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MATLAB optimization techniques / César Pérez López.

By: Contributor(s): Material type: TextTextPublisher: New York : Apress, [2014]Copyright date: ℗♭2014Description: xi, 277 pages : illustrations; 24 cmContent type:
  • text
Media type:
  • computer
Carrier type:
  • online resource
ISBN:
  • 9781484202937
  • 9781484202920
Subject(s): Genre/Form: Additional physical formats: Printed edition:: No titleDDC classification:
  • 512.9434 23 L.C.M
LOC classification:
  • TA347.D4
Online resources:
Contents:
Summary: 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.
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Holdings
Item type Current library Collection Call number Status Date due Barcode
Books Books Main library A8 Faculty of Engineering & Technology (General) 512.9434 L.C.M (Browse shelf(Opens below)) Available 00012784

Machine generated contents note: ch. 1 Introducing MATLAB and the MATLAB Working Environment --
1.1. Introduction --
1.1.1. Developing Algorithms and Applications --
1.1.2. Data Access and Analysis --
1.1.3. Data Visualization --
1.1.4. Numerical Calculation --
1.1.5. Publication of Results and Distribution of Applications --
1.2. MATLAB Working Environment --
1.3. Help in MATLAB --
ch. 2 MATLAB Programming --
2.1. MATLAB Programming --
2.1.1. Text Editor --
2.1.2. Scripts --
2.1.3. Functions and M-files. Eval and Feval --
2.1.4. Local and Global Variables --
2.1.5. Data Types --
2.1.6. Flow Control: FOR, WHILE and IF ELSEIF Loops --
2.1.7. Subfunctions --
2.1.8. Commands in M-files --
2.1.9. Functions Relating to Arrays of Cells --
2.1.10. Multidimensional Array Functions --
ch. 3 Basic MATLAB Functions for Linear and Non-Linear Optimization --
3.1. Solutions of Equations and Systems of Equations --
3.2. Working with Polynomials --
ch. 4 Optimization by Numerical Methods: Solving Equations --
4.1. Non-Linear Equations --
4.1.1. Fixed Point Method for Solving x = g(x) --
4.1.2. Newton's Method for Solving the Equation f(x) = 0 --
4.1.3. Schroder's Method for Solving the Equation f(x) = 0 --
4.2. Systems of Non-Linear Equations --
4.2.1. Seidel Method --
4.2.2. Newton-Raphson Method --
ch. 5 Optimization Using Symbolic Computation --
5.1. Symbolic Equations and Systems of Equations --
ch. 6 Optimization Techniques Via The Optimization Toolbox --
6.1. Optimization Toolbox --
6.1.1. Standard Algorithms --
6.1.2. Large Scale Algorithms --
6.2. Minimization Algorithms --
6.2.1. Multiobjective Problems --
6.2.2. Non-Linear Scalar Minimization With Boundary Conditions --
6.2.3. Non-Linear Minimization with Restrictions --
6.2.4. Minimax Optimization: fminimax and fminuc --
6.2.5. Minimax Optimization --
6.2.6. Minimum Optimization: fminsearch and fminuc --
6.2.7. Semi-Infinitely Constrained Minimization --
6.2.8. Linear Programming --
6.2.9. Quadratic programming --
6.3. Equation Solving Algorithms --
6.3.1. Solving Equations and Systems of Equations --
6.4. Fitting Curves by Least Squares --
6.4.1. Conditional Least Squares Problems --
6.4.2. Non- Linear Least Squares Problems --
6.4.3. Linear Non- Negative Least Squares Problems --
ch. 7 Differentiation in one and Several Variables. Applications to Optimization --
7.1. Derivatives --
7.2. Partial Derivatives --
7.3. Applications of Derivatives. Tangents, Asymptotes, Extreme Points and Turning Points --
7.4. Differentiation of Functions of Several Variables --
7.5. Maxima and Minima of Functions of Several Variables --
7.6. Conditional Minima and Maxima. The Method of "Lagrange Multipliers" --
7.7. Vector Differential Calculus --
7.8. Composite Function Theorem --
7.9. Implicit Function Theorem --
7.10. Inverse Function Theorem --
7.11. Change of Variables Theorem --
7.12. Series Expansions in Several Variables --
7.13. Vector Fields. Curl, Divergence and the Laplacian --
7.14. Spherical, Cylindrical and Rectangular Coordinates --
ch. 8 Optimization of Functions of Complex Variables --
8.1. Complex Numbers --
8.2. General Functions of a Complex Variable --
8.2.1. Trigonometric Functions of a Complex Variable --
8.2.2. Hyperbolic Functions of a Complex Variable --
8.2.3. Exponential and Logarithmic Functions of a Complex Variable --
8.3. Specific Functions of a Complex Variable --
8.4. Basic Functions with Complex Vector Arguments --
8.5. Basic Functions with Complex Matrix Arguments --
8.6. General Functions with Complex Matrix Arguments --
8.6.1. Trigonometric Functions of a Complex Matrix Variable --
8.6.2. Hyperbolic Functions of a Complex Matrix Variable --
8.6.3. Exponential and Logarithmic Functions of a Complex Matrix Variable --
8.6.4. Specific Functions of a Complex Matrix Variable --
8.7. Matrix Operations with Real and Complex Variables --
ch. 9 Algebraic Expressions, Polynomials, Equations and Systems. Tools for Optimization --
9.1. Expanding, Simplifying and Factoring Algebraic Expressions --
9.2. Polynomials --
9.3. Polynomial Interpolation --
9.4. Solving Equations and Systems of Equations --
9.4.1. General Methods --
9.4.2. Biconjugate Gradient Method --
9.4.3. Conjugate Gradients Method --
9.4.4. Residual Method --
9.4.5. Symmetric and Non-Negative Least Squares Method.

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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.

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