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| Introduction. | |
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| Problem Statement and Basic Definitions | |
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| Illustrative Examples | |
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| Guidelines for Model Construction | |
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| Exercises | |
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| Notes and References | |
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| Convex Analysis. | |
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| Convex Sets. | |
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| Convex Hulls | |
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| Closure and Interior of a Set | |
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| Weierstrass's Theorem | |
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| Separation and Support of Sets | |
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| Convex Cones and Polarity | |
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| Polyhedral Sets, Extreme Points, and Extreme Directions | |
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| Linear Programming and the Simplex Method | |
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| Exercises | |
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| Notes and References | |
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| Convex Functions and Generalizations. | |
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| Definitions and Basic Properties | |
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| Subgradients of Convex Functions | |
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| Differentiable Convex Functions | |
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| Minima and Maxima of Convex Functions | |
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| Generalizations of Convex Functions | |
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| Exercises | |
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| Notes and References | |
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| Optimality Conditions and Duality. | |
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| The Fritz John and Karush-Kuhn-Tucker Optimality Conditions. | |
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| Unconstrained Problems | |
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| Problems Having Inequality Constraints | |
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| Problems Having Inequality and Equality Constraints | |
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| Second-Order Necessary and Sufficient Optimality Conditions for Constrained Problems | |
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| Exercises | |
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| Notes and References | |
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| Constraint Qualifications. | |
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| Cone of Tangents | |
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| Other Constraint Qualifications | |
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| Problems Having Inequality and Equality Constraints | |
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| Exercises | |
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| Notes and References | |
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| Lagrangian Duality and Saddle Point Optimality Conditions. | |
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| Lagrangian Dual Problem | |
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| Duality Theorems and Saddle Point Optimality Conditions | |
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| Properties of the Dual Function | |
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| Formulating and Solving the Dual Problem | |
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| Getting the Primal Solution | |
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| Linear and Quadratic Programs | |
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| Exercises | |
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| Notes and References | |
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| Algorithms and Their Convergence | |
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| The Concept of an Algorithm. | |
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| Algorithms and Algorithmic Maps | |
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| Closed Maps and Convergence | |
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| Composition of Mappings | |
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| Comparison Among Algorithms | |
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| Exercises | |
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| Notes and References | |
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| Unconstrained Optimization. | |
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| Line Search Without Using Derivatives | |
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| Line Search Using Derivatives | |
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| Some Practical Line Search Methods | |
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| Closedness of the Line Search Algorithmic Map | |
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| Multidimensional Search Without Using Derivatives | |
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| Multidimensional Search Using Derivatives | |
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| Modification of Newton's Method: Levenberg-Marquardt and Trust Region Methods | |
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| Methods Using Conjugate Directions: Quasi-Newton and Conjugate Gradient Methods | |
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| Subgradient Optimization Methods | |
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| Exercises | |
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| Notes and References | |
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| Penalty and Barrier Functions. | |
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| Concept of Penalty Functions | |
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| Exterior Penalty Function Methods | |
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| Exact Absolute Value and Augmented Lagrangian Penalty Methods | |
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| Barrier Function Methods | |
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| Polynomial-Time Interior Point Algorithms for Linear Programming Based on a Barrier Function | |
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| Exercises | |
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| Notes and References | |
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| Methods of Feasible Directions. | |
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| Method of Zoutendijk | |
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| Convergence Analysis of the Method of Zoutendijk | |
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| Successive Linear Programming Approach | |
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| Successive Quadratic Programming or Projected Lagrangian Approach | |
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| Gradient Projection Method of Rosen | |
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| Reduced Gradient Method of Wolfe and Generalized Reduced Gradient Method | |
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| Convex-Simplex Method of Zangwill | |
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| Effective First- and Second-Order Variants of the Reduced Gradient Method | |
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| Exercises | |
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| Notes and References | |
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| Linear Complementary Problem, and Quadratic, Separable, Fractional, and Geometric Programming. | |
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| Linear Complementary Problem | |
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| Convex and Nonconvex Quadratic Programming: Global Optimization Approaches | |
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| Separable Programming | |
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| Linear Fractional Programming | |
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| Geometric Programming | |
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| Exercises | |
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| Notes and References | |
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| Mathematical Review. | |
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| Summary of Convexity, Optimality Conditions, and Duality. | |
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| Bibliography. | |
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| Index | |