Skip to main content

Fundamentals Of Optimization Techniques With Algorithms

Download Fundamentals Of Optimization Techniques With Algorithms Full eBooks in PDF, EPUB, and kindle. Fundamentals Of Optimization Techniques With Algorithms is one my favorite book and give us some inspiration, very enjoy to read. you could read this book anywhere anytime directly from your device.

Fundamentals of Optimization Techniques with Algorithms

Fundamentals of Optimization Techniques with Algorithms Book
Author : Sukanta Nayak
Publisher : Academic Press
Release : 2020-08-25
ISBN : 0128224924
Language : En, Es, Fr & De

DOWNLOAD

Book Description :

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 techniques along with a variety of example problems, algorithms and MATLAB© code optimization techniques, for linear and nonlinear single variable and multivariable models, as well as multi-objective and advanced optimization techniques. It presents both theoretical and numerical perspectives in a clear and approachable way. In order to help the reader apply optimization techniques in practice, the book details program codes and computer-aided designs in relation to real-world problems. Ten chapters cover, an introduction to optimization; linear programming; single variable nonlinear optimization; multivariable unconstrained nonlinear optimization; multivariable constrained nonlinear optimization; geometric programming; dynamic programming; integer programming; multi-objective optimization; and nature-inspired optimization. This book provides accessible coverage of optimization techniques, and helps the reader to apply them in practice. Presents optimization techniques clearly, including worked-out examples, from traditional to advanced Maps out the relations between optimization and other mathematical topics and disciplines Provides systematic coverage of algorithms to facilitate computer coding Gives MATLAB© codes in relation to optimization techniques and their use in computer-aided design Presents nature-inspired optimization techniques including genetic algorithms and artificial neural networks

Optimization Techniques and Applications with Examples

Optimization Techniques and Applications with Examples Book
Author : Xin-She Yang
Publisher : John Wiley & Sons
Release : 2018-08-30
ISBN : 111949060X
Language : En, Es, Fr & De

DOWNLOAD

Book Description :

A guide to modern optimization applications and techniques in newly emerging areas spanning optimization, data science, machine intelligence, engineering, and computer sciences Optimization Techniques and Applications with Examples introduces the fundamentals of all the commonly used techniques in optimization that encompass the broadness and diversity of the methods (traditional and new) and algorithms. The author—a noted expert in the field—covers a wide range of topics including mathematical foundations, optimization formulation, optimality conditions, algorithmic complexity, linear programming, convex optimization, and integer programming. In addition, the book discusses artificial neural network, clustering and classifications, constraint-handling, queueing theory, support vector machine and multi-objective optimization, evolutionary computation, nature-inspired algorithms and many other topics. Designed as a practical resource, all topics are explained in detail with step-by-step examples to show how each method works. The book’s exercises test the acquired knowledge that can be potentially applied to real problem solving. By taking an informal approach to the subject, the author helps readers to rapidly acquire the basic knowledge in optimization, operational research, and applied data mining. This important resource: Offers an accessible and state-of-the-art introduction to the main optimization techniques Contains both traditional optimization techniques and the most current algorithms and swarm intelligence-based techniques Presents a balance of theory, algorithms, and implementation Includes more than 100 worked examples with step-by-step explanations Written for upper undergraduates and graduates in a standard course on optimization, operations research and data mining, Optimization Techniques and Applications with Examples is a highly accessible guide to understanding the fundamentals of all the commonly used techniques in optimization.

Fundamentals of Optimization Techniques with Algorithms

Fundamentals of Optimization Techniques with Algorithms Book
Author : Sukanta Nayak
Publisher : Academic Press
Release : 2020-09-08
ISBN : 0128211261
Language : En, Es, Fr & De

DOWNLOAD

Book Description :

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 techniques along with a variety of example problems, algorithms and MATLAB© code optimization techniques, for linear and nonlinear single variable and multivariable models, as well as multi-objective and advanced optimization techniques. It presents both theoretical and numerical perspectives in a clear and approachable way. In order to help the reader apply optimization techniques in practice, the book details program codes and computer-aided designs in relation to real-world problems. Ten chapters cover, an introduction to optimization; linear programming; single variable nonlinear optimization; multivariable unconstrained nonlinear optimization; multivariable constrained nonlinear optimization; geometric programming; dynamic programming; integer programming; multi-objective optimization; and nature-inspired optimization. This book provides accessible coverage of optimization techniques, and helps the reader to apply them in practice. Presents optimization techniques clearly, including worked-out examples, from traditional to advanced Maps out the relations between optimization and other mathematical topics and disciplines Provides systematic coverage of algorithms to facilitate computer coding Gives MATLAB© codes in relation to optimization techniques and their use in computer-aided design Presents nature-inspired optimization techniques including genetic algorithms and artificial neural networks

Linear Algebra And Optimization With Applications To Machine Learning Volume Ii Fundamentals Of Optimization Theory With Applications To Machine Learning

Linear Algebra And Optimization With Applications To Machine Learning   Volume Ii  Fundamentals Of Optimization Theory With Applications To Machine Learning Book
Author : Quaintance Jocelyn,Gallier Jean H
Publisher : World Scientific
Release : 2020-03-16
ISBN : 9811216584
Language : En, Es, Fr & De

DOWNLOAD

Book Description :

Volume 2 applies the linear algebra concepts presented in Volume 1 to optimization problems which frequently occur throughout machine learning. This book blends theory with practice by not only carefully discussing the mathematical under pinnings of each optimization technique but by applying these techniques to linear programming, support vector machines (SVM), principal component analysis (PCA), and ridge regression. Volume 2 begins by discussing preliminary concepts of optimization theory such as metric spaces, derivatives, and the Lagrange multiplier technique for finding extrema of real valued functions. The focus then shifts to the special case of optimizing a linear function over a region determined by affine constraints, namely linear programming. Highlights include careful derivations and applications of the simplex algorithm, the dual-simplex algorithm, and the primal-dual algorithm. The theoretical heart of this book is the mathematically rigorous presentation of various nonlinear optimization methods, including but not limited to gradient decent, the Karush-Kuhn-Tucker (KKT) conditions, Lagrangian duality, alternating direction method of multipliers (ADMM), and the kernel method. These methods are carefully applied to hard margin SVM, soft margin SVM, kernel PCA, ridge regression, lasso regression, and elastic-net regression. Matlab programs implementing these methods are included.

Fundamentals of Optimization

Fundamentals of Optimization Book
Author : Mark French
Publisher : Springer
Release : 2018-05-02
ISBN : 3319761927
Language : En, Es, Fr & De

DOWNLOAD

Book Description :

This textbook is for readers new or returning to the practice of optimization whose interest in the subject may relate to a wide range of products and processes. Rooted in the idea of “minimum principles,” the book introduces the reader to the analytical tools needed to apply optimization practices to an array of single- and multi-variable problems. While comprehensive and rigorous, the treatment requires no more than a basic understanding of technical math and how to display mathematical results visually. It presents a group of simple, robust methods and illustrates their use in clearly-defined examples. Distinct from the majority of optimization books on the market intended for a mathematically sophisticated audience who might want to develop their own new methods of optimization or do research in the field, this volume fills the void in instructional material for those who need to understand the basic ideas. The text emerged from a set of applications-driven lecture notes used in optimization courses the author has taught for over 25 years. The book is class-tested and refined based on student feedback, devoid of unnecessary abstraction, and ideal for students and practitioners from across the spectrum of engineering disciplines. It provides context through practical examples and sections describing commercial application of optimization ideas, such as how containerized freight and changing sea routes have been used to continually reduce the cost of moving freight across oceans. It also features 2D and 3D plots and an appendix illustrating the most widely used MATLAB optimization functions.

Optimization in Engineering

Optimization in Engineering Book
Author : Ramteen Sioshansi,Antonio J. Conejo
Publisher : Springer
Release : 2017-06-24
ISBN : 3319567691
Language : En, Es, Fr & De

DOWNLOAD

Book Description :

This textbook covers the fundamentals of optimization, including linear, mixed-integer linear, nonlinear, and dynamic optimization techniques, with a clear engineering focus. It carefully describes classical optimization models and algorithms using an engineering problem-solving perspective, and emphasizes modeling issues using many real-world examples related to a variety of application areas. Providing an appropriate blend of practical applications and optimization theory makes the text useful to both practitioners and students, and gives the reader a good sense of the power of optimization and the potential difficulties in applying optimization to modeling real-world systems. The book is intended for undergraduate and graduate-level teaching in industrial engineering and other engineering specialties. It is also of use to industry practitioners, due to the inclusion of real-world applications, opening the door to advanced courses on both modeling and algorithm development within the industrial engineering and operations research fields.

Algorithms for Optimization

Algorithms for Optimization Book
Author : Mykel J. Kochenderfer,Tim A. Wheeler
Publisher : MIT Press
Release : 2019-03-12
ISBN : 0262039427
Language : En, Es, Fr & De

DOWNLOAD

Book Description :

A comprehensive introduction to optimization with a focus on practical algorithms for the design of engineering systems. This book offers a comprehensive introduction to optimization with a focus on practical algorithms. The book approaches optimization from an engineering perspective, where the objective is to design a system that optimizes a set of metrics subject to constraints. Readers will learn about computational approaches for a range of challenges, including searching high-dimensional spaces, handling problems where there are multiple competing objectives, and accommodating uncertainty in the metrics. Figures, examples, and exercises convey the intuition behind the mathematical approaches. The text provides concrete implementations in the Julia programming language. Topics covered include derivatives and their generalization to multiple dimensions; local descent and first- and second-order methods that inform local descent; stochastic methods, which introduce randomness into the optimization process; linear constrained optimization, when both the objective function and the constraints are linear; surrogate models, probabilistic surrogate models, and using probabilistic surrogate models to guide optimization; optimization under uncertainty; uncertainty propagation; expression optimization; and multidisciplinary design optimization. Appendixes offer an introduction to the Julia language, test functions for evaluating algorithm performance, and mathematical concepts used in the derivation and analysis of the optimization methods discussed in the text. The book can be used by advanced undergraduates and graduate students in mathematics, statistics, computer science, any engineering field, (including electrical engineering and aerospace engineering), and operations research, and as a reference for professionals.

Practical Augmented Lagrangian Methods for Constrained Optimization

Practical Augmented Lagrangian Methods for Constrained Optimization Book
Author : Ernesto G. Birgin,JosŸ Mario Martinez
Publisher : SIAM
Release : 2014-04-30
ISBN : 161197335X
Language : En, Es, Fr & De

DOWNLOAD

Book Description :

This book focuses on Augmented Lagrangian techniques for solving practical constrained optimization problems. The authors rigorously delineate mathematical convergence theory based on sequential optimality conditions and novel constraint qualifications. They also orient the book to practitioners by giving priority to results that provide insight on the practical behavior of algorithms and by providing geometrical and algorithmic interpretations of every mathematical result, and they fully describe a freely available computational package for constrained optimization and illustrate its usefulness with applications.

An Introduction to Optimization

An Introduction to Optimization Book
Author : Edwin K. P. Chong,Stanislaw H. Zak
Publisher : John Wiley & Sons
Release : 2013-02-05
ISBN : 1118515153
Language : En, Es, Fr & De

DOWNLOAD

Book Description :

Praise for the Third Edition ". . . guides and leads the reader through the learning path . . . [e]xamples are stated very clearly and the results are presented with attention to detail." —MAA Reviews Fully updated to reflect new developments in the field, the Fourth Edition of Introduction to Optimization fills the need for accessible treatment of optimization theory and methods with an emphasis on engineering design. Basic definitions and notations are provided in addition to the related fundamental background for linear algebra, geometry, and calculus. This new edition explores the essential topics of unconstrained optimization problems, linear programming problems, and nonlinear constrained optimization. The authors also present an optimization perspective on global search methods and include discussions on genetic algorithms, particle swarm optimization, and the simulated annealing algorithm. Featuring an elementary introduction to artificial neural networks, convex optimization, and multi-objective optimization, the Fourth Edition also offers: A new chapter on integer programming Expanded coverage of one-dimensional methods Updated and expanded sections on linear matrix inequalities Numerous new exercises at the end of each chapter MATLAB exercises and drill problems to reinforce the discussed theory and algorithms Numerous diagrams and figures that complement the written presentation of key concepts MATLAB M-files for implementation of the discussed theory and algorithms (available via the book's website) Introduction to Optimization, Fourth Edition is an ideal textbook for courses on optimization theory and methods. In addition, the book is a useful reference for professionals in mathematics, operations research, electrical engineering, economics, statistics, and business.

Modern Heuristic Optimization Techniques

Modern Heuristic Optimization Techniques Book
Author : Kwang Y. Lee,Mohamed A. El-Sharkawi
Publisher : John Wiley & Sons
Release : 2008-01-28
ISBN : 9780470225851
Language : En, Es, Fr & De

DOWNLOAD

Book Description :

This book explores how developing solutions with heuristic tools offers two major advantages: shortened development time and more robust systems. It begins with an overview of modern heuristic techniques and goes on to cover specific applications of heuristic approaches to power system problems, such as security assessment, optimal power flow, power system scheduling and operational planning, power generation expansion planning, reactive power planning, transmission and distribution planning, network reconfiguration, power system control, and hybrid systems of heuristic methods.

Optimization for Data Analysis

Optimization for Data Analysis Book
Author : Stephen J. Wright,Benjamin Recht
Publisher : Unknown
Release : 2021
ISBN : 9781009004282
Language : En, Es, Fr & De

DOWNLOAD

Book Description :

"Optimization formulations and algorithms have long played a central role in data analysis and machine learning. Maximum likelihood concepts date to Gauss and Laplace in the late 1700s; problems of this type drove developments in unconstrained optimization in the latter half of the 20th century. Mangasarian's papers in the 1960s on pattern separation using linear programming made an explicit connection between machine learning and optimization in the early days of the former subject. During the 1990s, optimization techniques (especially quadratic programming and duality) were key to the development of support vector machines and kernel learning. The period 1997-2010 saw many synergies emerge between regularized / sparse optimization, variable selection, and compressed sensing. In the current era of deep learning, two optimization techniques-stochastic gradient and automatic differentiation (a.k.a. back-propagation)-are essential"--

Convex Optimization

Convex Optimization Book
Author : Stephen Boyd,Stephen P. Boyd,Lieven Vandenberghe
Publisher : Cambridge University Press
Release : 2004-03-08
ISBN : 9780521833783
Language : En, Es, Fr & De

DOWNLOAD

Book Description :

A comprehensive introduction to the tools, techniques and applications of convex optimization.

Optimization in computer engineering Theory and applications

Optimization in computer engineering     Theory and applications Book
Author : Zoltán Ádám Mann
Publisher : Scientific Research Publishing, Inc. USA
Release : 2011-11-15
ISBN : 1618960571
Language : En, Es, Fr & De

DOWNLOAD

Book Description :

The aim of this book is to provide an overview of classic as well as new research results on optimization problems and algorithms. Beside the theoretical basis, the book contains a number of chapters describing the application of the theory in practice, that is, reports on successfully solving real-world engineering challenges by means of optimization algorithms. These case studies are collected from a wide range of application domains within computer engineering. The diversity of the presented approaches offers a number of practical tips and insights into the practical application of optimization algorithms, highlighting real-world challenges and solutions. Researchers, practitioners and graduate students will find the book equally useful.

Modern Optimization Methods for Science Engineering and Technology

Modern Optimization Methods for Science  Engineering and Technology Book
Author : G. R. Sinha
Publisher : Institute of Physics Publishing
Release : 2019-11-20
ISBN : 9780750324052
Language : En, Es, Fr & De

DOWNLOAD

Book Description :

This book reviews the fundamentals, background and theoretical concepts of optimization principles in a comprehensive manner along with their potential applications and implementation strategies. The book will be useful for a wide spectrum of target readers such as research scholars, academics and industry professionals.

Engineering Design Optimization

Engineering Design Optimization Book
Author : Joaquim R. R. A. Martins,Andrew Ning
Publisher : Cambridge University Press
Release : 2021-11-18
ISBN : 1108833411
Language : En, Es, Fr & De

DOWNLOAD

Book Description :

A rigorous yet accessible graduate textbook covering both fundamental and advanced optimization theory and algorithms.

Introduction to Optimization Techniques

Introduction to Optimization Techniques Book
Author : Masanao Aoki
Publisher : Unknown
Release : 1971
ISBN : 0987650XXX
Language : En, Es, Fr & De

DOWNLOAD

Book Description :

Some mathematical preliminaries; Criterion function representation; Location problems; Minimization of unconstrained functions; Minimization of constrained functions; Duality in optimization problems; Comparisons of optimization methods and test problems.

Optimization for Machine Learning

Optimization for Machine Learning Book
Author : Suvrit Sra,Sebastian Nowozin,Stephen J. Wright
Publisher : MIT Press
Release : 2012
ISBN : 026201646X
Language : En, Es, Fr & De

DOWNLOAD

Book Description :

An up-to-date account of the interplay between optimization and machine learning, accessible to students and researchers in both communities. The interplay between optimization and machine learning is one of the most important developments in modern computational science. Optimization formulations and methods are proving to be vital in designing algorithms to extract essential knowledge from huge volumes of data. Machine learning, however, is not simply a consumer of optimization technology but a rapidly evolving field that is itself generating new optimization ideas. This book captures the state of the art of the interaction between optimization and machine learning in a way that is accessible to researchers in both fields. Optimization approaches have enjoyed prominence in machine learning because of their wide applicability and attractive theoretical properties. The increasing complexity, size, and variety of today's machine learning models call for the reassessment of existing assumptions. This book starts the process of reassessment. It describes the resurgence in novel contexts of established frameworks such as first-order methods, stochastic approximations, convex relaxations, interior-point methods, and proximal methods. It also devotes attention to newer themes such as regularized optimization, robust optimization, gradient and subgradient methods, splitting techniques, and second-order methods. Many of these techniques draw inspiration from other fields, including operations research, theoretical computer science, and subfields of optimization. The book will enrich the ongoing cross-fertilization between the machine learning community and these other fields, and within the broader optimization community.

Modern Optimization Techniques with Applications in Electric Power Systems

Modern Optimization Techniques with Applications in Electric Power Systems Book
Author : Soliman Abdel-Hady Soliman,Abdel-Aal Hassan Mantawy
Publisher : Springer Science & Business Media
Release : 2011-12-15
ISBN : 146141752X
Language : En, Es, Fr & De

DOWNLOAD

Book Description :

This book presents the application of some AI related optimization techniques in the operation and control of electric power systems. With practical applications and examples the use of functional analysis, simulated annealing, Tabu-search, Genetic algorithms and fuzzy systems for the optimization of power systems is discussed in detail. Preliminary mathematical concepts are presented before moving to more advanced material. Researchers and graduate students will benefit from this book. Engineers working in utility companies, operations and control, and resource management will also find this book useful. ​

Optimization Techniques and Applications with Examples

Optimization Techniques and Applications with Examples Book
Author : Xin-She Yang
Publisher : John Wiley & Sons
Release : 2018-09-19
ISBN : 1119490545
Language : En, Es, Fr & De

DOWNLOAD

Book Description :

A guide to modern optimization applications and techniques in newly emerging areas spanning optimization, data science, machine intelligence, engineering, and computer sciences Optimization Techniques and Applications with Examples introduces the fundamentals of all the commonly used techniques in optimization that encompass the broadness and diversity of the methods (traditional and new) and algorithms. The author—a noted expert in the field—covers a wide range of topics including mathematical foundations, optimization formulation, optimality conditions, algorithmic complexity, linear programming, convex optimization, and integer programming. In addition, the book discusses artificial neural network, clustering and classifications, constraint-handling, queueing theory, support vector machine and multi-objective optimization, evolutionary computation, nature-inspired algorithms and many other topics. Designed as a practical resource, all topics are explained in detail with step-by-step examples to show how each method works. The book’s exercises test the acquired knowledge that can be potentially applied to real problem solving. By taking an informal approach to the subject, the author helps readers to rapidly acquire the basic knowledge in optimization, operational research, and applied data mining. This important resource: Offers an accessible and state-of-the-art introduction to the main optimization techniques Contains both traditional optimization techniques and the most current algorithms and swarm intelligence-based techniques Presents a balance of theory, algorithms, and implementation Includes more than 100 worked examples with step-by-step explanations Written for upper undergraduates and graduates in a standard course on optimization, operations research and data mining, Optimization Techniques and Applications with Examples is a highly accessible guide to understanding the fundamentals of all the commonly used techniques in optimization.

Adaptive Stochastic Optimization Techniques with Applications

Adaptive Stochastic Optimization Techniques with Applications Book
Author : James A. Momoh
Publisher : CRC Press
Release : 2015-12-02
ISBN : 1498782442
Language : En, Es, Fr & De

DOWNLOAD

Book Description :

Adaptive Stochastic Optimization Techniques with Applications provides a single, convenient source for state-of-the-art information on optimization techniques used to solve problems with adaptive, dynamic, and stochastic features. Presenting modern advances in static and dynamic optimization, decision analysis, intelligent systems, evolutionary pro