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図書
図書
1. Nonlinear programming : theory and algorithms
Mokhtar S. Bazaraa, C.M. Shetty
出版情報: New York : Wiley, c1979  xiv, 560 p. ; 24 cm
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目次情報: 続きを見る
Introduction. / Chapter 1：
Problem Statement and Basic Definitions / 1.1：
Illustrative Examples / 1.2：
Guidelines for Model Construction / 1.3：
Exercises
Notes and References
Convex Analysis. / Part 1：
Convex Sets. / Chapter 2：
Convex Hulls / 2.1：
Closure and Interior of a Set / 2.2：
Weierstrass's Theorem / 2.3：
Separation and Support of Sets / 2.4：
Convex Cones and Polarity / 2.5：
Polyhedral Sets, Extreme Points, and Extreme Directions / 2.6：
Linear Programming and the Simplex Method / 2.7：
Convex Functions and Generalizations. / Chapter 3：
Definitions and Basic Properties / 3.1：
Subgradients of Convex Functions / 3.2：
Differentiable Convex Functions / 3.3：
Minima and Maxima of Convex Functions / 3.4：
Generalizations of Convex Functions / 3.5：
Optimality Conditions and Duality. / Part 2：
The Fritz John and Karush-Kuhn-Tucker Optimality Conditions. / Chapter 4：
Unconstrained Problems / 4.1：
Problems Having Inequality Constraints / 4.2：
Problems Having Inequality and Equality Constraints / 4.3：
Second-Order Necessary and Sufficient Optimality Conditions for Constrained Problems / 4.4：
Constraint Qualifications. / Chapter 5：
Cone of Tangents / 5.1：
Other Constraint Qualifications / 5.2：
Lagrangian Duality and Saddle Point Optimality Conditions. / 5.3：
Lagrangian Dual Problem / 6.1：
Duality Theorems and Saddle Point Optimality Conditions / 6.2：
Properties of the Dual Function / 6.3：
Formulating and Solving the Dual Problem / 6.4：
Getting the Primal Solution / 6.5：
Linear and Quadratic Programs / 6.6：
Algorithms and Their Convergence / Part 3：
The Concept of an Algorithm. / Chapter 7：
Algorithms and Algorithmic Maps / 7.1：
Closed Maps and Convergence / 7.2：
Composition of Mappings / 7.3：
Comparison Among Algorithms / 7.4：
Unconstrained Optimization. / Chapter 8：
Line Search Without Using Derivatives / 8.1：
Line Search Using Derivatives / 8.2：
Some Practical Line Search Methods / 8.3：
Closedness of the Line Search Algorithmic Map / 8.4：
Multidimensional Search Without Using Derivatives / 8.5：
Multidimensional Search Using Derivatives / 8.6：
Modification of Newton's Method: Levenberg-Marquardt and Trust Region Methods / 8.7：
Methods Using Conjugate Directions: Quasi-Newton and Conjugate Gradient Methods / 8.8：
Subgradient Optimization Methods / 8.9：
Penalty and Barrier Functions. / Chapter 9：
Concept of Penalty Functions / 9.1：
Exterior Penalty Function Methods / 9.2：
Exact Absolute Value and Augmented Lagrangian Penalty Methods / 9.3：
Barrier Function Methods / 9.4：
Polynomial-Time Interior Point Algorithms for Linear Programming Based on a Barrier Function / 9.5：
Methods of Feasible Directions. / Chapter 10：
Method of Zoutendijk / 10.1：
Convergence Analysis of the Method of Zoutendijk / 10.2：
Successive Linear Programming Approach / 10.3：
Successive Quadratic Programming or Projected Lagrangian Approach / 10.4：
Gradient Projection Method of Rosen / 10.5：
Reduced Gradient Method of Wolfe and Generalized Reduced Gradient Method / 10.6：
Convex-Simplex Method of Zangwill / 10.7：
Effective First- and Second-Order Variants of the Reduced Gradient Method / 10.8：
Linear Complementary Problem, and Quadratic, Separable, Fractional, and Geometric Programming. / Chapter 11：
Linear Complementary Problem / 11.1：
Convex and Nonconvex Quadratic Programming: Global Optimization Approaches / 11.2：
Separable Programming / 11.3：
Linear Fractional Programming / 11.4：
Geometric Programming / 11.5：
Mathematical Review. / Appendix A：
Summary of Convexity, Optimality Conditions, and Duality. / Appendix B：
Bibliography.
Index
Introduction. / Chapter 1：
Problem Statement and Basic Definitions / 1.1：
Illustrative Examples / 1.2：
2.

図書
図書
2. Nonlinear programming : theory and algorithms. 2nd ed (: hard ; : pbk)
Mokhtar S. Bazaraa, Hanif D. Sherali, C.M. Shetty
出版情報: New York ; Chichester : Wiley, c1993  xiii, 638 p. ; 26 cm
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目次情報: 続きを見る
Introduction. / Chapter 1：
Problem Statement and Basic Definitions / 1.1：
Illustrative Examples / 1.2：
Guidelines for Model Construction / 1.3：
Exercises
Notes and References
Convex Analysis. / Part 1：
Convex Sets. / Chapter 2：
Convex Hulls / 2.1：
Closure and Interior of a Set / 2.2：
Weierstrass's Theorem / 2.3：
Separation and Support of Sets / 2.4：
Convex Cones and Polarity / 2.5：
Polyhedral Sets, Extreme Points, and Extreme Directions / 2.6：
Linear Programming and the Simplex Method / 2.7：
Convex Functions and Generalizations. / Chapter 3：
Definitions and Basic Properties / 3.1：
Subgradients of Convex Functions / 3.2：
Differentiable Convex Functions / 3.3：
Minima and Maxima of Convex Functions / 3.4：
Generalizations of Convex Functions / 3.5：
Optimality Conditions and Duality. / Part 2：
The Fritz John and Karush-Kuhn-Tucker Optimality Conditions. / Chapter 4：
Unconstrained Problems / 4.1：
Problems Having Inequality Constraints / 4.2：
Problems Having Inequality and Equality Constraints / 4.3：
Second-Order Necessary and Sufficient Optimality Conditions for Constrained Problems / 4.4：
Constraint Qualifications. / Chapter 5：
Cone of Tangents / 5.1：
Other Constraint Qualifications / 5.2：
Lagrangian Duality and Saddle Point Optimality Conditions. / 5.3：
Lagrangian Dual Problem / 6.1：
Duality Theorems and Saddle Point Optimality Conditions / 6.2：
Properties of the Dual Function / 6.3：
Formulating and Solving the Dual Problem / 6.4：
Getting the Primal Solution / 6.5：
Linear and Quadratic Programs / 6.6：
Algorithms and Their Convergence / Part 3：
The Concept of an Algorithm. / Chapter 7：
Algorithms and Algorithmic Maps / 7.1：
Closed Maps and Convergence / 7.2：
Composition of Mappings / 7.3：
Comparison Among Algorithms / 7.4：
Unconstrained Optimization. / Chapter 8：
Line Search Without Using Derivatives / 8.1：
Line Search Using Derivatives / 8.2：
Some Practical Line Search Methods / 8.3：
Closedness of the Line Search Algorithmic Map / 8.4：
Multidimensional Search Without Using Derivatives / 8.5：
Multidimensional Search Using Derivatives / 8.6：
Modification of Newton's Method: Levenberg-Marquardt and Trust Region Methods / 8.7：
Methods Using Conjugate Directions: Quasi-Newton and Conjugate Gradient Methods / 8.8：
Subgradient Optimization Methods / 8.9：
Penalty and Barrier Functions. / Chapter 9：
Concept of Penalty Functions / 9.1：
Exterior Penalty Function Methods / 9.2：
Exact Absolute Value and Augmented Lagrangian Penalty Methods / 9.3：
Barrier Function Methods / 9.4：
Polynomial-Time Interior Point Algorithms for Linear Programming Based on a Barrier Function / 9.5：
Methods of Feasible Directions. / Chapter 10：
Method of Zoutendijk / 10.1：
Convergence Analysis of the Method of Zoutendijk / 10.2：
Successive Linear Programming Approach / 10.3：
Successive Quadratic Programming or Projected Lagrangian Approach / 10.4：
Gradient Projection Method of Rosen / 10.5：
Reduced Gradient Method of Wolfe and Generalized Reduced Gradient Method / 10.6：
Convex-Simplex Method of Zangwill / 10.7：
Effective First- and Second-Order Variants of the Reduced Gradient Method / 10.8：
Linear Complementary Problem, and Quadratic, Separable, Fractional, and Geometric Programming. / Chapter 11：
Linear Complementary Problem / 11.1：
Convex and Nonconvex Quadratic Programming: Global Optimization Approaches / 11.2：
Separable Programming / 11.3：
Linear Fractional Programming / 11.4：
Geometric Programming / 11.5：
Mathematical Review. / Appendix A：
Summary of Convexity, Optimality Conditions, and Duality. / Appendix B：
Bibliography.
Index
Introduction. / Chapter 1：
Problem Statement and Basic Definitions / 1.1：
Illustrative Examples / 1.2：
3.

電子ブック
ＥＢ
3. Nonlinear programming : theory and algorithms. 3rd ed (: electronic bk)
Mokhtar S. Bazaraa, Hanif D. Sherali, C.M. Shetty
出版情報: Wiley Online Library, 2005  1 online resource (xv, 853p)
シリーズ名: A Wiley-Interscience publication
所蔵情報: loading…
目次情報: 続きを見る
Introduction. / Chapter 1：
Problem Statement and Basic Definitions / 1.1：
Illustrative Examples / 1.2：
Guidelines for Model Construction / 1.3：
Exercises
Notes and References
Convex Analysis. / Part 1：
Convex Sets. / Chapter 2：
Convex Hulls / 2.1：
Closure and Interior of a Set / 2.2：
Weierstrass's Theorem / 2.3：
Separation and Support of Sets / 2.4：
Convex Cones and Polarity / 2.5：
Polyhedral Sets, Extreme Points, and Extreme Directions / 2.6：
Linear Programming and the Simplex Method / 2.7：
Convex Functions and Generalizations. / Chapter 3：
Definitions and Basic Properties / 3.1：
Subgradients of Convex Functions / 3.2：
Differentiable Convex Functions / 3.3：
Minima and Maxima of Convex Functions / 3.4：
Generalizations of Convex Functions / 3.5：
Optimality Conditions and Duality. / Part 2：
The Fritz John and Karush-Kuhn-Tucker Optimality Conditions. / Chapter 4：
Unconstrained Problems / 4.1：
Problems Having Inequality Constraints / 4.2：
Problems Having Inequality and Equality Constraints / 4.3：
Second-Order Necessary and Sufficient Optimality Conditions for Constrained Problems / 4.4：
Constraint Qualifications. / Chapter 5：
Cone of Tangents / 5.1：
Other Constraint Qualifications / 5.2：
Lagrangian Duality and Saddle Point Optimality Conditions. / 5.3：
Lagrangian Dual Problem / 6.1：
Duality Theorems and Saddle Point Optimality Conditions / 6.2：
Properties of the Dual Function / 6.3：
Formulating and Solving the Dual Problem / 6.4：
Getting the Primal Solution / 6.5：
Linear and Quadratic Programs / 6.6：
Algorithms and Their Convergence / Part 3：
The Concept of an Algorithm. / Chapter 7：
Algorithms and Algorithmic Maps / 7.1：
Closed Maps and Convergence / 7.2：
Composition of Mappings / 7.3：
Comparison Among Algorithms / 7.4：
Unconstrained Optimization. / Chapter 8：
Line Search Without Using Derivatives / 8.1：
Line Search Using Derivatives / 8.2：
Some Practical Line Search Methods / 8.3：
Closedness of the Line Search Algorithmic Map / 8.4：
Multidimensional Search Without Using Derivatives / 8.5：
Multidimensional Search Using Derivatives / 8.6：
Modification of Newton's Method: Levenberg-Marquardt and Trust Region Methods / 8.7：
Methods Using Conjugate Directions: Quasi-Newton and Conjugate Gradient Methods / 8.8：
Subgradient Optimization Methods / 8.9：
Penalty and Barrier Functions. / Chapter 9：
Concept of Penalty Functions / 9.1：
Exterior Penalty Function Methods / 9.2：
Exact Absolute Value and Augmented Lagrangian Penalty Methods / 9.3：
Barrier Function Methods / 9.4：
Polynomial-Time Interior Point Algorithms for Linear Programming Based on a Barrier Function / 9.5：
Methods of Feasible Directions. / Chapter 10：
Method of Zoutendijk / 10.1：
Convergence Analysis of the Method of Zoutendijk / 10.2：
Successive Linear Programming Approach / 10.3：
Successive Quadratic Programming or Projected Lagrangian Approach / 10.4：
Gradient Projection Method of Rosen / 10.5：
Reduced Gradient Method of Wolfe and Generalized Reduced Gradient Method / 10.6：
Convex-Simplex Method of Zangwill / 10.7：
Effective First- and Second-Order Variants of the Reduced Gradient Method / 10.8：
Linear Complementary Problem, and Quadratic, Separable, Fractional, and Geometric Programming. / Chapter 11：
Linear Complementary Problem / 11.1：
Convex and Nonconvex Quadratic Programming: Global Optimization Approaches / 11.2：
Separable Programming / 11.3：
Linear Fractional Programming / 11.4：
Geometric Programming / 11.5：
Mathematical Review. / Appendix A：
Summary of Convexity, Optimality Conditions, and Duality. / Appendix B：
Bibliography.
Index
Introduction
Convex Analysis
Convex Sets
Convex Functions and Generalizations
Optimality Conditions and Duality
The Fritz John and Karush-Kuhn-Tucker Optimality Conditions
Constraint Qualification
Lagrangian Duality and Saddle Point Optimality Conditions
The Concept of an Algorithm
Unconstrained Optimization
Penalty and Barrier Functions
Methods of Feasible Directions
Linear Complementary Problem, and Quadratic, Separable, Fractional, and Geometric Programming
Mathematical Review
Summary of Convexity, Optimality Conditions, and Duality
Bibliography
Introduction. / Chapter 1：
Problem Statement and Basic Definitions / 1.1：
Illustrative Examples / 1.2：
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