Operations Research Optimization With Matlab Multiobjective Quadratic And Mixed Programming

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Operations Research. Optimization With Matlab. Multiobjective, Quadratic and Mixed Programming

Author : Perez C.
Publisher :
ISBN 13 : 9781974587209
Total Pages : 278 pages
Book Rating : 4.5/5 (872 download)

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Book Synopsis Operations Research. Optimization With Matlab. Multiobjective, Quadratic and Mixed Programming by : Perez C.

Download or read book Operations Research. Optimization With Matlab. Multiobjective, Quadratic and Mixed Programming written by Perez C. and published by . This book was released on 2017-08-16 with total page 278 pages. Available in PDF, EPUB and Kindle. Book excerpt: The generalization of optimization theory and techniques to other formulations comprises a large area of applied mathematics. Optimization includes finding "best available" values of some objective function given a defined domain (or input), including a variety of different types of objective functions and different types of domains.Adding more than one objective to an optimization problem adds complexity. For example, to optimize a structural design, one would desire a design that is both light and rigid. When two objectives conflict, a trade-off must be created. There may be one lightest design, one stiffest design, and an infinite number of designs that are some compromise of weight and rigidity. The set of trade-off designs that cannot be improved upon according to one criterion without hurting another criterion is known as the Pareto set. The curve created plotting weight against stiffness of the best designs is known as the Pareto frontier.A design is judged to be "Pareto optimal" (equivalently, "Pareto efficient" or in the Pareto set) if it is not dominated by any other design: If it is worse than another design in some respects and no better in any respect, then it is dominated and is not Pareto optimal. The choice among "Pareto optimal" solutions to determine the "favorite solution" is delegated to the decision maker. In other words, defining the problem as multi-objective optimization signals that some information is missing: desirable objectives are given but combinations of them are not rated relative to each other. In some cases, the missing information can be derived by interactive sessions with the decision maker.Multi-objective optimization problems have been generalized further into vector optimization problems where the (partial) ordering is no longer given by the Pareto ordering.Optimization problems are often multi-modal; that is, they possess multiple good solutions. They could all be globally good or there could be a mix of globally good and locally good solutions. Obtaining all (or at least some of) the multiple solutions is the goal of a multi-modal optimizer.Classical optimization techniques due to their iterative approach do not perform satisfactorily when they are used to obtain multiple solutions, since it is not guaranteed that different solutions will be obtained even with different starting points in multiple runs of the algorithm. Evolutionary algorithms, however, are a very popular approach to obtain multiple solutions in a multi-modal optimization task.This book develops the following topics:* "Multiobjective Optimization Algorithms" * "Using fminimax with a Simulink Model" * "Signal Processing Using fgoalattain" * "Generate and Plot a Pareto Front" * "Linear Programming Algorithms" * "Maximize Long-Term Investments Using Linear Programming" * "Mixed-Integer Linear Programming Algorithms" * "Tuning Integer Linear Programming" * "Mixed-Integer Linear Programming Basics" * "Optimal Dispatch of Power Generators" * "Mixed-Integer Quadratic Programming Portfolio Optimization" * "Quadratic Programming Algorithms"* "Quadratic Minimization with Bound Constraints" * "Quadratic Minimization with Dense, Structured Hessian"* "Large Sparse Quadratic Program with Interior Point Algorithm" * "Least-Squares (Model Fitting) Algorithms" * "lsqnonlin with a Simulink Model" * "Nonlinear Least Squares With and Without Jacobian" * "Linear Least Squares with Bound Constraints" * "Optimization App with the lsqlin Solver" * "Maximize Long-Term Investments Using Linear Programming" * "Jacobian Multiply Function with Linear Least Squares" * "Nonlinear Curve Fitting with lsqcurvefit" * "Fit a Model to Complex-Valued Data" * "Systems of Equations" * "Nonlinear Equations with Analytic Jacobian" * "Nonlinear Equations with Jacobian" * "Nonlinear Equations with Jacobian Sparsity Pattern"* "Nonlinear Systems with Constraints" * "Parallel Computing for Optimization"

Operations Research. Optimization Functions in Matlab for Linear and Nonlinear Programming

Author : Perez C.
Publisher :
ISBN 13 : 9781974588053
Total Pages : 328 pages
Book Rating : 4.5/5 (88 download)

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Book Synopsis Operations Research. Optimization Functions in Matlab for Linear and Nonlinear Programming by : Perez C.

Download or read book Operations Research. Optimization Functions in Matlab for Linear and Nonlinear Programming written by Perez C. and published by . This book was released on 2017-08-16 with total page 328 pages. Available in PDF, EPUB and Kindle. Book excerpt: In the simplest case, an optimization problem consists of maximizing or minimizing a real function by systematically choosing input values from within an allowed set and computing the value of the function. The generalization of optimization theory and techniques to other formulations comprises a large area of applied mathematics. More generally, optimization includes finding "best available" values of some objective function given a defined domain (or input), including a variety of different types of objective functions and different types of domains.MATLAB Optimization Toolbox provides functions for finding parameters that minimize or maximize objectives while satisfying constraints. The toolbox includes solvers for linear programming, mixed-integer linear programming, quadratic programming, nonlinear optimization, and nonlinear least squares. You can use these solvers to find optimal solutions to continuous and discrete problems, perform tradeoff analyses, and incorporate optimization methods into algorithms and applications.Adding more than one objective to an optimization problem adds complexity. For example, to optimize a structural design, one would desire a design that is both light and rigid. When two objectives conflict, a trade-off must be created. There may be one lightest design, one stiffest design, and an infinite number of designs that are some compromise of weight and rigidity. The set of trade-off designs that cannot be improved upon according to one criterion without hurting another criterion is known as the Pareto set. The curve created plotting weight against stiffness of the best designs is known as the Pareto frontier.Also MATLAB Optimization Toolbox provides functions for MULTIOBJECTIVE, QUADRATIC and MIXED DATA PROGRAMMING

Operations Research. Optimization With Matlab. Linear and Nonlinear Programming

Author : Perez C.
Publisher :
ISBN 13 : 9781974585328
Total Pages : 316 pages
Book Rating : 4.5/5 (853 download)

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Book Synopsis Operations Research. Optimization With Matlab. Linear and Nonlinear Programming by : Perez C.

Download or read book Operations Research. Optimization With Matlab. Linear and Nonlinear Programming written by Perez C. and published by . This book was released on 2017-08-16 with total page 316 pages. Available in PDF, EPUB and Kindle. Book excerpt: In mathematics, computer science and operations research, mathematical optimization, also spelled mathematical optimisation (alternatively named mathematical programming or simply optimization or optimisation), is the selection of a best element (with regard to some criterion) from some set of available alternatives.In the simplest case, an optimization problem consists of maximizing or minimizing a real function by systematically choosing input values from within an allowed set and computing the value of the function. The generalization of optimization theory and techniques to other formulations comprises a large area of applied mathematics. More generally, optimization includes finding "best available" values of some objective function given a defined domain (or input), including a variety of different types of objective functions and different types of domains.MATLAB Optimization Toolbox provides functions for finding parameters that minimize or maximize objectives while satisfying constraints. The toolbox includes solvers for linear programming, mixed-integer linear programming, quadratic programming, nonlinear optimization, and nonlinear least squares. You can use these solvers to find optimal solutions to continuous and discrete problems, perform tradeoff analyses, and incorporate optimization methods into algorithms and applications.This book develops the following topics:* "Linear Programming" * "Nonlinear Programming" * "Constrained Linear and Nonlinear Problem" * "Optimization Toolbox Solvers" * "Optimization Decision Table" * "fmincon Algorithms" * "fsolve Algorithms"* "fminunc Algorithms"* "Least Squares Algorithms"* "Linear Programming Algorithms"* "Quadratic Programming Algorithms"* "Large-Scale vs. Medium-Scale Algorithms"* "Potential Inaccuracy with Interior-Point Algorithms"* "Edit Optimization Parameters" * "Complex Numbers in Optimization Toolbox Solvers" * "Scalar Objective Functions" * "Vector and Matrix Objective Functions" * "Objective Functions for Linear or Quadratic Problems" * "Maximizing an Objective"* "Bound Constraints" * "Linear an Nonlinlear Constraints"* "optimoptions and optimset" * "Tolerances and Stopping Criteria"* "Checking Validity of Gradients or Jacobians"* "Iterations and Function Counts" * "First-Order Optimality Measure" * "Lagrange Multiplier Structures" * "Plot an Optimization During Execution" * "Local vs. Global Optima" * "Optimizing a Simulation or Ordinary Differential Equation"* "Optimization App" * "Nonlinear algorithms and examples"* "Unconstrained Nonlinear Optimization Algorithms" * "fminsearch Algorithm"* "fminunc Unconstrained Minimization"* "Minimization with Gradient and Hessian" * "Minimization with Gradient and Hessian Sparsity Pattern" * "Constrained Nonlinear Optimization Algorithms" * "Nonlinear Inequality Constraints" * "Nonlinear Constraints with Gradients" * "fmincon Interior-Point Algorithm with Analytic Hessian"* "Linear or Quadratic Objective with Quadratic Constraints" * "Nonlinear Equality and Inequality Constraints"* "Optimization App with the fmincon Solver" * "Minimization with Bound Constraints and Banded Preconditioner"* "Minimization with Linear Equality Constraints"* "Minimization with Dense Structured Hessian, Linear Equalities"* "One-Dimensional Semi-Infinite Constraints" * "Two-Dimensional Semi-Infinite Constraint"

Solving Optimization Problems with MATLAB®

Author : Dingyü Xue
Publisher : Walter de Gruyter GmbH & Co KG
ISBN 13 : 3110667010
Total Pages : 342 pages
Book Rating : 4.1/5 (16 download)

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Book Synopsis Solving Optimization Problems with MATLAB® by : Dingyü Xue

Download or read book Solving Optimization Problems with MATLAB® written by Dingyü Xue and published by Walter de Gruyter GmbH & Co KG. This book was released on 2020-04-06 with total page 342 pages. Available in PDF, EPUB and Kindle. Book excerpt: 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.

Introduction to Linear Optimization and Extensions with MATLAB

Author : Roy H. Kwon
Publisher : CRC Press
ISBN 13 : 1482204347
Total Pages : 356 pages
Book Rating : 4.4/5 (822 download)

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Book Synopsis Introduction to Linear Optimization and Extensions with MATLAB by : Roy H. Kwon

Download or read book Introduction to Linear Optimization and Extensions with MATLAB written by Roy H. Kwon and published by CRC Press. This book was released on 2013-09-05 with total page 356 pages. Available in PDF, EPUB and Kindle. Book excerpt: 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 l

MATLAB Optimization Techniques

Author : Cesar Lopez
Publisher : Apress
ISBN 13 : 1484202929
Total Pages : 284 pages
Book Rating : 4.4/5 (842 download)

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Book Synopsis MATLAB Optimization Techniques by : Cesar Lopez

Download or read book MATLAB Optimization Techniques written by Cesar Lopez and published by Apress. This book was released on 2014-11-12 with total page 284 pages. Available in PDF, EPUB and Kindle. Book excerpt: 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 Optimization Techniques introduces you to the MATLAB language with practical hands-on instructions and results, allowing you to quickly achieve your goals. It begins by introducing the MATLAB environment and the structure of MATLAB programming before moving on to the mathematics of optimization. The central part of the book is dedicated to MATLAB’s Optimization Toolbox, which implements state-of-the-art algorithms for solving multiobjective problems, non-linear minimization with boundary conditions and restrictions, minimax optimization, semi-infinitely constrained minimization and linear and quadratic programming. A wide range of exercises and examples are included, illustrating the most widely used optimization methods.

Solving Optimization Problems with MATLAB

Author : Dingyu Xue
Publisher : de Gruyter
ISBN 13 : 9783110663648
Total Pages : 10 pages
Book Rating : 4.6/5 (636 download)

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Book Synopsis Solving Optimization Problems with MATLAB by : Dingyu Xue

Download or read book Solving Optimization Problems with MATLAB written by Dingyu Xue and published by de Gruyter. This book was released on 2020-05-30 with total page 10 pages. Available in PDF, EPUB and Kindle. Book excerpt: The book focused on solving equations and optimization problems with MATLAB. The topics on unconstrained optimization, linear and quadratic programming, nonlinear constrained optimization problems, mixed integer programming, multi-objective programming, dynamic programming and intelligent optimization methods are covered. With extensive exercises, the book sets up a new viewpoint for the readers in understanding linear algebra problems.

OPTIMIZATION with MATLAB. NONLINEAR and MULTIOBJECTIVE OPTIMIZATION

Author : J Lopez
Publisher :
ISBN 13 : 9781099794346
Total Pages : 388 pages
Book Rating : 4.7/5 (943 download)

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Book Synopsis OPTIMIZATION with MATLAB. NONLINEAR and MULTIOBJECTIVE OPTIMIZATION by : J Lopez

Download or read book OPTIMIZATION with MATLAB. NONLINEAR and MULTIOBJECTIVE OPTIMIZATION written by J Lopez and published by . This book was released on 2019-05-22 with total page 388 pages. Available in PDF, EPUB and Kindle. Book excerpt: Optimization Toolbox provides functions for finding parameters that minimize or maximize objectives while satisfying constraints. The toolbox includes solvers for linear programming (LP), mixed-integer linear programming (MILP), quadratic programming(QP), nonlinear programming (NLP), constrained linear least squares, nonlinear least squares, and nonlinear equations. You can define your optimization problem with functions and matrices or by specifying variable expressions that reflect the underlying mathematics. You can use the toolbox solvers to fin optimal solutions to continuous and discrete problems, perform trade of analyses, and incorporate optimization methods into algorithms and applications. The toolbox lets you perform design optimization tasks, including parameter estimation, component selection, and parameter tuning. It can be used to fin optimal solutions in applications such as portfolio optimization, resource allocation, and production planning and scheduling.Key Features-Nonlinear and multiobjective optimization of smooth constrained and unconstrained problems-Solvers for nonlinear least squares, constrained linear least squares, data fitting and nonlinear equations-Quadratic programming (QP) and linear programming (LP)-Mixed-integer linear programming (MILP)-Optimization modeling tools-Graphical monitoring of optimization progress-Gradient estimation acceleration (with Parallel Computing Toolbox(TM))

Optimization Functions in MATLAB

Author : J Lopez
Publisher :
ISBN 13 : 9781080303083
Total Pages : 442 pages
Book Rating : 4.3/5 (3 download)

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Book Synopsis Optimization Functions in MATLAB by : J Lopez

Download or read book Optimization Functions in MATLAB written by J Lopez and published by . This book was released on 2019-07-21 with total page 442 pages. Available in PDF, EPUB and Kindle. Book excerpt: Optimization Toolbox provides functions for finding parameters that minimize or maximize objectives while satisfying constraints. The toolbox includes solvers for linear programming (LP), mixed-integer linear programming (MILP), quadratic programming(QP), nonlinear programming (NLP), constrained linear least squares, nonlinear least squares, and nonlinear equations. You can define your optimization problem with functions and matrices or by specifying variable expressions that reflect the underlying mathematics. You can use the toolbox solvers to fin optimal solutions to continuous and discrete problems, perform trade of analyses, and incorporate optimization methods into algorithms and applications. The toolbox lets you perform design optimization tasks, including parameter estimation, component selection, and parameter tuning. It can be used to fin optimal solutions in applications such as portfolio optimization, resource allocation, and production planning and scheduling. This book develops the functions of Matlab for optimization through examples

Multi-Objective Optimization in Theory and Practice I: Classical Methods

Author : Andre A. Keller
Publisher : Bentham Science Publishers
ISBN 13 : 1681085682
Total Pages : 296 pages
Book Rating : 4.6/5 (81 download)

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Book Synopsis Multi-Objective Optimization in Theory and Practice I: Classical Methods by : Andre A. Keller

Download or read book Multi-Objective Optimization in Theory and Practice I: Classical Methods written by Andre A. Keller and published by Bentham Science Publishers. This book was released on 2017-12-13 with total page 296 pages. Available in PDF, EPUB and Kindle. Book excerpt: Multi-Objective Optimization in Theory and Practice is a traditional two-part approach to solving multi-objective optimization (MOO) problems namely the use of classical methods and evolutionary algorithms. This first book is devoted to classical methods including the extended simplex method by Zeleny and preference-based techniques. This part covers three main topics through nine chapters. The first topic focuses on the design of such MOO problems, their complexities including nonlinearities and uncertainties, and optimality theory. The second topic introduces the founding solving methods including the extended simplex method to linear MOO problems and weighting objective methods. The third topic deals with particular structures of MOO problems, such as mixed-integer programming, hierarchical programming, fuzzy logic programming, and bimatrix games. Multi-Objective Optimization in Theory and Practice is a user-friendly book with detailed, illustrated calculations, examples, test functions, and small-size applications in Mathematica® (among other mathematical packages) and from scholarly literature. It is an essential handbook for students and teachers involved in advanced optimization courses in engineering, information science, and mathematics degree programs.

OPTIMIZATION with MATLAB. LINEAR PROGRAMMING and MIXED-INTEGER LINEAR PROGRAMMING

Author : J Lopez
Publisher :
ISBN 13 : 9781079536232
Total Pages : 220 pages
Book Rating : 4.5/5 (362 download)

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Book Synopsis OPTIMIZATION with MATLAB. LINEAR PROGRAMMING and MIXED-INTEGER LINEAR PROGRAMMING by : J Lopez

Download or read book OPTIMIZATION with MATLAB. LINEAR PROGRAMMING and MIXED-INTEGER LINEAR PROGRAMMING written by J Lopez and published by . This book was released on 2019-07-09 with total page 220 pages. Available in PDF, EPUB and Kindle. Book excerpt: Optimization Toolbox provides functions for finding parameters that minimize or maximize objectives while satisfying constraints. The toolbox includes solvers for linear programming (LP), mixed-integer linear programming (MILP), quadratic programming (QP), nonlinear programming (NLP), constrained linear least squares, nonlinear least squares, and nonlinear equations. You can define your optimization problem with functions and matrices or by specifying variable expressions that reflect the underlying mathematics. You can use the toolbox solvers to fin optimal solutions to continuous and discrete problems, perform trade of analyses, and incorporate optimization methods into algorithms and applications. The toolbox lets you perform design optimization tasks, including parameter estimation, component selection, and parameter tuning. It can be used to fin optimal solutions in applications such as portfolio optimization, resource allocation, and production planning and scheduling.You can use the toolbox solvers to find optimal solutions to continuous and discrete problems, perform tradeoff analyses, and incorporate optimization methods into algorithms and applications. The toolbox lets you perform design optimization tasks, including parameter estimation, component selection, and parameter tuning. It can be used to find optimal solutions in applications such as portfolio optimization, resource allocation, and production planning and scheduling.

Introduction to Linear Optimization and Extensions with MATLAB®

Author : Roy H. Kwon
Publisher : CRC Press
ISBN 13 : 9781439862636
Total Pages : 0 pages
Book Rating : 4.8/5 (626 download)

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Book Synopsis Introduction to Linear Optimization and Extensions with MATLAB® by : Roy H. Kwon

Download or read book Introduction to Linear Optimization and Extensions with MATLAB® written by Roy H. Kwon and published by CRC Press. This book was released on 2013-09-05 with total page 0 pages. Available in PDF, EPUB and Kindle. Book excerpt: 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.

ADVANCED OPTIMIZATION with MATLAB

Author : J Lopez
Publisher :
ISBN 13 : 9781074831813
Total Pages : 190 pages
Book Rating : 4.8/5 (318 download)

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Book Synopsis ADVANCED OPTIMIZATION with MATLAB by : J Lopez

Download or read book ADVANCED OPTIMIZATION with MATLAB written by J Lopez and published by . This book was released on 2019-06-18 with total page 190 pages. Available in PDF, EPUB and Kindle. Book excerpt: Optimization Toolbox provides functions for finding parameters that minimize or maximize objectives while satisfying constraints. The toolbox includes solvers for linear programming (LP), mixed-integer linear programming (MILP), quadratic programming(QP), nonlinear programming (NLP), constrained linear least squares, nonlinear least squares, and nonlinear equations. You can define your optimization problem with functions and matrices or by specifying variable expressions that reflect the underlying mathematics. You can use the toolbox solvers to fin optimal solutions to continuous and discrete problems, perform trade of analyses, and incorporate optimization methods into algorithms and applications. The toolbox lets you perform design optimization tasks, including parameter estimation, component selection, and parameter tuning. It can be used to fin optimal solutions in applications such as portfolio optimization, resource allocation, and production planning and scheduling.Global Optimization Toolbox provides functions that search for global solutions to problems that contain multiple maxima or minima. Toolbox solvers include surrogate, pattern search, genetic algorithm, particle swarm, simulated annealing, multi start, and global search. You can use these solvers for optimization problems where the objective or constraint function is continuous, discontinuous, stochastic, does not possess derivatives, or includes simulations or black-box functions. For problems with multiple objectives, you can identify a Pareto front using genetic algorithm or pattern search solvers. You can improve solver effective es by adjusting options and, for applicable solvers, customizing creation, update, and search functions. You can use custom data types with the genetic algorithm and simulated annealing solvers to represent problems not easily expressed with standard data types. The hybrid function option lets you improve a solution by applying a second solver after the first.

OPTIMIZATION INTRODUCTION with MATLAB

Author : J Lopez
Publisher : Independently Published
ISBN 13 : 9781099648281
Total Pages : 238 pages
Book Rating : 4.6/5 (482 download)

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Book Synopsis OPTIMIZATION INTRODUCTION with MATLAB by : J Lopez

Download or read book OPTIMIZATION INTRODUCTION with MATLAB written by J Lopez and published by Independently Published. This book was released on 2019-05-21 with total page 238 pages. Available in PDF, EPUB and Kindle. Book excerpt: Optimization Toolbox provides functions for finding parameters that minimize or maximize objectives while satisfying constraints. The toolbox includes solvers for linear programming (LP), mixed-integer linear programming (MILP), quadratic programming(QP), nonlinear programming (NLP), constrained linear least squares, nonlinear least squares, and nonlinear equations. You can define your optimization problem with functions and matrices or by specifying variable expressions that reflect the underlying mathematics. You can use the toolbox solvers to fin optimal solutions to continuous and discrete problems, perform trade of analyses, and incorporate optimization methods into algorithms and applications. The toolbox lets you perform design optimization tasks, including parameter estimation, component selection, and parameter tuning. It can be used to fin optimal solutions in applications such as portfolio optimization, resource allocation, and production planning and scheduling.Key Features: -Nonlinear and multiobjective optimization of smooth constrained and unconstrained problems-Solvers for nonlinear least squares, constrained linear least squares, data fitting and nonlinear equations-Quadratic programming (QP) and linear programming (LP)-Mixed-integer linear programming (MILP)-Optimization modeling tools-Graphical monitoring of optimization progress-Gradient estimation acceleration (with Parallel Computing Toolbox(TM))

Applied Optimization with MATLAB Programming

Author : P. Venkataraman
Publisher : John Wiley & Sons
ISBN 13 : 047008488X
Total Pages : 546 pages
Book Rating : 4.4/5 (7 download)

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Book Synopsis Applied Optimization with MATLAB Programming by : P. Venkataraman

Download or read book Applied Optimization with MATLAB Programming written by P. Venkataraman and published by John Wiley & Sons. This book was released on 2009-03-23 with total page 546 pages. Available in PDF, EPUB and Kindle. Book excerpt: 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.

Optimization Techniques With MATLAB

Author : Subrata Pandey
Publisher : SUBRATA PANDEY
ISBN 13 :
Total Pages : 48 pages
Book Rating : 4./5 ( download)

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Book Synopsis Optimization Techniques With MATLAB by : Subrata Pandey

Download or read book Optimization Techniques With MATLAB written by Subrata Pandey and published by SUBRATA PANDEY. This book was released on 2023-03-03 with total page 48 pages. Available in PDF, EPUB and Kindle. Book excerpt: Optimization is a critical area in the fields of science, engineering, and mathematics. It involves finding the optimal solution among feasible alternatives to satisfy certain constraints. Optimization techniques can be applied to a wide range of applications, including finance, machine learning, signal processing, control systems, and many others. This book provides a comprehensive introduction to optimization techniques and their implementation using MATLAB. MATLAB is a powerful computational tool widely used in academia and industry for numerical analysis and scientific computing. The combination of optimization techniques and MATLAB provides a powerful framework for solving complex problems in a variety of fields.

Optimization Methods in Operations Research and Systems Analysis

Author : K V Mital
Publisher : New Age International
ISBN 13 : 9788122408737
Total Pages : 408 pages
Book Rating : 4.4/5 (87 download)

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Book Synopsis Optimization Methods in Operations Research and Systems Analysis by : K V Mital

Download or read book Optimization Methods in Operations Research and Systems Analysis written by K V Mital and published by New Age International. This book was released on 1996 with total page 408 pages. Available in PDF, EPUB and Kindle. Book excerpt: The Mathematical Aspects Of Operations Research And Systems Analysis Concerned With Optimization Of Objectives Form The Subject Of This Book. In Its Revised, Updated And Enlarged Third Edition, Discussion On Linear Programming Has Been Expanded And Recast With Greater Emphasis On Duality Theory, Sensitivity Analysis, Parametric Programming, Multiobjective And Goal Programming And Formulation And Solution Of Practical Problems. Chapters On Nonlinear Programming Include Integer Programming, Kuhn-Tucker Theory, Separable And Quadratic Programming, Dynamic Programming, Geometric Programming And Direct Search And Gradient Methods. A Chapter On Theory Of Games Is Also Included. A Short Note On Karmarkars Projective Algorithm Is Given In The Appendix.The Book Keeps In View The Needs Of The Student Taking A Regular Course In Operations Research Or Mathematical Programming, And Also Of Research Scholars In Other Disciplines Who Have A Limited Objective Of Learning The Practical Aspects Of Various Optimization Methods To Solve Their Special Problems. For The Former, Illustrative Solved Examples And Unsolved Examples At The End Of Each Chapter, Small Enough To Be Solved By Hand, Would Be Of Greater Interest, While For He Latter, Summaries Of Computational Algorithms For Various Methods Which Would Help Him To Write Computer Programmes To Solve Larger Problems Would Be More Helpful. A Few Computer Programmes In Fortran Iv Have Also Been Given In The Appendix.