Nonlinear Optimization
portes grátis
Nonlinear Optimization
Models and Applications
Fox, William P.
Taylor & Francis Ltd
12/2020
394
Dura
Inglês
9780367444150
15 a 20 dias
680
Descrição não disponível.
Chapter 1. Nonlinear Optimization Overview
1.1 Introduction
1.2 Modeling
1.3 Exercises
Chapter 2. Review of Single Variable Calculus Topics
2.1 Limits
2.2 Continuity
2.3 Differentiation
2.4 Convexity
Chapter 3. Single Variable Optimization
3.1 Introduction
3.2 Optimization Applications
3.3 Optimization Models
Constrained Optimization by Calculus
Chapter 4. Single Variable Search Methods
4.1 Introduction
4.2 Unrestricted Search
4.3 Dichotomous Search
4.4 Golden Section Search
4.5 Fibonacci Search
4.6 Newton's Method
4.7 Bisection Derivative Search
Chapter 5. Review of MV Calculus Topics
5.1 Introduction, Basic Theory, and Partial Derivatives
5.2 Directional Derivatives and The Gradient
Chapter 6. MV Optimization
6.1 Introduction
6.2 The Hessian
6.3 Unconstrained Optimization
Convexity and The Hessian Matrix
Max and Min Problems with Several Variables
Chapter 7. Multi-variable Search Methods
7.1 Introduction
7.2 Gradient Search
7.3 Modified Newton's Method
Chapter 8. Equality Constrained Optimization: Lagrange Multipliers
8.1 Introduction and Theory
8.2 Graphical Interpretation
8.3 Computational Methods
8.4 Modeling and Applications
Chapter 9. Inequality Constrained Optimization; Kuhn-Tucker Methods
9.1 Introduction
9.2 Basic Theory
9.3 Graphical Interpretation and Computational Methods
9.4 Modeling and Applications
Chapter 10. Method of Feasible Directions and Other Special NL Methods
10.1 Methods of Feasible Directions
Numerical methods (Directional Searches)
Starting Point Methods
10.2 Separable Programming
10.3 Quadratic Programming
Chapter 11. Dynamic Programming
11.1 Introduction
11.2 Continuous Dynamic Programming
11.3 Modeling and Applications with Continuous DP
11.4 Discrete Dynamic Programming
11.5 Modeling and Applications with Discrete Dynamic Programming
1.1 Introduction
1.2 Modeling
1.3 Exercises
Chapter 2. Review of Single Variable Calculus Topics
2.1 Limits
2.2 Continuity
2.3 Differentiation
2.4 Convexity
Chapter 3. Single Variable Optimization
3.1 Introduction
3.2 Optimization Applications
3.3 Optimization Models
Constrained Optimization by Calculus
Chapter 4. Single Variable Search Methods
4.1 Introduction
4.2 Unrestricted Search
4.3 Dichotomous Search
4.4 Golden Section Search
4.5 Fibonacci Search
4.6 Newton's Method
4.7 Bisection Derivative Search
Chapter 5. Review of MV Calculus Topics
5.1 Introduction, Basic Theory, and Partial Derivatives
5.2 Directional Derivatives and The Gradient
Chapter 6. MV Optimization
6.1 Introduction
6.2 The Hessian
6.3 Unconstrained Optimization
Convexity and The Hessian Matrix
Max and Min Problems with Several Variables
Chapter 7. Multi-variable Search Methods
7.1 Introduction
7.2 Gradient Search
7.3 Modified Newton's Method
Chapter 8. Equality Constrained Optimization: Lagrange Multipliers
8.1 Introduction and Theory
8.2 Graphical Interpretation
8.3 Computational Methods
8.4 Modeling and Applications
Chapter 9. Inequality Constrained Optimization; Kuhn-Tucker Methods
9.1 Introduction
9.2 Basic Theory
9.3 Graphical Interpretation and Computational Methods
9.4 Modeling and Applications
Chapter 10. Method of Feasible Directions and Other Special NL Methods
10.1 Methods of Feasible Directions
Numerical methods (Directional Searches)
Starting Point Methods
10.2 Separable Programming
10.3 Quadratic Programming
Chapter 11. Dynamic Programming
11.1 Introduction
11.2 Continuous Dynamic Programming
11.3 Modeling and Applications with Continuous DP
11.4 Discrete Dynamic Programming
11.5 Modeling and Applications with Discrete Dynamic Programming
Este título pertence ao(s) assunto(s) indicados(s). Para ver outros títulos clique no assunto desejado.
Economic Order Quantity;Nonlinear optimization;NLP Problem;Numerical approximations;Number Function Evaluations;Model sensitivity analysis;Vice Versa;Geometric interpretation;Golden Section Search;Nonlinear programming techniques;Simplex Method;Hessian Matrix;Newton's Method;EOQ Model;Shadow Price;Peripheral Computer;Lagrangian Function;SP6 Computer;Total Cost;Final Interval;CS.;Non-basic Variables;Golden Section Method;Point P1 P4;Feasible Directions;Interior Point Methods;DP Problem;Piecewise Linear Approximation;Kuhn Tucker Conditions;Unconstrained Nonlinear Optimization Problem
Chapter 1. Nonlinear Optimization Overview
1.1 Introduction
1.2 Modeling
1.3 Exercises
Chapter 2. Review of Single Variable Calculus Topics
2.1 Limits
2.2 Continuity
2.3 Differentiation
2.4 Convexity
Chapter 3. Single Variable Optimization
3.1 Introduction
3.2 Optimization Applications
3.3 Optimization Models
Constrained Optimization by Calculus
Chapter 4. Single Variable Search Methods
4.1 Introduction
4.2 Unrestricted Search
4.3 Dichotomous Search
4.4 Golden Section Search
4.5 Fibonacci Search
4.6 Newton's Method
4.7 Bisection Derivative Search
Chapter 5. Review of MV Calculus Topics
5.1 Introduction, Basic Theory, and Partial Derivatives
5.2 Directional Derivatives and The Gradient
Chapter 6. MV Optimization
6.1 Introduction
6.2 The Hessian
6.3 Unconstrained Optimization
Convexity and The Hessian Matrix
Max and Min Problems with Several Variables
Chapter 7. Multi-variable Search Methods
7.1 Introduction
7.2 Gradient Search
7.3 Modified Newton's Method
Chapter 8. Equality Constrained Optimization: Lagrange Multipliers
8.1 Introduction and Theory
8.2 Graphical Interpretation
8.3 Computational Methods
8.4 Modeling and Applications
Chapter 9. Inequality Constrained Optimization; Kuhn-Tucker Methods
9.1 Introduction
9.2 Basic Theory
9.3 Graphical Interpretation and Computational Methods
9.4 Modeling and Applications
Chapter 10. Method of Feasible Directions and Other Special NL Methods
10.1 Methods of Feasible Directions
Numerical methods (Directional Searches)
Starting Point Methods
10.2 Separable Programming
10.3 Quadratic Programming
Chapter 11. Dynamic Programming
11.1 Introduction
11.2 Continuous Dynamic Programming
11.3 Modeling and Applications with Continuous DP
11.4 Discrete Dynamic Programming
11.5 Modeling and Applications with Discrete Dynamic Programming
1.1 Introduction
1.2 Modeling
1.3 Exercises
Chapter 2. Review of Single Variable Calculus Topics
2.1 Limits
2.2 Continuity
2.3 Differentiation
2.4 Convexity
Chapter 3. Single Variable Optimization
3.1 Introduction
3.2 Optimization Applications
3.3 Optimization Models
Constrained Optimization by Calculus
Chapter 4. Single Variable Search Methods
4.1 Introduction
4.2 Unrestricted Search
4.3 Dichotomous Search
4.4 Golden Section Search
4.5 Fibonacci Search
4.6 Newton's Method
4.7 Bisection Derivative Search
Chapter 5. Review of MV Calculus Topics
5.1 Introduction, Basic Theory, and Partial Derivatives
5.2 Directional Derivatives and The Gradient
Chapter 6. MV Optimization
6.1 Introduction
6.2 The Hessian
6.3 Unconstrained Optimization
Convexity and The Hessian Matrix
Max and Min Problems with Several Variables
Chapter 7. Multi-variable Search Methods
7.1 Introduction
7.2 Gradient Search
7.3 Modified Newton's Method
Chapter 8. Equality Constrained Optimization: Lagrange Multipliers
8.1 Introduction and Theory
8.2 Graphical Interpretation
8.3 Computational Methods
8.4 Modeling and Applications
Chapter 9. Inequality Constrained Optimization; Kuhn-Tucker Methods
9.1 Introduction
9.2 Basic Theory
9.3 Graphical Interpretation and Computational Methods
9.4 Modeling and Applications
Chapter 10. Method of Feasible Directions and Other Special NL Methods
10.1 Methods of Feasible Directions
Numerical methods (Directional Searches)
Starting Point Methods
10.2 Separable Programming
10.3 Quadratic Programming
Chapter 11. Dynamic Programming
11.1 Introduction
11.2 Continuous Dynamic Programming
11.3 Modeling and Applications with Continuous DP
11.4 Discrete Dynamic Programming
11.5 Modeling and Applications with Discrete Dynamic Programming
Este título pertence ao(s) assunto(s) indicados(s). Para ver outros títulos clique no assunto desejado.
Economic Order Quantity;Nonlinear optimization;NLP Problem;Numerical approximations;Number Function Evaluations;Model sensitivity analysis;Vice Versa;Geometric interpretation;Golden Section Search;Nonlinear programming techniques;Simplex Method;Hessian Matrix;Newton's Method;EOQ Model;Shadow Price;Peripheral Computer;Lagrangian Function;SP6 Computer;Total Cost;Final Interval;CS.;Non-basic Variables;Golden Section Method;Point P1 P4;Feasible Directions;Interior Point Methods;DP Problem;Piecewise Linear Approximation;Kuhn Tucker Conditions;Unconstrained Nonlinear Optimization Problem
