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minimize and optimize book: MM Optimization Algorithms Kenneth Lange, 2016-07-11 MM Optimization Algorithms offers an overview of the MM principle, a device for deriving optimization algorithms satisfying the ascent or descent property. These algorithms can separate the variables of a problem, avoid large matrix inversions, linearize a problem, restore symmetry, deal with equality and inequality constraints gracefully, and turn a nondifferentiable problem into a smooth problem. The author presents the first extended treatment of MM algorithms, which are ideal for high-dimensional optimization problems in data mining, imaging, and genomics; derives numerous algorithms from a broad diversity of application areas, with a particular emphasis on statistics, biology, and data mining; and summarizes a large amount of literature that has not reached book form before. |
minimize and optimize book: Algorithms for Optimization Mykel J. Kochenderfer, Tim A. Wheeler, 2019-03-12 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. |
minimize and optimize book: Global Optimization Marco Locatelli, Fabio Schoen, 2013-10-16 This volume contains a thorough overview of the rapidly growing field of global optimization, with chapters on key topics such as complexity, heuristic methods, derivation of lower bounds for minimization problems, and branch-and-bound methods and convergence. The final chapter offers both benchmark test problems and applications of global optimization, such as finding the conformation of a molecule or planning an optimal trajectory for interplanetary space travel. An appendix provides fundamental information on convex and concave functions. Intended for Ph.D. students, researchers, and practitioners looking for advanced solution methods to difficult optimization problems. It can be used as a supplementary text in an advanced graduate-level seminar. |
minimize and optimize book: First-Order Methods in Optimization Amir Beck, 2017-10-02 The primary goal of this book is to provide a self-contained, comprehensive study of the main ?rst-order methods that are frequently used in solving large-scale problems. First-order methods exploit information on values and gradients/subgradients (but not Hessians) of the functions composing the model under consideration. With the increase in the number of applications that can be modeled as large or even huge-scale optimization problems, there has been a revived interest in using simple methods that require low iteration cost as well as low memory storage. The author has gathered, reorganized, and synthesized (in a unified manner) many results that are currently scattered throughout the literature, many of which cannot be typically found in optimization books. First-Order Methods in Optimization offers comprehensive study of first-order methods with the theoretical foundations; provides plentiful examples and illustrations; emphasizes rates of convergence and complexity analysis of the main first-order methods used to solve large-scale problems; and covers both variables and functional decomposition methods. |
minimize and optimize book: Minimization Methods for Non-Differentiable Functions N.Z. Shor, 2012-12-06 In recent years much attention has been given to the development of auto matic systems of planning, design and control in various branches of the national economy. Quality of decisions is an issue which has come to the forefront, increasing the significance of optimization algorithms in math ematical software packages for al,ltomatic systems of various levels and pur poses. Methods for minimizing functions with discontinuous gradients are gaining in importance and the ~xperts in the computational methods of mathematical programming tend to agree that progress in the development of algorithms for minimizing nonsmooth functions is the key to the con struction of efficient techniques for solving large scale problems. This monograph summarizes to a certain extent fifteen years of the author's work on developing generalized gradient methods for nonsmooth minimization. This work started in the department of economic cybernetics of the Institute of Cybernetics of the Ukrainian Academy of Sciences under the supervision of V.S. Mikhalevich, a member of the Ukrainian Academy of Sciences, in connection with the need for solutions to important, practical problems of optimal planning and design. In Chap. I we describe basic classes of nonsmooth functions that are dif ferentiable almost everywhere, and analyze various ways of defining generalized gradient sets. In Chap. 2 we study in detail various versions of the su bgradient method, show their relation to the methods of Fejer-type approximations and briefly present the fundamentals of e-subgradient methods. |
minimize and optimize book: Optimization and Control with Applications Liqun Qi, Kok Lay Teo, Xiao Qi Yang, 2006-03-30 A collection of 28 refereed papers grouped according to four broad topics: duality and optimality conditions, optimization algorithms, optimal control, and variational inequality and equilibrium problems. Suitable for researchers, practitioners and postgrads. |
minimize and optimize book: Optimization in Industry Shubhabrata Datta, J. Paulo Davim, 2018-11-03 This book describes different approaches for solving industrial problems like product design, process optimization, quality enhancement, productivity improvement and cost minimization. Several optimization techniques are described. The book covers case studies on the applications of classical as well as evolutionary and swarm optimization tools for solving industrial issues. The content is very helpful for industry personnel, particularly engineers from the Operation, R&D and Quality Assurance sectors, and also the academic researchers of different engineering and/or business administration background. |
minimize and optimize book: Entropy Generation Minimization Adrian Bejan, 2013-10-29 This book presents the diverse and rapidly expanding field of Entropy Generation Minimization (EGM), the method of thermodynamic optimization of real devices. The underlying principles of the EGM method - also referred to as thermodynamic optimization, thermodynamic design, and finite time thermodynamics - are thoroughly discussed, and the me |
minimize and optimize book: Optimization H. Ronald Miller, 1999-11-23 A thorough and highly accessible resource for analysts in a broadrange of social sciences. Optimization: Foundations and Applications presents a series ofapproaches to the challenges faced by analysts who must find thebest way to accomplish particular objectives, usually with theadded complication of constraints on the available choices.Award-winning educator Ronald E. Miller provides detailed coverageof both classical, calculus-based approaches and newer,computer-based iterative methods. Dr. Miller lays a solid foundation for both linear and nonlinearmodels and quickly moves on to discuss applications, includingiterative methods for root-finding and for unconstrainedmaximization, approaches to the inequality constrained linearprogramming problem, and the complexities of inequality constrainedmaximization and minimization in nonlinear problems. Otherimportant features include: * More than 200 geometric interpretations of algebraic results,emphasizing the intuitive appeal of mathematics * Classic results mixed with modern numerical methods to aidusers of computer programs * Extensive appendices containing mathematical details importantfor a thorough understanding of the topic With special emphasis on questions most frequently asked by thoseencountering this material for the first time, Optimization:Foundations and Applications is an extremely useful resource forprofessionals in such areas as mathematics, engineering, economicsand business, regional science, geography, sociology, politicalscience, management and decision sciences, public policy analysis,and numerous other social sciences. An Instructor's Manual presenting detailed solutions to all theproblems in the book is available upon request from the Wileyeditorial department. |
minimize and optimize book: Nonlinear Parameter Optimization Using R Tools John C. Nash, 2014-04-03 Nonlinear Parameter Optimization Using R John C. Nash, Telfer School of Management, University of Ottawa, Canada A systematic and comprehensive treatment of optimization software using R In recent decades, optimization techniques have been streamlined by computational and artificial intelligence methods to analyze more variables, especially under non–linear, multivariable conditions, more quickly than ever before. Optimization is an important tool for decision science and for the analysis of physical systems used in engineering. Nonlinear Parameter Optimization with R explores the principal tools available in R for function minimization, optimization, and nonlinear parameter determination and features numerous examples throughout. Nonlinear Parameter Optimization with R: Provides a comprehensive treatment of optimization techniques Examines optimization problems that arise in statistics and how to solve them using R Enables researchers and practitioners to solve parameter determination problems Presents traditional methods as well as recent developments in R Is supported by an accompanying website featuring R code, examples and datasets Researchers and practitioners who have to solve parameter determination problems who are users of R but are novices in the field optimization or function minimization will benefit from this book. It will also be useful for scientists building and estimating nonlinear models in various fields such as hydrology, sports forecasting, ecology, chemical engineering, pharmaco-kinetics, agriculture, economics and statistics. |
minimize and optimize book: Performance Tuning and Optimizing ASP. Net Applications Kenneth Tu, Jeffrey Hasan, 2014-01-15 |
minimize and optimize book: Pricing and Revenue Optimization Robert Phillips, 2005-08-05 This is the first comprehensive introduction to the concepts, theories, and applications of pricing and revenue optimization. From the initial success of yield management in the commercial airline industry down to more recent successes of markdown management and dynamic pricing, the application of mathematical analysis to optimize pricing has become increasingly important across many different industries. But, since pricing and revenue optimization has involved the use of sophisticated mathematical techniques, the topic has remained largely inaccessible to students and the typical manager. With methods proven in the MBA courses taught by the author at Columbia and Stanford Business Schools, this book presents the basic concepts of pricing and revenue optimization in a form accessible to MBA students, MS students, and advanced undergraduates. In addition, managers will find the practical approach to the issue of pricing and revenue optimization invaluable. Solutions to the end-of-chapter exercises are available to instructors who are using this book in their courses. For access to the solutions manual, please contact marketing@www.sup.org. |
minimize and optimize book: Social Security Strategies William R. Reichenstein, 2011 |
minimize and optimize book: Speed Up Your Site Andrew B. King, 2003 Discover how to use a variety of techniques to shrink the size of a Web page, including HTML, CSS, JavaScript, PHP, XHTML, graphics, multimedia, and server-based techniques. Learn from real-life case studies of existing Web sites, practical examples, and code listings throughout the book. |
minimize and optimize book: Local Minimization, Variational Evolution and Γ-Convergence Andrea Braides, 2014-07-08 This book addresses new questions related to the asymptotic description of converging energies from the standpoint of local minimization and variational evolution. It explores the links between Gamma-limits, quasistatic evolution, gradient flows and stable points, raising new questions and proposing new techniques. These include the definition of effective energies that maintain the pattern of local minima, the introduction of notions of convergence of energies compatible with stable points, the computation of homogenized motions at critical time-scales through the definition of minimizing movement along a sequence of energies, the use of scaled energies to study long-term behavior or backward motion for variational evolutions. The notions explored in the book are linked to existing findings for gradient flows, energetic solutions and local minimizers, for which some generalizations are also proposed. |
minimize and optimize book: Complexity in Numerical Optimization Panos M. Pardalos, 1993 Computational complexity, originated from the interactions between computer science and numerical optimization, is one of the major theories that have revolutionized the approach to solving optimization problems and to analyzing their intrinsic difficulty.The main focus of complexity is the study of whether existing algorithms are efficient for the solution of problems, and which problems are likely to be tractable.The quest for developing efficient algorithms leads also to elegant general approaches for solving optimization problems, and reveals surprising connections among problems and their solutions.This book is a collection of articles on recent complexity developments in numerical optimization. The topics covered include complexity of approximation algorithms, new polynomial time algorithms for convex quadratic minimization, interior point algorithms, complexity issues regarding test generation of NP-hard problems, complexity of scheduling problems, min-max, fractional combinatorial optimization, fixed point computations and network flow problems.The collection of articles provide a broad spectrum of the direction in which research is going and help to elucidate the nature of computational complexity in optimization. The book will be a valuable source of information to faculty, students and researchers in numerical optimization and related areas. |
minimize and optimize book: Robust Optimization Aharon Ben-Tal, Laurent El Ghaoui, Arkadi Nemirovski, 2009-08-10 Robust optimization is still a relatively new approach to optimization problems affected by uncertainty, but it has already proved so useful in real applications that it is difficult to tackle such problems today without considering this powerful methodology. Written by the principal developers of robust optimization, and describing the main achievements of a decade of research, this is the first book to provide a comprehensive and up-to-date account of the subject. Robust optimization is designed to meet some major challenges associated with uncertainty-affected optimization problems: to operate under lack of full information on the nature of uncertainty; to model the problem in a form that can be solved efficiently; and to provide guarantees about the performance of the solution. The book starts with a relatively simple treatment of uncertain linear programming, proceeding with a deep analysis of the interconnections between the construction of appropriate uncertainty sets and the classical chance constraints (probabilistic) approach. It then develops the robust optimization theory for uncertain conic quadratic and semidefinite optimization problems and dynamic (multistage) problems. The theory is supported by numerous examples and computational illustrations. An essential book for anyone working on optimization and decision making under uncertainty, Robust Optimization also makes an ideal graduate textbook on the subject. |
minimize and optimize book: Engineering Design Optimization Joaquim R. R. A. Martins, Andrew Ning, 2021-11-18 Based on course-tested material, this rigorous yet accessible graduate textbook covers both fundamental and advanced optimization theory and algorithms. It covers a wide range of numerical methods and topics, including both gradient-based and gradient-free algorithms, multidisciplinary design optimization, and uncertainty, with instruction on how to determine which algorithm should be used for a given application. It also provides an overview of models and how to prepare them for use with numerical optimization, including derivative computation. Over 400 high-quality visualizations and numerous examples facilitate understanding of the theory, and practical tips address common issues encountered in practical engineering design optimization and how to address them. Numerous end-of-chapter homework problems, progressing in difficulty, help put knowledge into practice. Accompanied online by a solutions manual for instructors and source code for problems, this is ideal for a one- or two-semester graduate course on optimization in aerospace, civil, mechanical, electrical, and chemical engineering departments. |
minimize and optimize book: Optimization in Machine Learning and Applications Anand J. Kulkarni, Suresh Chandra Satapathy, 2020-12-10 This book discusses one of the major applications of artificial intelligence: the use of machine learning to extract useful information from multimodal data. It discusses the optimization methods that help minimize the error in developing patterns and classifications, which further helps improve prediction and decision-making. The book also presents formulations of real-world machine learning problems, and discusses AI solution methodologies as standalone or hybrid approaches. Lastly, it proposes novel metaheuristic methods to solve complex machine learning problems. Featuring valuable insights, the book helps readers explore new avenues leading toward multidisciplinary research discussions. |
minimize and optimize book: Layout Optimization in VLSI Design Bing Lu, Ding-Zhu Du, S. Sapatnekar, 2013-06-29 Introduction The exponential scaling of feature sizes in semiconductor technologies has side-effects on layout optimization, related to effects such as inter connect delay, noise and crosstalk, signal integrity, parasitics effects, and power dissipation, that invalidate the assumptions that form the basis of previous design methodologies and tools. This book is intended to sample the most important, contemporary, and advanced layout opti mization problems emerging with the advent of very deep submicron technologies in semiconductor processing. We hope that it will stimulate more people to perform research that leads to advances in the design and development of more efficient, effective, and elegant algorithms and design tools. Organization of the Book The book is organized as follows. A multi-stage simulated annealing algorithm that integrates floorplanning and interconnect planning is pre sented in Chapter 1. To reduce the run time, different interconnect plan ning approaches are applied in different ranges of temperatures. Chapter 2 introduces a new design methodology - the interconnect-centric design methodology and its centerpiece, interconnect planning, which consists of physical hierarchy generation, floorplanning with interconnect planning, and interconnect architecture planning. Chapter 3 investigates a net-cut minimization based placement tool, Dragon, which integrates the state of the art partitioning and placement techniques. |
minimize and optimize book: An Introduction to Optimization Edwin K. P. Chong, Stanislaw H. Żak, 2013-02-05 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. |
minimize and optimize book: Combinatorial And Global Optimization Rainer E Burkard, Athanasios Migdalas, Panos M Pardalos, 2002-04-05 Combinatorial and global optimization problems appear in a wide range of applications in operations research, engineering, biological science, and computer science. In combinatorial optimization and graph theory, many approaches have been developed that link the discrete universe to the continuous universe through geometric, analytic, and algebraic techniques. Such techniques include global optimization formulations, semidefinite programming, and spectral theory. Recent major successes based on these approaches include interior point algorithms for linear and discrete problems, the celebrated Goemans-Williamson relaxation of the maximum cut problem, and the Du-Hwang solution of the Gilbert-Pollak conjecture. Since integer constraints are equivalent to nonconvex constraints, the fundamental difference between classes of optimization problems is not between discrete and continuous problems but between convex and nonconvex optimization problems. This volume is a selection of refereed papers based on talks presented at a conference on “Combinatorial and Global Optimization” held at Crete, Greece. |
minimize and optimize book: Optimization Kenneth Lange, 2014-01-15 |
minimize and optimize book: Optimization Jan Brinkhuis, Vladimir Tikhomirov, 2011-02-11 This self-contained textbook is an informal introduction to optimization through the use of numerous illustrations and applications. The focus is on analytically solving optimization problems with a finite number of continuous variables. In addition, the authors provide introductions to classical and modern numerical methods of optimization and to dynamic optimization. The book's overarching point is that most problems may be solved by the direct application of the theorems of Fermat, Lagrange, and Weierstrass. The authors show how the intuition for each of the theoretical results can be supported by simple geometric figures. They include numerous applications through the use of varied classical and practical problems. Even experts may find some of these applications truly surprising. A basic mathematical knowledge is sufficient to understand the topics covered in this book. More advanced readers, even experts, will be surprised to see how all main results can be grounded on the Fermat-Lagrange theorem. The book can be used for courses on continuous optimization, from introductory to advanced, for any field for which optimization is relevant. |
minimize and optimize book: Algorithms from THE BOOK Kenneth Lange, 2020-05-04 Algorithms are a dominant force in modern culture, and every indication is that they will become more pervasive, not less. The best algorithms are undergirded by beautiful mathematics. This text cuts across discipline boundaries to highlight some of the most famous and successful algorithms. Readers are exposed to the principles behind these examples and guided in assembling complex algorithms from simpler building blocks. Written in clear, instructive language within the constraints of mathematical rigor, Algorithms from THE BOOK includes a large number of classroom-tested exercises at the end of each chapter. The appendices cover background material often omitted from undergraduate courses. Most of the algorithm descriptions are accompanied by Julia code, an ideal language for scientific computing. This code is immediately available for experimentation. Algorithms from THE BOOK is aimed at first-year graduate and advanced undergraduate students. It will also serve as a convenient reference for professionals throughout the mathematical sciences, physical sciences, engineering, and the quantitative sectors of the biological and social sciences. |
minimize and optimize book: Mastering AWS Cost Optimization Eli Mansoor, Yair Green, 2019 The book Mastering AWS Cost Optimization is intended to support you in overcoming one of the top challenges that organizations face in their journey towards public cloud: the challenge of cost control and optimization. Reading this book will give you a better understanding of both the technical and operational aspects of the process. This ensures that you will succeed in taking advantage of advanced technology for building innovative products, while doing so in an optimized and cost-effective manner. This book contains many proven technical, operational, and applications-related best practices. All are real-life best practices that were implemented in the efforts of controlling and reducing the costs of our own cloud infrastructure as well as that of our customers. What You Will Learn? • Amazon's Compute (EC2, Lambda, Container Services), Storage (S3, Glacier, EBS, and EFS), and Networking services pricing models. • Best practices for architecting and operating your cloud environments for cost optimization and efficiency. • How to build applications that are lightweight from the perspective of resource requirements. • How to leverage AWS operational services (Service Catalog, Config, Budgets, Landing Zone, Tagging, CloudWatch, and others) for ensuring continuous governance and on-going cost efficiency. The KAOTM Methodology We have developed the KAOTM (Knowledge, Architecture, and Operation) methodology to provide a structured approach towards optimizing the costs of any cloud service you will consume - even services not covered within this book. This methodology will lay the foundation needed for addressing any cost-optimization task and provide a structured approach for each optimization effort. Who Should Read this Book? We recommend that everyone involved in a cloud project read this book. This includes those undergoing their first cloud transformation project (moving workloads to the cloud) through early adopters in born-to-the-cloud companies. Why? Because cloud computing represents much more than new technology and tools. The costs of cloud computing are related to new pay-per-use pricing models, new consumption models, new operational methodologies, new tracking and reporting systems, and more. Traditional approaches to cost analysis and optimization simply do not apply to public cloud computing. |
minimize and optimize book: Optimize Your Healthcare Supply Chain Performance Gerald R. Ledlow, Allison Corry, Mark A. Cwiek, 2007 If you are not optimizing supply chain performance, you are missing the opportunity to control costs, improve patient safety, and increase nurse and physician satisfaction. This book provides the information you need to understand and improve supply chain management at your organization. Written for senior leaders, this book explains how to enable your team to make sound supply chain decisions. Selecting where to make changes, when to use different supply chain approaches, and how to find greater value are at the heart of this book. This practical resource provides guidelines for ensuring optimal supply chain operations, including how to: Minimize supply chain tasks for clinicians Increase staff knowledge of supply chain operations Develop supply chain metrics for service, quality, and cost Develop supply chain goals and objectives by service line Focus on the management of billable supplies Get started right away by implementing one of the supply chain improvement projects described in the book. Each project description includes estimated costs and potential benefits to help you determine the best fit for your organization. This book also provides tips for managing relationships with various stakeholders, including manufacturers and distributors. |
minimize and optimize book: Cost Reduction and Optimization for Manufacturing and Industrial Companies Joseph Berk, 2010-02-22 Focuses on rapid implementation of practical, real-world cost reduction solutions In today's economic climate, the need to cut costs can be the difference between success and failure. Cost Reduction and Optimization for Manufacturing and Industrial Companies covers all major cost reduction areas, providing easy to read examples and advice on steps to take. It provides the roadmap for implementing recommended actions with true and tried methods by taking a modern, all-inclusive look at manufacturing processes. Based on the author's cost reduction experience gained during 30 years of senior operations and consulting engagements with hundreds of organizations, this book includes easy-to-understand and easy-to-implement cost reduction concepts organized into five general areas --labor, material, design, process, and overhead. Each chapter: Dives into a cost reduction area and starts with the bottom line first by summarizing key points Provides proven tactics for cutting costs without a lot of extraneous data Follows a qualitative and design-oriented approach Emphasizes quick implementation and measurable cost reduction Identifies who in the organization should do the work Outlines risks and suggested risk mitigation actions Contains numerous tables, graphs, and photos to show the concepts described in the book Praise for Cost Reduction and Optimization for Manufacturing and Industrial Companies In this introductory book, Berk not only takes a modern, all-inclusive look at manufacturing processes but also provides substantial coverage of engineering materials and production systems. It follows a more qualitative and design-oriented approach than other texts in the market, helping readers gain a better understanding of important concepts. They'll also discover how micro-economic conditions relate to the process variables in a given process as well as how to perform manufacturing science and quantitative engineering analysis of manufacturing processes. —Fred Silverman, Director Engineering of Hi-Shear Technology Corporation Joe Berk has created a unique, practical and straightforward approach to cost reduction in manufacturing. This work provides valuable insights and concrete techniques, based on real-world experiences, to any manufacturing organization undertaking change to position itself to compete successfully in the global marketplace. —Joe Carleone, President and COO of American Pacific Corporation Check out author Joseph Berk's blog at http://manufacturingtraining.wordpress.com/ |
minimize and optimize book: Infinite-Dimensional Optimization and Convexity Ivar Ekeland, Thomas Turnbull, 1983-09-15 The caratheodory approach; Infinite-dimensional optimization; Duality theory. |
minimize and optimize book: Step-By-Step Optimization With Excel Solver - The Excel Statistical Master Mark Harmon, 2012-04-01 For anyone who wants to be operating at a high level with the Excel Solver quickly, this is the book for you. Step-By-Step Optimization With Excel Solver is more than 200+ pages of simple yet thorough explanations on how to use the Excel Solver to solve today's most widely known optimization problems. Loaded with screen shots that are coupled with easy-to-follow instructions, this book will simplify many difficult optimization problems and make you a master of the Excel Solver almost immediately. Here are just some of the Solver optimization problems that are solved completely with simple-to-understand instructions and screen shots in this book: The famous Traveling Salesman problem using Solver's Alldifferent constraint and the Solver's Evolutionary method to find the shortest path to reach all customers. This also provides an advanced use of the Excel INDEX function. The well-known Knapsack Problem which shows how optimize the use of limited space while satisfying numerous other criteria. How to perform nonlinear regression and curve-fitting on the Solver using the Solver's GRG Nonlinear solving method. How to solve the Cutting Stock Problem faced by many manufacturing companies who are trying to determine the optimal way to cut sheets of material to minimize waste while satisfying customer orders. Portfolio optimization to maximize return or minimize risk. Venture capital investment selection using the Solver's Binary constraint to maximize Net Present Value of selected cash flows at year 0. Clever use of the If-Then-Else statements makes this a simple problem. How use Solver to minimize the total cost of purchasing and shipping goods from multiple suppliers to multiple locations. How to optimize the selection of different production machine to minimize cost while fulfilling an order. How to optimally allocate a marketing budget to generate the greatest reach and frequency or number of inbound leads at the lowest cost. Step-By-Step Optimization With Excel Solver has complete instructions and numerous tips on every aspect of operating the Excel Solver. You'll fully understand the reports and know exactly how to tweek all of the Solver's settings for total custom use. The book also provides lots of inside advice and guidance on setting up the model in Excel so that it will be as simple and intuitive as possible to work with. All of the optimization problems in this book are solved step-by-step using a 6-step process that works every time. In addition to detailed screen shots and easy-to-follow explanations on how to solve every optimization problem in the book, a link is provided to download an Excel workbook that has all problems completed exactly as they are in this book. Step-By-Step Optimization With Excel Solver is exactly the book you need if you want to be optimizing at an advanced level with the Excel Solver quickly. |
minimize and optimize book: Introduction to Nonlinear Optimization Amir Beck, 2023-06-29 Built on the framework of the successful first edition, this book serves as a modern introduction to the field of optimization. The author’s objective is to provide the foundations of theory and algorithms of nonlinear optimization as well as to present a variety of applications from diverse areas of applied sciences. Introduction to Nonlinear Optimization gradually yet rigorously builds connections between theory, algorithms, applications, and actual implementation. The book contains several topics not typically included in optimization books, such as optimality conditions in sparsity constrained optimization, hidden convexity, and total least squares. Readers will discover a wide array of applications such as circle fitting, Chebyshev center, the Fermat–Weber problem, denoising, clustering, total least squares, and orthogonal regression. These applications are studied both theoretically and algorithmically, illustrating concepts such as duality. Python and MATLAB programs are used to show how the theory can be implemented. The extremely popular CVX toolbox (MATLAB) and CVXPY module (Python) are described and used. More than 250 theoretical, algorithmic, and numerical exercises enhance the reader's understanding of the topics. (More than 70 of the exercises provide detailed solutions, and many others are provided with final answers.) The theoretical and algorithmic topics are illustrated by Python and MATLAB examples. This book is intended for graduate or advanced undergraduate students in mathematics, computer science, electrical engineering, and potentially other engineering disciplines. |
minimize and optimize book: Practical Mathematical Optimization Jan Snyman, 2005-11-29 This book presents basic optimization principles and gradient-based algorithms to a general audience, in a brief and easy-to-read form. It enables professionals to apply optimization theory to engineering, physics, chemistry, or business economics. |
minimize and optimize book: Digital Minimalism Cal Newport, 2019-02-05 A New York Times, Wall Street Journal, Publishers Weekly, and USA Today bestseller Newport is making a bid to be the Marie Kondo of technology: someone with an actual plan for helping you realize the digital pursuits that do, and don't, bring value to your life.--Ezra Klein, Vox Minimalism is the art of knowing how much is just enough. Digital minimalism applies this idea to our personal technology. It's the key to living a focused life in an increasingly noisy world. In this timely and enlightening book, the bestselling author of Deep Work introduces a philosophy for technology use that has already improved countless lives. Digital minimalists are all around us. They're the calm, happy people who can hold long conversations without furtive glances at their phones. They can get lost in a good book, a woodworking project, or a leisurely morning run. They can have fun with friends and family without the obsessive urge to document the experience. They stay informed about the news of the day, but don't feel overwhelmed by it. They don't experience fear of missing out because they already know which activities provide them meaning and satisfaction. Now, Newport gives us a name for this quiet movement, and makes a persuasive case for its urgency in our tech-saturated world. Common sense tips, like turning off notifications, or occasional rituals like observing a digital sabbath, don't go far enough in helping us take back control of our technological lives, and attempts to unplug completely are complicated by the demands of family, friends and work. What we need instead is a thoughtful method to decide what tools to use, for what purposes, and under what conditions. Drawing on a diverse array of real-life examples, from Amish farmers to harried parents to Silicon Valley programmers, Newport identifies the common practices of digital minimalists and the ideas that underpin them. He shows how digital minimalists are rethinking their relationship to social media, rediscovering the pleasures of the offline world, and reconnecting with their inner selves through regular periods of solitude. He then shares strategies for integrating these practices into your life, starting with a thirty-day digital declutter process that has already helped thousands feel less overwhelmed and more in control. Technology is intrinsically neither good nor bad. The key is using it to support your goals and values, rather than letting it use you. This book shows the way. |
minimize and optimize book: Optimizing Project Management Te Wu, 2020-04-27 SHELVING GUIDE: Project Management This hands-on guide is written for project professionals seeking to find an optimized way of performing project management. It provides answers to such critical questions as: Why should an organization apply project management? What is the value of project management in the broader context of an organization? Is project management as successful as some advocates suggested or is it a waste of time and resources because of the many extensive and bureaucratic processes? Which project management approach should our project team adopt: predictive or adaptive, waterfall or rolling water, extreme programming or Scrum? This book aims to provide an optimized view of project management by balancing and blending competing methodologies (e.g., traditional versus Agile), lengthy methodologies and broad principles, processes and practices, and the need to understand versus the need to apply. It includes project management templates, an integrated case study illustrating how to apply tools and concepts, and a glossary of key terms. Optimizing Project Management is for both aspiring and practicing project management professionals. It covers the core concepts, practices, and skills that are useful for developing new ideas, planning activities, implementing projects, and conducting planning and controlling of schedule, budget, and scope. The text is particularly useful for students, project professionals wanting to refresh their knowledge, and those pursuing project management certifications. This book is aligned with common project management standards such as the Project Management Body of Knowledge and the ISO 21502: Project, Programme and Portfolio Management — Guidance on Project Management. |
minimize and optimize book: Engineering Optimization R. Russell Rhinehart, 2018-03-26 An Application-Oriented Introduction to Essential Optimization Concepts and Best Practices Optimization is an inherent human tendency that gained new life after the advent of calculus; now, as the world grows increasingly reliant on complex systems, optimization has become both more important and more challenging than ever before. Engineering Optimization provides a practically-focused introduction to modern engineering optimization best practices, covering fundamental analytical and numerical techniques throughout each stage of the optimization process. Although essential algorithms are explained in detail, the focus lies more in the human function: how to create an appropriate objective function, choose decision variables, identify and incorporate constraints, define convergence, and other critical issues that define the success or failure of an optimization project. Examples, exercises, and homework throughout reinforce the author’s “do, not study” approach to learning, underscoring the application-oriented discussion that provides a deep, generic understanding of the optimization process that can be applied to any field. Providing excellent reference for students or professionals, Engineering Optimization: Describes and develops a variety of algorithms, including gradient based (such as Newton’s, and Levenberg-Marquardt), direct search (such as Hooke-Jeeves, Leapfrogging, and Particle Swarm), along with surrogate functions for surface characterization Provides guidance on optimizer choice by application, and explains how to determine appropriate optimizer parameter values Details current best practices for critical stages of specifying an optimization procedure, including decision variables, defining constraints, and relationship modeling Provides access to software and Visual Basic macros for Excel on the companion website, along with solutions to examples presented in the book Clear explanations, explicit equation derivations, and practical examples make this book ideal for use as part of a class or self-study, assuming a basic understanding of statistics, calculus, computer programming, and engineering models. Anyone seeking best practices for “making the best choices” will find value in this introductory resource. |
minimize and optimize book: Optimization in Mechanics P. Brousse, 2013-10-22 Optimization in Mechanics: Problems and Methods investigates various problems and methods of optimization in mechanics. The subjects under study range from minimization of masses and stresses or displacements, to maximization of loads, vibration frequencies, and critical speeds of rotating shafts. Comprised of seven chapters, this book begins by presenting examples of optimization problems in mechanics and considering their application, as well as illustrating the usefulness of some optimizations like those of a reinforced shell, a robot, and a booster. The next chapter outlines some of the mathematical concepts that form the framework for optimization methods and techniques and demonstrates their efficiency in yielding relevant results. Subsequent chapters focus on the Kuhn Tucker theorem and duality, with proofs; associated problems and classical numerical methods of mathematical programming, including gradient and conjugate gradient methods; and techniques for dealing with large-scale problems. The book concludes by describing optimizations of discrete or continuous structures subject to dynamical effects. Mass minimization and fundamental eigenvalue problems as well as problems of minimization of some dynamical responses are studied. This monograph is written for students, engineers, scientists, and even self-taught individuals. |
minimize and optimize book: Lectures on Convex Optimization Yurii Nesterov, 2018-09-23 This book provides a comprehensive, modern introduction to convex optimization, a field that is becoming increasingly important in applied mathematics, economics and finance, engineering, and computer science, notably in data science and machine learning. Written by a leading expert in the field, this book includes recent advances in the algorithmic theory of convex optimization, naturally complementing the existing literature. It contains a unified and rigorous presentation of the acceleration techniques for minimization schemes of first- and second-order. It provides readers with a full treatment of the smoothing technique, which has tremendously extended the abilities of gradient-type methods. Several powerful approaches in structural optimization, including optimization in relative scale and polynomial-time interior-point methods, are also discussed in detail. Researchers in theoretical optimization as well as professionals working on optimization problems will find this book very useful. It presents many successful examples of how to develop very fast specialized minimization algorithms. Based on the author’s lectures, it can naturally serve as the basis for introductory and advanced courses in convex optimization for students in engineering, economics, computer science and mathematics. |
minimize and optimize book: Computational Optimization, Methods and Algorithms Slawomir Koziel, Xin-She Yang, 2011-06-17 Computational optimization is an important paradigm with a wide range of applications. In virtually all branches of engineering and industry, we almost always try to optimize something - whether to minimize the cost and energy consumption, or to maximize profits, outputs, performance and efficiency. In many cases, this search for optimality is challenging, either because of the high computational cost of evaluating objectives and constraints, or because of the nonlinearity, multimodality, discontinuity and uncertainty of the problem functions in the real-world systems. Another complication is that most problems are often NP-hard, that is, the solution time for finding the optimum increases exponentially with the problem size. The development of efficient algorithms and specialized techniques that address these difficulties is of primary importance for contemporary engineering, science and industry. This book consists of 12 self-contained chapters, contributed from worldwide experts who are working in these exciting areas. The book strives to review and discuss the latest developments concerning optimization and modelling with a focus on methods and algorithms for computational optimization. It also covers well-chosen, real-world applications in science, engineering and industry. Main topics include derivative-free optimization, multi-objective evolutionary algorithms, surrogate-based methods, maximum simulated likelihood estimation, support vector machines, and metaheuristic algorithms. Application case studies include aerodynamic shape optimization, microwave engineering, black-box optimization, classification, economics, inventory optimization and structural optimization. This graduate level book can serve as an excellent reference for lecturers, researchers and students in computational science, engineering and industry. |
minimize and optimize book: Introduction to Shape Optimization Jan Sokolowski, Jean-Paul Zolesio, 2012-12-06 This book is motivated largely by a desire to solve shape optimization prob lems that arise in applications, particularly in structural mechanics and in the optimal control of distributed parameter systems. Many such problems can be formulated as the minimization of functionals defined over a class of admissible domains. Shape optimization is quite indispensable in the design and construction of industrial structures. For example, aircraft and spacecraft have to satisfy, at the same time, very strict criteria on mechanical performance while weighing as little as possible. The shape optimization problem for such a structure consists in finding a geometry of the structure which minimizes a given functional (e. g. such as the weight of the structure) and yet simultaneously satisfies specific constraints (like thickness, strain energy, or displacement bounds). The geometry of the structure can be considered as a given domain in the three-dimensional Euclidean space. The domain is an open, bounded set whose topology is given, e. g. it may be simply or doubly connected. The boundary is smooth or piecewise smooth, so boundary value problems that are defined in the domain and associated with the classical partial differential equations of mathematical physics are well posed. In general the cost functional takes the form of an integral over the domain or its boundary where the integrand depends smoothly on the solution of a boundary value problem. |
minimize and optimize book: NASA Reference Publication , 1985 |
How to remove small yellow box with "Minimize"?
Jan 18, 2025 · Harassment is any behavior intended to disturb or upset a person or group of people. Threats include any threat of violence, or harm to another.
How to Minimize and Restore App Window in Windows 10
Nov 22, 2022 · How to Minimize and Restore App Window in Windows 10 Minimize allows you to hide a window without closing it to the taskbar until you Restore it. Restore will show the …
How do I set not to close Outlook completely? - Microsoft …
Nov 21, 2024 · 1. Minimize to the System Tray: Find the Outlook Icon in the System Tray: Open Outlook, and ensure it is running. Look for the small Outlook icon in the system tray (bottom …
73 Keyboard Shortcuts in Windows - Microsoft Community
Oct 1, 2024 · Windows key + M: Minimize all open windows. Windows key + Shift + M: Restore minimized windows. Windows key + Home: Minimize all windows except the selected or …
Outlook automatically closes when minimized - Microsoft Community
5 days ago · Recently whenever I minimize Outlook, the program automatically closes. I have made not changes so I don't know what happened. Was there an update that affected this? Is …
I would like to Disable Windows' "Shake to Minimize" function …
Oct 12, 2022 · I find the "Shake to Minimize" function bothersome. Often times, I have 10 different windows open, that I am actively using, and they seemingly minimize "without a reason" until I …
Minimise or minimize | Learn English - Preply
Minimize means to reduce to the smallest possible amount, to estimate to the least possible degree, to belittle or represent as worth less than is actually true. Minimize is a transitive verb …
How to minimize programs in Windows 10 - Ten Forums
May 18, 2016 · I dont want to minimize all programs, or show the entire desktop... I want to be able to minimize programs one at a time, like I could in all the other windows. And some of my …
How to fix browser getting minimized from full screen when alt …
Jun 30, 2022 · Hi, Thanks for your post in Microsoft Community. I understand that you have encountered the problem of minimizing the Edge window when using Alt+Tab to switch to the …
In Windows 10, how do I Minimize all open windows to view the …
Sep 24, 2015 · I need this shortcut to minimize all open windows to view the desktop. Also note, before I post here, I do research the answers online, however, there is almost no help for …
How to remove small yellow box with "Minimize"?
Jan 18, 2025 · Harassment is any behavior intended to disturb or upset a person or group of people. Threats include any threat of violence, or harm to another.
How to Minimize and Restore App Window in Windows 10
Nov 22, 2022 · How to Minimize and Restore App Window in Windows 10 Minimize allows you to hide a window without closing it to the taskbar until you Restore it. Restore will show the …
How do I set not to close Outlook completely? - Microsoft …
Nov 21, 2024 · 1. Minimize to the System Tray: Find the Outlook Icon in the System Tray: Open Outlook, and ensure it is running. Look for the small Outlook icon in the system tray (bottom …
73 Keyboard Shortcuts in Windows - Microsoft Community
Oct 1, 2024 · Windows key + M: Minimize all open windows. Windows key + Shift + M: Restore minimized windows. Windows key + Home: Minimize all windows except the selected or …
Outlook automatically closes when minimized - Microsoft …
5 days ago · Recently whenever I minimize Outlook, the program automatically closes. I have made not changes so I don't know what happened. Was there an update that affected this? Is …
I would like to Disable Windows' "Shake to Minimize" function …
Oct 12, 2022 · I find the "Shake to Minimize" function bothersome. Often times, I have 10 different windows open, that I am actively using, and they seemingly minimize "without a reason" until I …
Minimise or minimize | Learn English - Preply
Minimize means to reduce to the smallest possible amount, to estimate to the least possible degree, to belittle or represent as worth less than is actually true. Minimize is a transitive verb …
How to minimize programs in Windows 10 - Ten Forums
May 18, 2016 · I dont want to minimize all programs, or show the entire desktop... I want to be able to minimize programs one at a time, like I could in all the other windows. And some of my …
How to fix browser getting minimized from full screen when alt …
Jun 30, 2022 · Hi, Thanks for your post in Microsoft Community. I understand that you have encountered the problem of minimizing the Edge window when using Alt+Tab to switch to the …
In Windows 10, how do I Minimize all open windows to view the …
Sep 24, 2015 · I need this shortcut to minimize all open windows to view the desktop. Also note, before I post here, I do research the answers online, however, there is almost no help for …