Advances and Trends in Optimization with Engineering Applications PDF Download
Are you looking for read ebook online? Search for your book and save it on your Kindle device, PC, phones or tablets. Download Advances and Trends in Optimization with Engineering Applications PDF full book. Access full book title Advances and Trends in Optimization with Engineering Applications by Tamas Terlaky. Download full books in PDF and EPUB format.
Author: Tamas Terlaky
Publisher: SIAM
ISBN: 1611974674
Category : Mathematics
Languages : en
Pages : 730
Get Book Here
Book Description
Optimization is of critical importance in engineering. Engineers constantly strive for the best possible solutions, the most economical use of limited resources, and the greatest efficiency. As system complexity increases, these goals mandate the use of state-of-the-art optimization techniques. In recent years, the theory and methodology of optimization have seen revolutionary improvements. Moreover, the exponential growth in computational power, along with the availability of multicore computing with virtually unlimited memory and storage capacity, has fundamentally changed what engineers can do to optimize their designs. This is a two-way process: engineers benefit from developments in optimization methodology, and challenging new classes of optimization problems arise from novel engineering applications. Advances and Trends in Optimization with Engineering Applications reviews 10 major areas of optimization and related engineering applications, providing a broad summary of state-of-the-art optimization techniques most important to engineering practice. Each part provides a clear overview of a specific area and discusses a range of real-world problems. The book provides a solid foundation for engineers and mathematical optimizers alike who want to understand the importance of optimization methods to engineering and the capabilities of these methods.
Author: Tamas Terlaky
Publisher: SIAM
ISBN: 1611974674
Category : Mathematics
Languages : en
Pages : 730
Get Book Here
Book Description
Optimization is of critical importance in engineering. Engineers constantly strive for the best possible solutions, the most economical use of limited resources, and the greatest efficiency. As system complexity increases, these goals mandate the use of state-of-the-art optimization techniques. In recent years, the theory and methodology of optimization have seen revolutionary improvements. Moreover, the exponential growth in computational power, along with the availability of multicore computing with virtually unlimited memory and storage capacity, has fundamentally changed what engineers can do to optimize their designs. This is a two-way process: engineers benefit from developments in optimization methodology, and challenging new classes of optimization problems arise from novel engineering applications. Advances and Trends in Optimization with Engineering Applications reviews 10 major areas of optimization and related engineering applications, providing a broad summary of state-of-the-art optimization techniques most important to engineering practice. Each part provides a clear overview of a specific area and discusses a range of real-world problems. The book provides a solid foundation for engineers and mathematical optimizers alike who want to understand the importance of optimization methods to engineering and the capabilities of these methods.
Author: Sébastien Bubeck
Publisher: Foundations and Trends (R) in Machine Learning
ISBN: 9781601988607
Category : Convex domains
Languages : en
Pages : 142
Get Book Here
Book Description
This monograph presents the main complexity theorems in convex optimization and their corresponding algorithms. It begins with the fundamental theory of black-box optimization and proceeds to guide the reader through recent advances in structural optimization and stochastic optimization. The presentation of black-box optimization, strongly influenced by the seminal book by Nesterov, includes the analysis of cutting plane methods, as well as (accelerated) gradient descent schemes. Special attention is also given to non-Euclidean settings (relevant algorithms include Frank-Wolfe, mirror descent, and dual averaging), and discussing their relevance in machine learning. The text provides a gentle introduction to structural optimization with FISTA (to optimize a sum of a smooth and a simple non-smooth term), saddle-point mirror prox (Nemirovski's alternative to Nesterov's smoothing), and a concise description of interior point methods. In stochastic optimization it discusses stochastic gradient descent, mini-batches, random coordinate descent, and sublinear algorithms. It also briefly touches upon convex relaxation of combinatorial problems and the use of randomness to round solutions, as well as random walks based methods.
Author: Ratan Prakash Agarwal
Publisher: World Scientific
ISBN: 9789810223823
Category : Science
Languages : en
Pages : 506
Get Book Here
Book Description
World Scientific Series in Applicable Analysis (WSSIAA) aims at reporting new developments of high mathematical standard and current interest. Each volume in the series shall be devoted to the mathematical analysis that has been applied or potentially applicable to the solutions of scientific, engineering, and social problems. This volume contains 30 research articles on the theory of optimization and its applications by the leading scientists in the field. It is hoped that the material in the present volume will open new vistas in research.Contributors: B D O Anderson, M Bertaja, O J Boxma, O Burdakov, A Cantoni, D J Clements, B D Craven, J B Cruz, Jr., P Diamond, S V Drakunov, Y G Evtushenko, N M Filatov, I Galligani, J C Geromel, F Giannessi, M J Grimble, G O Guardabassi, D-W Gu, C H Houpis, D G Hull, C Itiki, X Jian, M A Johnson, R E Kalaba, J C Kalkkuhl, M R Katebi, T J Kim, P Kloeden, T Kobylarz, A J Laub, C S Lee, G Leitmann, B-G Liu, J Liu, Z-Q Luo, K A Lurie, P Maponi, J B Matson, A Mess, G Pacelli, M Pachter, I Postlethwaite, T Rapcsak, M C Recchioni, Y Sakawa, S V Savastyuk, K Schittkowski, Y Shi, M A Sikora, D D Siljak, K L Teo, C Tovey, P Tseng, F E Udwadia, H Unbehauen, A Vladimirov, B Vo, J F Whidborne, R Xu, P L Yu, V G Zhadan, F Zirilli.
Author: Sinan Melih Nigdeli
Publisher: Springer Nature
ISBN: 303061848X
Category : Technology & Engineering
Languages : en
Pages : 317
Get Book Here
Book Description
This book is an up-to-date source for computation applications of optimization, prediction via artificial intelligence methods, and evaluation of metaheuristic algorithm with different structural applications. As the current interest of researcher, metaheuristic algorithms are a high interest topic area since advance and non-optimized problems via mathematical methods are challenged by the development of advance and modified algorithms. The artificial intelligence (AI) area is also important in predicting optimum results by skipping long iterative optimization processes. The machine learning used in generation of AI models also needs optimum results of metaheuristic-based approaches. This book is a great source to researcher, graduate students, and bachelor students who gain project about structural optimization. Differently from the academic use, the chapter that emphasizes different scopes and methods can take the interest and help engineer working in design and production of structural engineering projects.
Author: Stefan Voß
Publisher: Springer Science & Business Media
ISBN: 1461557755
Category : Business & Economics
Languages : en
Pages : 513
Get Book Here
Book Description
Meta-Heuristics: Advances and Trends in Local Search Paradigms for Optimizations comprises a carefully refereed selection of extended versions of the best papers presented at the Second Meta-Heuristics Conference (MIC 97). The selected articles describe the most recent developments in theory and applications of meta-heuristics, heuristics for specific problems, and comparative case studies. The book is divided into six parts, grouped mainly by the techniques considered. The extensive first part with twelve papers covers tabu search and its application to a great variety of well-known combinatorial optimization problems (including the resource-constrained project scheduling problem and vehicle routing problems). In the second part we find one paper where tabu search and simulated annealing are investigated comparatively and two papers which consider hybrid methods combining tabu search with genetic algorithms. The third part has four papers on genetic and evolutionary algorithms. Part four arrives at a new paradigm within meta-heuristics. The fifth part studies the behavior of parallel local search algorithms mainly from a tabu search perspective. The final part examines a great variety of additional meta-heuristics topics, including neural networks and variable neighbourhood search as well as guided local search. Furthermore, the integration of meta-heuristics with the branch-and-bound paradigm is investigated.
Author: Yin, Peng-Yeng
Publisher: IGI Global
ISBN: 146662146X
Category : Computers
Languages : en
Pages : 375
Get Book Here
Book Description
Developments in metaheuristics continue to advance computation beyond its traditional methods. With groundwork built on multidisciplinary research findings; metaheuristics, algorithms, and optimization approaches uses memory manipulations in order to take full advantage of strategic level problem solving. Trends in Developing Metaheuristics, Algorithms, and Optimization Approaches provides insight on the latest advances and analysis of technologies in metaheuristics computing. Offering widespread coverage on topics such as genetic algorithms, differential evolution, and ant colony optimization, this book aims to be a forum researchers, practitioners, and students who wish to learn and apply metaheuristic computing.
Author: Srinivas Shakkottai
Publisher: Now Publishers Inc
ISBN: 1601981023
Category : Data transmission systems
Languages : en
Pages : 123
Get Book Here
Book Description
Network Optimization and Control is the ideal starting point for a mature reader with little background on the subject of congestion control to understand the basic concepts underlying network resource allocation.
Author: Stephen Boyd
Publisher: Now Publishers Inc
ISBN: 160198460X
Category : Computers
Languages : en
Pages : 138
Get Book Here
Book Description
Surveys the theory and history of the alternating direction method of multipliers, and discusses its applications to a wide variety of statistical and machine learning problems of recent interest, including the lasso, sparse logistic regression, basis pursuit, covariance selection, support vector machines, and many others.
Author: V. Jeyakumar
Publisher: Springer Science & Business Media
ISBN: 9780387267692
Category : Business & Economics
Languages : en
Pages : 476
Get Book Here
Book Description
The search for the best possible performance is inherent in human nature. Individuals, enterprises and governments all seek optimal—that is, the best—possible solutions of problems that they meet. Evidently, continuous optimization plays an increasingly significant role in everyday management and technical decisions in science, engineering and commerce. The collection of 16 refereed papers in this book covers a diverse number of topics and provides a good picture of recent research in continuous optimization. The first part of the book presents substantive survey articles in a number of important topic areas of continuous optimization. Most of the papers in the second part present results on the theoretical aspects as well as numerical methods of continuous optimization. The papers in the third part are mainly concerned with applications of continuous optimization. Hence, the book will be an additional valuable source of information to faculty, students, and researchers who use continuous optimization to model and solve problems. Audience This book is intended for researchers in mathematical programming, optimization and operations research; engineers in various fields; and graduate students in applied mathematics, engineering and operations research.
Author: Neal Parikh
Publisher: Now Pub
ISBN: 9781601987167
Category : Mathematics
Languages : en
Pages : 130
Get Book Here
Book Description
Proximal Algorithms discusses proximal operators and proximal algorithms, and illustrates their applicability to standard and distributed convex optimization in general and many applications of recent interest in particular. Much like Newton's method is a standard tool for solving unconstrained smooth optimization problems of modest size, proximal algorithms can be viewed as an analogous tool for nonsmooth, constrained, large-scale, or distributed versions of these problems. They are very generally applicable, but are especially well-suited to problems of substantial recent interest involving large or high-dimensional datasets. Proximal methods sit at a higher level of abstraction than classical algorithms like Newton's method: the base operation is evaluating the proximal operator of a function, which itself involves solving a small convex optimization problem. These subproblems, which generalize the problem of projecting a point onto a convex set, often admit closed-form solutions or can be solved very quickly with standard or simple specialized methods. Proximal Algorithms discusses different interpretations of proximal operators and algorithms, looks at their connections to many other topics in optimization and applied mathematics, surveys some popular algorithms, and provides a large number of examples of proximal operators that commonly arise in practice.
