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| linear programming matlab code: Linear Programming with MATLAB Michael C. Ferris, Olvi L. Mangasarian, Stephen J. Wright, 2007-01-01 A self-contained introduction to linear programming using MATLAB® software to elucidate the development of algorithms and theory. Exercises are included in each chapter, and additional information is provided in two appendices and an accompanying Web site. Only a basic knowledge of linear algebra and calculus is required. |
| linear programming matlab code: Linear Programming Using MATLAB® Nikolaos Ploskas, Nikolaos Samaras, 2017-10-28 This book offers a theoretical and computational presentation of a variety of linear programming algorithms and methods with an emphasis on the revised simplex method and its components. A theoretical background and mathematical formulation is included for each algorithm as well as comprehensive numerical examples and corresponding MATLAB® code. The MATLAB® implementations presented in this book are sophisticated and allow users to find solutions to large-scale benchmark linear programs. Each algorithm is followed by a computational study on benchmark problems that analyze the computational behavior of the presented algorithms. As a solid companion to existing algorithmic-specific literature, this book will be useful to researchers, scientists, mathematical programmers, and students with a basic knowledge of linear algebra and calculus. The clear presentation enables the reader to understand and utilize all components of simplex-type methods, such as presolve techniques, scaling techniques, pivoting rules, basis update methods, and sensitivity analysis. |
| linear programming matlab code: Mixed Integer Nonlinear Programming Jon Lee, Sven Leyffer, 2011-12-02 Many engineering, operations, and scientific applications include a mixture of discrete and continuous decision variables and nonlinear relationships involving the decision variables that have a pronounced effect on the set of feasible and optimal solutions. Mixed-integer nonlinear programming (MINLP) problems combine the numerical difficulties of handling nonlinear functions with the challenge of optimizing in the context of nonconvex functions and discrete variables. MINLP is one of the most flexible modeling paradigms available for optimization; but because its scope is so broad, in the most general cases it is hopelessly intractable. Nonetheless, an expanding body of researchers and practitioners — including chemical engineers, operations researchers, industrial engineers, mechanical engineers, economists, statisticians, computer scientists, operations managers, and mathematical programmers — are interested in solving large-scale MINLP instances. |
| linear programming matlab code: Applied Optimization with MATLAB Programming P. Venkataraman, 2009-03-23 Technology/Engineering/Mechanical Provides all the tools needed to begin solving optimization problems using MATLAB® The Second Edition of Applied Optimization with MATLAB® Programming enables readers to harness all the features of MATLAB® to solve optimization problems using a variety of linear and nonlinear design optimization techniques. By breaking down complex mathematical concepts into simple ideas and offering plenty of easy-to-follow examples, this text is an ideal introduction to the field. Examples come from all engineering disciplines as well as science, economics, operations research, and mathematics, helping readers understand how to apply optimization techniques to solve actual problems. This Second Edition has been thoroughly revised, incorporating current optimization techniques as well as the improved MATLAB® tools. Two important new features of the text are: Introduction to the scan and zoom method, providing a simple, effective technique that works for unconstrained, constrained, and global optimization problems New chapter, Hybrid Mathematics: An Application, using examples to illustrate how optimization can develop analytical or explicit solutions to differential systems and data-fitting problems Each chapter ends with a set of problems that give readers an opportunity to put their new skills into practice. Almost all of the numerical techniques covered in the text are supported by MATLAB® code, which readers can download on the text's companion Web site www.wiley.com/go/venkat2e and use to begin solving problems on their own. This text is recommended for upper-level undergraduate and graduate students in all areas of engineering as well as other disciplines that use optimization techniques to solve design problems. |
| linear programming matlab code: Introduction to Linear Programming with MATLAB Shashi Kant Mishra, Bhagwat Ram, 2017-09-07 This book is based on the lecture notes of the author delivered to the students at the Institute of Science, Banaras Hindu University, India. It covers simplex, revised simplex, two-phase method, duality, dual simplex, complementary slackness, transportation and assignment problems with good number of examples, clear proofs, MATLAB codes and homework problems. The book will be useful for both students and practitioners. |
| linear programming matlab code: Applied Linear Algebra and Optimization Using MATLAB Rizwan Butt, 2011 |
| linear programming matlab code: Optimization in Practice with MATLAB Achille Messac, 2015-03-19 This textbook is designed for students and industry practitioners for a first course in optimization integrating MATLAB® software. |
| linear programming matlab code: Solving Optimization Problems with MATLAB® Dingyü Xue, 2020-04-06 This book focuses on solving optimization problems with MATLAB. Descriptions and solutions of nonlinear equations of any form are studied first. Focuses are made on the solutions of various types of optimization problems, including unconstrained and constrained optimizations, mixed integer, multiobjective and dynamic programming problems. Comparative studies and conclusions on intelligent global solvers are also provided. |
| linear programming matlab code: Robust Adaptive Dynamic Programming Yu Jiang, Zhong-Ping Jiang, 2017-04-25 A comprehensive look at state-of-the-art ADP theory and real-world applications This book fills a gap in the literature by providing a theoretical framework for integrating techniques from adaptive dynamic programming (ADP) and modern nonlinear control to address data-driven optimal control design challenges arising from both parametric and dynamic uncertainties. Traditional model-based approaches leave much to be desired when addressing the challenges posed by the ever-increasing complexity of real-world engineering systems. An alternative which has received much interest in recent years are biologically-inspired approaches, primarily RADP. Despite their growing popularity worldwide, until now books on ADP have focused nearly exclusively on analysis and design, with scant consideration given to how it can be applied to address robustness issues, a new challenge arising from dynamic uncertainties encountered in common engineering problems. Robust Adaptive Dynamic Programming zeros in on the practical concerns of engineers. The authors develop RADP theory from linear systems to partially-linear, large-scale, and completely nonlinear systems. They provide in-depth coverage of state-of-the-art applications in power systems, supplemented with numerous real-world examples implemented in MATLAB. They also explore fascinating reverse engineering topics, such how ADP theory can be applied to the study of the human brain and cognition. In addition, the book: Covers the latest developments in RADP theory and applications for solving a range of systems’ complexity problems Explores multiple real-world implementations in power systems with illustrative examples backed up by reusable MATLAB code and Simulink block sets Provides an overview of nonlinear control, machine learning, and dynamic control Features discussions of novel applications for RADP theory, including an entire chapter on how it can be used as a computational mechanism of human movement control Robust Adaptive Dynamic Programming is both a valuable working resource and an intriguing exploration of contemporary ADP theory and applications for practicing engineers and advanced students in systems theory, control engineering, computer science, and applied mathematics. |
| linear programming matlab code: Programming with MATLAB 2016 Huei-Huang Lee, 2016 This book is designed for undergraduate students, completely new to programming with MATLAB. Case studies and examples are used extensively throughout this book and are at the core of what makes this book so unique. The author believes that the best way to learn MATLAB is to study programs written by experienced programmers and that the quality of these example programs determines the quality of the book. The examples in this book are carefully designed to teach you MATLAB programming as well as to inspire within you your own problem solving potential. Most of the examples used in this book are designed to solve a whole class of problems, rather than a single, specific problem. A learn by doing teaching approach is used all through the book. You are guided to tackle a problem using MATLAB commands first and then the commands are explained line by line. This process of learning through hands on experience is one of the most efficient and pain-free ways of learning MATLAB. This approach, together with the extensive use of ordered textboxes, figures, and tables, greatly reduces the size of the book, while still providing you with a book that’s comprehensive and easy to follow. The first chapter of this book introduces the MATLAB programming environment and familiarizes you with MATLAB’s core functionality. Chapters two through nine discuss basic MATLAB functionalities in a progressive and comprehensive way. The chapters start out simple and build in complexity as you advance through the book. Chapters ten through thirteen cover advanced topics that are particularly useful in college programs. Each chapter consists of sections, each covering a topic and providing one or more examples. Related MATLAB functions are organized at the end of a section. Additional exercise problems are provided at the end of chapters two through nine. Examples in each section are presented in a consistent way. An example is usually described first, followed by a MATLAB script. Any resulting text and graphics output (and in some cases inputs) that are produced from running a script are presented and discussed. Finally, the remainder of each section is devoted to explaining the purpose of the lines of the script. |
| linear programming matlab code: A Guide to MATLAB Brian R. Hunt, Ronald L. Lipsman, Jonathan M. Rosenberg, 2001-08-06 This book is a short, focused introduction to MATLAB and should be useful to both beginning and experienced users. |
| linear programming matlab code: Programming Mathematics Using MATLAB Lisa A. Oberbroeckling, 2020-05-27 Providing an alternative to engineering-focused resources in the area, Programming Mathematics Using MATLAB® introduces the basics of programming and of using MATLAB® by highlighting many mathematical examples. Emphasizing mathematical concepts through the visualization of programming throughout the book, this useful resource utilizes examples that may be familiar to math students (such as numerical integration) and others that may be new (such as fractals). Additionally, the text uniquely offers a variety of MATLAB® projects, all of which have been class-tested thoroughly, and which enable students to put MATLAB® programming into practice while expanding their comprehension of concepts such as Taylor polynomials and the Gram-Schmidt process. Programming Mathematics Using MATLAB® is appropriate for readers familiar with sophomore-level mathematics (vectors, matrices, multivariable calculus), and is useful for math courses focused on MATLAB® specifically and those focused on mathematical concepts which seek to utilize MATLAB® in the classroom. |
| linear programming matlab code: Numerical Computing with MATLAB Cleve B. Moler, 2010-08-12 A revised textbook for introductory courses in numerical methods, MATLAB and technical computing, which emphasises the use of mathematical software. |
| linear programming matlab code: Programming and Engineering Computing with MATLAB 2020 Huei-Huang Lee, 2020-10 This book is designed for undergraduate students completely new to programming with MATLAB. Case studies and examples are used extensively throughout this book and are at the core of what makes this book so unique. The author believes that the best way to learn MATLAB is to study programs written by experienced programmers and that the quality of these example programs determines the quality of the book. The examples in this book are carefully designed to teach you MATLAB programming as well as to inspire within you your own problem solving potential. Most of the examples used in this book are designed to solve a whole class of problems, rather than a single, specific problem. A learn by doing teaching approach is used all through the book. You are guided to tackle a problem using MATLAB commands first and then the commands are explained line by line. This process of learning through hands on experience is one of the most efficient and pain-free ways of learning MATLAB. This approach, together with the extensive use of ordered textboxes, figures, and tables, greatly reduces the size of the book, while still providing you with a book that’s comprehensive and easy to follow. The first chapter of this book introduces the MATLAB programming environment and familiarizes you with MATLAB’s core functionality. Chapters two through nine discuss basic MATLAB functionalities in a progressive and comprehensive way. The chapters start out simple and build in complexity as you advance through the book. Chapters ten through thirteen cover advanced topics that are particularly useful in college programs. Each chapter consists of sections, each covering a topic and providing one or more examples. Related MATLAB functions are organized at the end of a section. Additional exercise problems are provided at the end of chapters two through nine. Examples in each section are presented in a consistent way. An example is usually described first, followed by a MATLAB script. Any resulting text and graphics output (and in some cases inputs) that are produced from running a script are presented and discussed. Finally, the remainder of each section is devoted to explaining the purpose of the lines of the script. Who this book is for This book is developed mainly for undergraduate engineering students. It may be used in courses such as Computers in Engineering, or others that use MATLAB as a software platform. It can also be used as a self-study book for learning MATLAB. College level engineering examples are used in this book. Background knowledge for these engineering examples is illustrated as thoroughly as possible. |
| linear programming matlab code: Metaheuristics for Portfolio Optimization G. A. Vijayalakshmi Pai, 2017-12-27 The book is a monograph in the cross disciplinary area of Computational Intelligence in Finance and elucidates a collection of practical and strategic Portfolio Optimization models in Finance, that employ Metaheuristics for their effective solutions and demonstrates the results using MATLAB implementations, over live portfolios invested across global stock universes. The book has been structured in such a way that, even novices in finance or metaheuristics should be able to comprehend and work on the hybrid models discussed in the book. |
| linear programming matlab code: Primal-dual Interior-Point Methods Stephen J. Wright, 1997-01-01 In the past decade, primal-dual algorithms have emerged as the most important and useful algorithms from the interior-point class. This book presents the major primal-dual algorithms for linear programming in straightforward terms. A thorough description of the theoretical properties of these methods is given, as are a discussion of practical and computational aspects and a summary of current software. This is an excellent, timely, and well-written work. The major primal-dual algorithms covered in this book are path-following algorithms (short- and long-step, predictor-corrector), potential-reduction algorithms, and infeasible-interior-point algorithms. A unified treatment of superlinear convergence, finite termination, and detection of infeasible problems is presented. Issues relevant to practical implementation are also discussed, including sparse linear algebra and a complete specification of Mehrotra's predictor-corrector algorithm. Also treated are extensions of primal-dual algorithms to more general problems such as monotone complementarity, semidefinite programming, and general convex programming problems. |
| linear programming matlab code: Matlab Dorothy C. Attaway, 2013-06-03 MatLab, Third Edition is the only book that gives a full introduction to programming in MATLAB combined with an explanation of the software's powerful functions, enabling engineers to fully exploit its extensive capabilities in solving engineering problems. The book provides a systematic, step-by-step approach, building on concepts throughout the text, facilitating easier learning. Sections on common pitfalls and programming guidelines direct students towards best practice. The book is organized into 14 chapters, starting with programming concepts such as variables, assignments, input/output, and selection statements; moves onto loops; and then solves problems using both the 'programming concept' and the 'power of MATLAB' side-by-side. In-depth coverage is given to input/output, a topic that is fundamental to many engineering applications. Vectorized Code has been made into its own chapter, in order to emphasize the importance of using MATLAB efficiently. There are also expanded examples on low-level file input functions, Graphical User Interfaces, and use of MATLAB Version R2012b; modified and new end-of-chapter exercises; improved labeling of plots; and improved standards for variable names and documentation. This book will be a valuable resource for engineers learning to program and model in MATLAB, as well as for undergraduates in engineering and science taking a course that uses (or recommends) MATLAB. - Presents programming concepts and MATLAB built-in functions side-by-side - Systematic, step-by-step approach, building on concepts throughout the book, facilitating easier learning - Sections on common pitfalls and programming guidelines direct students towards best practice |
| linear programming matlab code: Feasibility and Infeasibility in Optimization: John W. Chinneck, 2007-10-25 Written by a world leader in the field and aimed at researchers in applied and engineering sciences, this brilliant text has as its main goal imparting an understanding of the methods so that practitioners can make immediate use of existing algorithms and software, and so that researchers can extend the state of the art and find new applications. It includes algorithms on seeking feasibility and analyzing infeasibility, as well as describing new and surprising applications. |
| linear programming matlab code: Introduction to Linear Optimization and Extensions with MATLAB® Roy H. Kwon, 2013-09-05 Filling the need for an introductory book on linear programming that discusses the important ways to mitigate parameter uncertainty, Introduction to Linear Optimization and Extensions with MATLAB® provides a concrete and intuitive yet rigorous introduction to modern linear optimization. In addition to fundamental topics, the book discusses current linear optimization technologies such as predictor-path following interior point methods for both linear and quadratic optimization as well as the inclusion of linear optimization of uncertainty i.e. stochastic programming with recourse and robust optimization. The author introduces both stochastic programming and robust optimization as frameworks to deal with parameter uncertainty. The author’s unusual approach—developing these topics in an introductory book—highlights their importance. Since most applications require decisions to be made in the face of uncertainty, the early introduction of these topics facilitates decision making in real world environments. The author also includes applications and case studies from finance and supply chain management that involve the use of MATLAB. Even though there are several LP texts in the marketplace, most do not cover data uncertainty using stochastic programming and robust optimization techniques. Most emphasize the use of MS Excel, while this book uses MATLAB which is the primary tool of many engineers, including financial engineers. The book focuses on state-of-the-art methods for dealing with parameter uncertainty in linear programming, rigorously developing theory and methods. But more importantly, the author’s meticulous attention to developing intuition before presenting theory makes the material come alive. |
| linear programming matlab code: Understanding and Using Linear Programming Jiri Matousek, Bernd Gärtner, 2006-10-05 The book is an introductory textbook mainly for students of computer science and mathematics. Our guiding phrase is what every theoretical computer scientist should know about linear programming. A major focus is on applications of linear programming, both in practice and in theory. The book is concise, but at the same time, the main results are covered with complete proofs and in sufficient detail, ready for presentation in class. The book does not require more prerequisites than basic linear algebra, which is summarized in an appendix. One of its main goals is to help the reader to see linear programming behind the scenes. |
| linear programming matlab code: Linear Programming Robert J Vanderbei, 2013-07-16 This Fourth Edition introduces the latest theory and applications in optimization. It emphasizes constrained optimization, beginning with a substantial treatment of linear programming and then proceeding to convex analysis, network flows, integer programming, quadratic programming, and convex optimization. Readers will discover a host of practical business applications as well as non-business applications. Topics are clearly developed with many numerical examples worked out in detail. Specific examples and concrete algorithms precede more abstract topics. With its focus on solving practical problems, the book features free C programs to implement the major algorithms covered, including the two-phase simplex method, primal-dual simplex method, path-following interior-point method, and homogeneous self-dual methods. In addition, the author provides online JAVA applets that illustrate various pivot rules and variants of the simplex method, both for linear programming and for network flows. These C programs and JAVA tools can be found on the book's website. The website also includes new online instructional tools and exercises. |
| linear programming matlab code: Computer Programming with MATLAB J. Michael Fitzpatrick, Ákos Lédeczi, 2015 |
| linear programming matlab code: Nonnegative Matrix Factorization Nicolas Gillis, 2020-12-18 Nonnegative matrix factorization (NMF) in its modern form has become a standard tool in the analysis of high-dimensional data sets. This book provides a comprehensive and up-to-date account of the most important aspects of the NMF problem and is the first to detail its theoretical aspects, including geometric interpretation, nonnegative rank, complexity, and uniqueness. It explains why understanding these theoretical insights is key to using this computational tool effectively and meaningfully. Nonnegative Matrix Factorization is accessible to a wide audience and is ideal for anyone interested in the workings of NMF. It discusses some new results on the nonnegative rank and the identifiability of NMF and makes available MATLAB codes for readers to run the numerical examples presented in the book. Graduate students starting to work on NMF and researchers interested in better understanding the NMF problem and how they can use it will find this book useful. It can be used in advanced undergraduate and graduate-level courses on numerical linear algebra and on advanced topics in numerical linear algebra and requires only a basic knowledge of linear algebra and optimization. |
| linear programming matlab code: GPU Programming in MATLAB Nikolaos Ploskas, Nikolaos Samaras, 2016-08-25 GPU programming in MATLAB is intended for scientists, engineers, or students who develop or maintain applications in MATLAB and would like to accelerate their codes using GPU programming without losing the many benefits of MATLAB. The book starts with coverage of the Parallel Computing Toolbox and other MATLAB toolboxes for GPU computing, which allow applications to be ported straightforwardly onto GPUs without extensive knowledge of GPU programming. The next part covers built-in, GPU-enabled features of MATLAB, including options to leverage GPUs across multicore or different computer systems. Finally, advanced material includes CUDA code in MATLAB and optimizing existing GPU applications. Throughout the book, examples and source codes illustrate every concept so that readers can immediately apply them to their own development. - Provides in-depth, comprehensive coverage of GPUs with MATLAB, including the parallel computing toolbox and built-in features for other MATLAB toolboxes - Explains how to accelerate computationally heavy applications in MATLAB without the need to re-write them in another language - Presents case studies illustrating key concepts across multiple fields - Includes source code, sample datasets, and lecture slides |
| linear programming matlab code: Chemical Production Scheduling Christos Maravelias, 2021-05-06 Understand common scheduling as well as other advanced operational problems with this valuable reference from a recognized leader in the field. Beginning with basic principles and an overview of linear and mixed-integer programming, this unified treatment introduces the fundamental ideas underpinning most modeling approaches, and will allow you to easily develop your own models. With more than 150 figures, the basic concepts and ideas behind the development of different approaches are clearly illustrated. Addresses a wide range of problems arising in diverse industrial sectors, from oil and gas to fine chemicals, and from commodity chemicals to food manufacturing. A perfect resource for engineering and computer science students, researchers working in the area, and industrial practitioners. |
| linear programming matlab code: Practical Optimization Andreas Antoniou, Wu-Sheng Lu, 2007-12-14 Practical Optimization: Algorithms and Engineering Applications provides a hands-on treatment of the subject of optimization. A comprehensive set of problems and exercises makes the book suitable for use in one or two semesters of a first-year graduate course or an advanced undergraduate course. Each half of the book contains a full semester’s worth of complementary yet stand-alone material. The practical orientation of the topics chosen and a wealth of useful examples also make the book suitable for practitioners in the field. Advancements in the efficiency of digital computers and the evolution of reliable software for numerical computation during the past three decades have led to a rapid growth in the theory, methods, and algorithms of numerical optimization. This body of knowledge has motivated widespread applications of optimization methods in many disciplines, e.g., engineering, business, and science, and has subsequently led to problem solutions that were considered intractable not too long ago. |
| linear programming matlab code: Design Optimization using MATLAB and SOLIDWORKS Krishnan Suresh, 2021-04-29 A hands-on text integrating mathematics, numerics and applications of optimization, with MATLAB code illustrating every concept. |
| linear programming matlab code: Programming and Engineering Computing with MATLAB 2021 Huei-Huang Lee, 2021-09 This book is designed for undergraduate students completely new to programming with MATLAB. Case studies and examples are used extensively throughout this book and are at the core of what makes this book so unique. The author believes that the best way to learn MATLAB is to study programs written by experienced programmers and that the quality of these example programs determines the quality of the book. The examples in this book are carefully designed to teach you MATLAB programming as well as to inspire within you your own problem solving potential. Most of the examples used in this book are designed to solve a whole class of problems, rather than a single, specific problem. A learn by doing teaching approach is used all through the book. You are guided to tackle a problem using MATLAB commands first and then the commands are explained line by line. This process of learning through hands on experience is one of the most efficient and pain-free ways of learning MATLAB. This approach, together with the extensive use of ordered textboxes, figures, and tables, greatly reduces the size of the book, while still providing you with a book that’s comprehensive and easy to follow. The first chapter of this book introduces the MATLAB programming environment and familiarizes you with MATLAB’s core functionality. Chapters two through nine discuss basic MATLAB functionalities in a progressive and comprehensive way. The chapters start out simple and build in complexity as you advance through the book. Chapters ten through thirteen cover advanced topics that are particularly useful in college programs. Each chapter consists of sections, each covering a topic and providing one or more examples. Related MATLAB functions are organized at the end of a section. Additional exercise problems are provided at the end of chapters two through nine. Examples in each section are presented in a consistent way. An example is usually described first, followed by a MATLAB script. Any resulting text and graphics output (and in some cases inputs) that are produced from running a script are presented and discussed. Finally, the remainder of each section is devoted to explaining the purpose of the lines of the script. Who this book is for This book is developed mainly for undergraduate engineering students. It may be used in courses such as Computers in Engineering, or others that use MATLAB as a software platform. It can also be used as a self-study book for learning MATLAB. College level engineering examples are used in this book. Background knowledge for these engineering examples is illustrated as thoroughly as possible. |
| linear programming matlab code: Getting Started with MATLAB 7 Rudra Pratap, 2006 MATLAB is one of the most widely used tools in the field of engineering today. Its broad appeal lies in its interactive environment with hundreds of built-in functions. This book is designed to get you up and running in just a few hours. |
| linear programming matlab code: Aise MATLAB Programming for Engineers Stephen Chapman, 2014-05-20 |
| linear programming matlab code: Python for MATLAB Development Albert Danial, 2022-03-26 MATLAB can run Python code! Python for MATLAB Development shows you how to enhance MATLAB with Python solutions to a vast array of computational problems in science, engineering, optimization, statistics, finance, and simulation. It is three books in one: A thorough Python tutorial that leverages your existing MATLAB knowledge with a comprehensive collection of MATLAB/Python equivalent expressions A reference guide to setting up and managing a Python environment that integrates cleanly with MATLAB A collection of recipes that demonstrate Python solutions invoked directly from MATLAB This book shows how to call Python functions to enhance MATLAB's capabilities. Specifically, you'll see how Python helps MATLAB: Run faster with numba Distribute work to a compute cluster with dask Find symbolic solutions to integrals, derivatives, and series summations with SymPy Overlay data on maps with Cartopy Solve mixed-integer linear programming problems with PuLP Interact with Redis via pyredis, PostgreSQL via psycopg2, and MongoDB via pymongo Read and write file formats that are not natively understood by MATLAB, such as SQLite, YAML, and ini Who This Book Is For MATLAB developers who are new to Python and other developers with some prior experience with MATLAB, R, IDL, or Mathematica. |
| linear programming matlab code: Interior Point Algorithms Yinyu Ye, 2011-10-11 The first comprehensive review of the theory and practice of one oftoday's most powerful optimization techniques. The explosive growth of research into and development of interiorpoint algorithms over the past two decades has significantlyimproved the complexity of linear programming and yielded some oftoday's most sophisticated computing techniques. This book offers acomprehensive and thorough treatment of the theory, analysis, andimplementation of this powerful computational tool. Interior Point Algorithms provides detailed coverage of all basicand advanced aspects of the subject. Beginning with an overview offundamental mathematical procedures, Professor Yinyu Ye movesswiftly on to in-depth explorations of numerous computationalproblems and the algorithms that have been developed to solve them.An indispensable text/reference for students and researchers inapplied mathematics, computer science, operations research,management science, and engineering, Interior Point Algorithms: * Derives various complexity results for linear and convexprogramming * Emphasizes interior point geometry and potential theory * Covers state-of-the-art results for extension, implementation,and other cutting-edge computational techniques * Explores the hottest new research topics, including nonlinearprogramming and nonconvex optimization. |
| linear programming matlab code: Programming for Computations - MATLAB/Octave Svein Linge, Hans Petter Langtangen, 2016-07-28 This book presents computer programming as a key method for solving mathematical problems. There are two versions of the book, one for MATLAB and one for Python. The book was inspired by the Springer book TCSE 6: A Primer on Scientific Programming with Python (by Langtangen), but the style is more accessible and concise, in keeping with the needs of engineering students. The book outlines the shortest possible path from no previous experience with programming to a set of skills that allows the students to write simple programs for solving common mathematical problems with numerical methods in engineering and science courses. The emphasis is on generic algorithms, clean design of programs, use of functions, and automatic tests for verification. |
| linear programming matlab code: MATLAB Control Systems Engineering Cesar Lopez, 2014-09-22 MATLAB is a high-level language and environment for numerical computation, visualization, and programming. Using MATLAB, you can analyze data, develop algorithms, and create models and applications. The language, tools, and built-in math functions enable you to explore multiple approaches and reach a solution faster than with spreadsheets or traditional programming languages, such as C/C++ or Java. MATLAB Control Systems Engineering introduces you to the MATLAB language with practical hands-on instructions and results, allowing you to quickly achieve your goals. In addition to giving an introduction to the MATLAB environment and MATLAB programming, this book provides all the material needed to design and analyze control systems using MATLAB’s specialized Control Systems Toolbox. The Control Systems Toolbox offers an extensive range of tools for classical and modern control design. Using these tools you can create models of linear time-invariant systems in transfer function, zero-pole-gain or state space format. You can manipulate both discrete-time and continuous-time systems and convert between various representations. You can calculate and graph time response, frequency response and loci of roots. Other functions allow you to perform pole placement, optimal control and estimates. The Control System Toolbox is open and extendible, allowing you to create customized M-files to suit your specific applications. |
| linear programming matlab code: Differential Equations and Linear Algebra Gilbert Strang, 2015-02-12 Differential equations and linear algebra are two central topics in the undergraduate mathematics curriculum. This innovative textbook allows the two subjects to be developed either separately or together, illuminating the connections between two fundamental topics, and giving increased flexibility to instructors. It can be used either as a semester-long course in differential equations, or as a one-year course in differential equations, linear algebra, and applications. Beginning with the basics of differential equations, it covers first and second order equations, graphical and numerical methods, and matrix equations. The book goes on to present the fundamentals of vector spaces, followed by eigenvalues and eigenvectors, positive definiteness, integral transform methods and applications to PDEs. The exposition illuminates the natural correspondence between solution methods for systems of equations in discrete and continuous settings. The topics draw on the physical sciences, engineering and economics, reflecting the author's distinguished career as an applied mathematician and expositor. |
| linear programming matlab code: Fundamentals of Optimization Techniques with Algorithms Sukanta Nayak, 2020-08-25 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. |
| linear programming matlab code: Systems Analysis for Sustainable Engineering: Theory and Applications Ni-Bin Chang, 2010-10-22 IMPLEMENT SYSTEMS ANALYSIS TOOLS IN SUSTAINABLE ENGINEERING Featuring a multidisciplinary approach, Systems Analysis for Sustainable Engineering: Theory and Applications provides a proven framework for applying systems analysis tools to account for environmental impacts, energy efficiency, cost-effectiveness, socioeconomic implications, and ecosystem health in engineering solutions. This pioneering work addresses the increased levels of sophistication embedded in many complex large-scale infrastructure systems and their interactions with the natural environment. After a detailed overview of sustainable systems engineering, the book covers mathematical theories of systems analysis, environmental resources management, industrial ecology, and sustainable design. Real-world examples highlight the methodologies presented in this authoritative resource. COVERAGE INCLUDES: Structured systems analysis for sustainable design Systems analysis and sustainable management strategies Ecomomic valuation, instruments, and project selection Statistical forecasting models Linear, nonlinear, integer, and dynamic programming models Multicriteria decision analyses System dynamics models and simulation analyses Water resources and quality management Air quality management Solid waste management Soil and groundwater remediation planning Industrial ecology and sustainability Green building and green infrastructure systems Energy resources management and energy systems engineering Land resources management and agricultural sustainability |
| linear programming matlab code: Numerical Methods with Worked Examples: Matlab Edition C. Woodford, C. Phillips, 2011-09-17 This book is for students following an introductory course in numerical methods, numerical techniques or numerical analysis. It introduces MATLAB as a computing environment for experimenting with numerical methods. It approaches the subject from a pragmatic viewpoint; theory is kept at a minimum commensurate with comprehensive coverage of the subject and it contains abundant worked examples which provide easy understanding through a clear and concise theoretical treatment. This edition places even greater emphasis on ‘learning by doing’ than the previous edition. Fully documented MATLAB code for the numerical methods described in the book will be available as supplementary material to the book on http://extras.springer.com |
| linear programming matlab code: Mathematical Methods and Algorithms for Signal Processing Todd K. Moon, Wynn C. Stirling, 2000 This previously included a CD. The CD contents can be accessed via World Wide Web. |
| linear programming matlab code: Optimization in Practice with MATLAB® Achille Messac, 2015-03-19 Optimization in Practice with MATLAB® provides a unique approach to optimization education. It is accessible to both junior and senior undergraduate and graduate students, as well as industry practitioners. It provides a strongly practical perspective that allows the student to be ready to use optimization in the workplace. It covers traditional materials, as well as important topics previously unavailable in optimization books (e.g. numerical essentials - for successful optimization). Written with both the reader and the instructor in mind, Optimization in Practice with MATLAB® provides practical applications of real-world problems using MATLAB®, with a suite of practical examples and exercises that help the students link the theoretical, the analytical, and the computational in each chapter. Additionally, supporting MATLAB® m-files are available for download via www.cambridge.org.messac. Lastly, adopting instructors will receive a comprehensive solution manual with solution codes along with lectures in PowerPoint with animations for each chapter, and the text's unique flexibility enables instructors to structure one- or two-semester courses. |
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Download the Linear app for desktop and mobile. Available for Mac, Windows, iOS, and Android.
Pricing – Linear
Use Linear for free with your whole team. Upgrade to enable unlimited issues, enhanced security controls, and additional features.
Customer Requests – Linear
Capture feedback across any customer interaction – from sales calls to support chats – and turn it into a customer request linked to a Linear project or …
Linear MCP server – Changelog
May 1, 2025 · Your AI models and agents can use our official MCP server to access your Linear data in a simple and secure way. Connect to our MCP …
Linear – Plan and build products
Linear is a purpose-built tool for modern product development. Streamline issues, projects, and product roadmaps.
Download Linear
Download the Linear app for desktop and mobile. Available for Mac, Windows, iOS, and Android.
Pricing – Linear
Use Linear for free with your whole team. Upgrade to enable unlimited issues, enhanced security controls, and additional features.
Customer Requests – Linear
Capture feedback across any customer interaction – from sales calls to support chats – and turn it into a customer request linked to a Linear project or issue.
Linear MCP server – Changelog
May 1, 2025 · Your AI models and agents can use our official MCP server to access your Linear data in a simple and secure way. Connect to our MCP server natively as a new Claude Integration , or …
About – Linear
Today, thousands of teams around the globe — from early-stage startups to public companies — use Linear to build their products. Linear helps them to focus on what they do best: Crafting …
Linear Method – Practices for building
The quality of a product is driven by both the talent of its creators and how they feel while they’re crafting it. To bring back the right focus, these are the foundational and evolving ideas Linear is …
Features – Linear
With its meticulous design, breathtaking speed and purpose-built workflows, Linear unlocks your team’s full potential. It is the tool of choice for high-performance teams to build products better.
Linear Customers
Linear Projects give Mercury a source-of-truth across all their work. Read story. Watershed switched to Linear to accelerate their team. Read story. Linear keeps Retool's teams customer …
Building our way - Linear Blog
Jun 10, 2025 · Linear is unique because it actually manages all these workflows in a structured way and in a single product, bringing teams together. We see this end-to-end workflow as the natural …
