traditional and advanced single-/multi-objective criteria optimization methods for addressing problems of performance enhancement of the products and systems design. The book in detail discusses the core ideas, underlying...

Apress Media LLC, 2023. — 249 p. — ISBN-13: 978-1-4842-9063-7. This book covers the essential theory and implementation of popular Bayesian optimization techniques in an intuitive and well-illustrated manner. The techniques covered in this book will enable you to better tune the hyperparemeters of your machine learning models and learn sample-efficient approaches to global...

Springer, 2020. — 399 p. — ISBN: 978-3-030-27045-2 (eBook). This book focuses on mathematical modeling, describes the process of constructing and evaluating models, discusses the challenges and delicacies of the modeling process, and explicitly outlines the required rules and regulations so that the reader will be able to generalize and reuse concepts in other problems by...

Elsevier Inc., 2020. — 322 p. — ISBN: 978-0-12-821126-7. Optimization is a key concept in mathematics, computer science, and operations research, and is essential to the modeling of any system, playing an integral role in computer-aided design. Fundamentals of Optimization Techniques with Algorithms presents a complete package of various traditional and advanced optimization...

Manning Publications, 2023. — 426 р. — ISBN: 978-1633439078. Bayesian optimization helps pinpoint the best configuration for your machine learning models with speed and accuracy. Put its advanced techniques into practice with this hands-on guide. In Bayesian Optimization in Action you will learn how to: Train Gaussian processes on both sparse and large data sets Combine...

Springer, 2024. — 193 p. — (Mathematics Study Resources, 5). — ISBN 978-3-662-66240-3. This textbook is an introduction to global optimization, which treats mathematical facts stringently on the one hand, but also motivates them in great detail and illustrates them with 80 figures. The book is therefore not only aimed at mathematicians, but also at natural scientists, engineers...

De Gruyter, 2020. — 365 p. — ISBN: 978-3-11-066369-3. The book focused on solving equations and optimization problems with MatLAB. The topics on unconstrained optimization, linear and quadratic programming, nonlinear constrained optimization problems, mixed integer programming, multi-objective programming, dynamic programming and intelligent optimization methods are covered....

Xingyi Zhang, Ran Cheng, Ye Tian, Yaochu Jin. — Wiley-IEEE Press, 2024. — 352 p. — ISBN 978-1394178438. Tackle the most challenging problems in science and engineering with these cutting-edge algorithms Multi-objective optimization problems (MOPs) are those in which more than one objective needs to be optimized simultaneously. As a ubiquitous component of research and...

Учебное пособие. — 5 изд., стереотип. — М.: Физматлит, 2004. — 264 с. Рассматривается широкий круг вопросов, связанных с математическим программированием. Изложены теоретические основы задач линейного, выпуклого и нелинейного программирования и построения численных методов для их решения. Для студентов высших учебных заведений.