Description

Book Synopsis

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 te

    Table of Contents
    Preface to the Second Edition.

    Preface.

    Chapter 1: Introduction.

    1.1 Optimization Fundamentals.

    1.2 Introduction to MATLAB.

    Problems.

    Chapter 2: Graphical Optimization.

    2.1 Problem Definition.

    2.2 Graphical Solution.

    2.3 Additional Examples.

    2.4 Additional MATLAB Graphics.

    References.

    Problems.

    Chapter 3: Linear Programming.

    3.1 Problem Definition.

    3.2 Graphical Solution.

    3.3 Numerical Solution - The Simplex Method.

    3.4 Additional Examples.

    3.5.Additional Topics in Linear Programming.

    References.

    Problems.

    Chapter 4: Nonlinear Programming.

    4.1 Problem Definition.

    4.2 Mathematical Concepts.

    4.3 Analytical Conditions.

    4.4 Examples.

    4.5 Additional Topics.

    References.

    Problems.

    Chapter 5: Numerical Techniques - The One Dimensional Problem.

    5.1 Problem Definition.

    5.2 Numerical Techniques.

    5.3 Importance of the One Dimensional Problem.

    5.4 Additional Examples.

    References.

    Problems.

    Chapter 6: Numerical Techniques for Unconstrained Optimization.

    6.1 Problem Definition.

    6.2 Numerical Techniques: Non Gradient Methods.

    6.3 Numerical Technique: Gradient Based Methods.

    6.4 Numerical Technique: Second Order.

    6.5 Additional Examples.

    6.6 Summary.

    References.

    Problems.

    Chapter 7: Numerical Techniques for Constrained Optimization.

    7.1 Problem Definition.

    7.2 Indirect Methods for Constrained Optimization.

    7.3 Direct Methods for Constrained Optimization.

    7.4 Additional Examples.

    References.

    Problems.

    Chapter 8: Discrete Optimization.

    8.1 Concepts in Discrete Programming.

    8.2 Discrete Optimization Techniques.

    8.3 Additional Examples.

    References.

    Problems.

    Chapter 9: Global Optimization.

    9.1 Problem Definition.

    9.2 Numerical Techniques and Additional Examples.

    References.

    Problems.

    Chapter 10: Optimization Toolbox from MATLAB.

    10.1 The Optimization Toolbox.

    10.2 Examples.

    References.

    Chapter 11: Hybrid Mathematics: An Application of.

    11.1 Central Idea.

    11.2 Data Handling Examples.

    11.3. Solutions to Differential Systems.

    11.4 Summary.

    References.

    Index.

Applied Optimization with MATLAB Programming

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    A Hardback by P. Venkataraman

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      View other formats and editions of Applied Optimization with MATLAB Programming by P. Venkataraman

      Publisher: John Wiley & Sons Inc
      Publication Date: 03/04/2009
      ISBN13: 9780470084885, 978-0470084885
      ISBN10: 047008488X

      Description

      Book Synopsis

      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 te

        Table of Contents
        Preface to the Second Edition.

        Preface.

        Chapter 1: Introduction.

        1.1 Optimization Fundamentals.

        1.2 Introduction to MATLAB.

        Problems.

        Chapter 2: Graphical Optimization.

        2.1 Problem Definition.

        2.2 Graphical Solution.

        2.3 Additional Examples.

        2.4 Additional MATLAB Graphics.

        References.

        Problems.

        Chapter 3: Linear Programming.

        3.1 Problem Definition.

        3.2 Graphical Solution.

        3.3 Numerical Solution - The Simplex Method.

        3.4 Additional Examples.

        3.5.Additional Topics in Linear Programming.

        References.

        Problems.

        Chapter 4: Nonlinear Programming.

        4.1 Problem Definition.

        4.2 Mathematical Concepts.

        4.3 Analytical Conditions.

        4.4 Examples.

        4.5 Additional Topics.

        References.

        Problems.

        Chapter 5: Numerical Techniques - The One Dimensional Problem.

        5.1 Problem Definition.

        5.2 Numerical Techniques.

        5.3 Importance of the One Dimensional Problem.

        5.4 Additional Examples.

        References.

        Problems.

        Chapter 6: Numerical Techniques for Unconstrained Optimization.

        6.1 Problem Definition.

        6.2 Numerical Techniques: Non Gradient Methods.

        6.3 Numerical Technique: Gradient Based Methods.

        6.4 Numerical Technique: Second Order.

        6.5 Additional Examples.

        6.6 Summary.

        References.

        Problems.

        Chapter 7: Numerical Techniques for Constrained Optimization.

        7.1 Problem Definition.

        7.2 Indirect Methods for Constrained Optimization.

        7.3 Direct Methods for Constrained Optimization.

        7.4 Additional Examples.

        References.

        Problems.

        Chapter 8: Discrete Optimization.

        8.1 Concepts in Discrete Programming.

        8.2 Discrete Optimization Techniques.

        8.3 Additional Examples.

        References.

        Problems.

        Chapter 9: Global Optimization.

        9.1 Problem Definition.

        9.2 Numerical Techniques and Additional Examples.

        References.

        Problems.

        Chapter 10: Optimization Toolbox from MATLAB.

        10.1 The Optimization Toolbox.

        10.2 Examples.

        References.

        Chapter 11: Hybrid Mathematics: An Application of.

        11.1 Central Idea.

        11.2 Data Handling Examples.

        11.3. Solutions to Differential Systems.

        11.4 Summary.

        References.

        Index.

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