Operations Research Optimization With Matlab Multiobjective Quadratic And Mixed Programming
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Operations Research Optimization With Matlab Multiobjective Quadratic And Mixed Programming
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Author : Perez C.
language : en
Publisher:
Release Date : 2017-08-16
Operations Research Optimization With Matlab Multiobjective Quadratic And Mixed Programming written by Perez C. and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 2017-08-16 with categories.
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
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Author : Perez C.
language : en
Publisher:
Release Date : 2017-08-16
Operations Research Optimization Functions In Matlab For Linear And Nonlinear Programming written by Perez C. and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 2017-08-16 with categories.
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
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Author : Perez C.
language : en
Publisher:
Release Date : 2017-08-16
Operations Research Optimization With Matlab Linear And Nonlinear Programming written by Perez C. and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 2017-08-16 with categories.
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
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Author : Dingyü Xue
language : en
Publisher: Walter de Gruyter GmbH & Co KG
Release Date : 2020-04-06
Solving Optimization Problems With Matlab written by Dingyü Xue and has been published by Walter de Gruyter GmbH & Co KG this book supported file pdf, txt, epub, kindle and other format this book has been release on 2020-04-06 with Computers categories.
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
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Author : Roy H. Kwon
language : en
Publisher: CRC Press
Release Date : 2013-09-05
Introduction To Linear Optimization And Extensions With Matlab written by Roy H. Kwon and has been published by CRC Press this book supported file pdf, txt, epub, kindle and other format this book has been release on 2013-09-05 with Business & Economics categories.
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
Optimization With Matlab Nonlinear And Multiobjective Optimization
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Author : J Lopez
language : en
Publisher:
Release Date : 2019-05-22
Optimization With Matlab Nonlinear And Multiobjective Optimization written by J Lopez and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 2019-05-22 with categories.
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))
Solving Optimization Problems With Matlab
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Author : Dingyu Xue
language : en
Publisher: de Gruyter
Release Date : 2020-05-30
Solving Optimization Problems With Matlab written by Dingyu Xue and has been published by de Gruyter this book supported file pdf, txt, epub, kindle and other format this book has been release on 2020-05-30 with categories.
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.
Matlab Optimization Techniques
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Author : Cesar Lopez
language : en
Publisher: Apress
Release Date : 2014-11-12
Matlab Optimization Techniques written by Cesar Lopez and has been published by Apress this book supported file pdf, txt, epub, kindle and other format this book has been release on 2014-11-12 with Computers categories.
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.
Optimization Functions In Matlab
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Author : J Lopez
language : en
Publisher:
Release Date : 2019-07-21
Optimization Functions In Matlab written by J Lopez and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 2019-07-21 with categories.
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
Introduction To Linear Optimization And Extensions With Matlab
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Author : Roy H. Kwon
language : en
Publisher: CRC Press
Release Date : 2013-09-05
Introduction To Linear Optimization And Extensions With Matlab written by Roy H. Kwon and has been published by CRC Press this book supported file pdf, txt, epub, kindle and other format this book has been release on 2013-09-05 with Business & Economics categories.
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.