线性和非线性规划 第3版
Chapter 1.Introduction
1.1.Optimization
1.2.Types of Problems
1.3.Size of Problems
1.4.Iterative Algorithms and Convergence
PART Ⅰ Linear Programming
Chapter 2.Basic Properties of Linear Programs
2.1.Introduction
2.2.Examples of Linear Programming …
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1.1.Optimization
1.2.Types of Problems
1.3.Size of Problems
1.4.Iterative Algorithms and Convergence
PART Ⅰ Linear Programming
Chapter 2.Basic Properties of Linear Programs
2.1.Introduction
2.2.Examples of Linear Programming …
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David G. Luenberger(D.G.吕恩博格,美国)是国际知名学者，在数学和物理学界享有盛誉。本书凝聚了作者多年科研和教学成果，适用于科研工作者、高校教师和研究生。
这部研究运筹学的经典教材，在原来版本的基本上做了大量的修订补充，涵盖了这个运算领域的大量的理论洞见，是各行各业分析学者和运筹学研究人员所必需的。书中将运筹问题的纯分析特性和解决其的算术行为联系起来，将*鲜的手运筹学方法包括其中。目次：导论;（线性规划）:线性规划的基本性质；单纯型方法；对偶；内部点方法；运输和网络流问题；（无条件问题）解的基本特性和运算；基本下降方法；共轭方向法；拟牛顿法；（条件小化）条件小化条件；原始方法；惩罚和柱式开采法；对偶和割平面方法；原始对偶方法；附录A:数学回顾；凸集合；高斯估计。
读者对象：数学、特别是运筹学专业的高年级本科生、研究生和工程人员。
读者对象：数学、特别是运筹学专业的高年级本科生、研究生和工程人员。
Chapter 1.Introduction
1.1.Optimization
1.2.Types of Problems
1.3.Size of Problems
1.4.Iterative Algorithms and Convergence
PART Ⅰ Linear Programming
Chapter 2.Basic Properties of Linear Programs
2.1.Introduction
2.2.Examples of Linear Programming Problems
2.3.Basic Solutions
2.4.The Fundamental Theorem of Linear Programming
2.5.Relations to Convexity
2.6.Exercises
Chapter 3.The Simplex Method
3.1.Pivots
3.2.Adjacent Extreme Points
3.3.Determining a Minimum Feasible Solution
3.4.Computational Procedure—Simplex Method
3.5.Artificial Variables
3.6.Matrix Form of the Simplex Method
3.7.The Revised Simplex Method
3.8.The Simplex Method and LU Decomposition
3.9.Decomposition
3.10.Summary
3.11.Exercises
Chapter 4.Duality
4.1.Dual Linear Programs
4.2.The Duality Theorem
4.3.Relations to the Simplex Procedure
4.4.Sensitivity and Complementary Slackness
4.5.The Dual Simplex Method
4.6.The—Primal—Dual Algorithm
4.7.Reduction of Linear Inequalities
4.8.Exercises
Chapter 5.Interior—Point Methods
5.1.Elements of Complexity Theory
5.2.The Simplex Method is not Polynomial—Time
5.3.The Ellipsoid Method
5.4.The Analytic Center
5.5.The Central Path
5.6.Solution Strategies
5.7.Termination and Initialization
5.8.Summary
5.9.Exercises
Chapter 6.Transportation and Network Flow Problems
6.1.The Transportation Problem
6.2.Finding a Basic Feasible Solution
6.3.Basis Triangularity
6.4.Simplex Method for Transportation Problems
6.5.The Assignment Problem
6.6.Basic Network Concepts
6.7.Minimum Cost Flow
6.8.Maximal Flow
6.9.Summary
6.10.Exercises
PART Ⅱ Unconstrained Problems
Chapter 7.Basic Properties of Solutions and Algorithms
7.1.First—Order Necessary Conditions
7.2.Examples of Unconstrained Problems
7.3.Second—Order Conditions
7.4.Convex and Concave Functions
7.5.Minimization and Maximization of Convex Functions
7.6.Zero—Order Conditions
7.7.Global Convergence of Descent Algorithms
7.8.Speed of Convergence
7.9.Summary
7.10.Exercises
Chapter 8.Basic Descent Methods
8.1.Fibonacci and Golden Section Search
8.2.Line Search by Curve Fitting
8.3.Global Convergence of Curve Fitting
8.4.Closedness of Line Search Algorithms
8.5.Inaccurate Line Search
8.6.The Method of Steepest Descent
8.7.Applications of the Theory
8.8.Newton's Method
8.9.Coordinate Descent Methods
8.10.Spacer Steps
8.11.Summary
8.12.Exercises
Chapter 9.Conjugate Direction Methods
9.1.Conjugate Directions
9.2.Descent Properties of the Conjugate Direction Method
9.3.The Conjugate Gradient Method
9.4.The C—G Method as an Optimal Process
9.5.The Partial Conjugate Gradient Method
9.6.Extension to Nonquadratic Problems
9.7.Parallel Tangents
9.8.Exercises
Chapter 10.Quasi—Newton Methods
10.1.Modified Newton Method
10.2.Construction of the Inverse
10.3.Davidon—Fletcher—Powell Method
10.4.The Broyden Family
10.5.Convergence Properties
10.6.Scaling
10.7.Memoryless Quasi—Newton Methods
10.8.Combination of Steepest Descent and Newton's Method
10.9.Summary
10.10.Exercises
PART Ⅲ Constrained Minimization
Chapter 11.Constrained Minimization Conditions
1.1.Constraints
1.2.Tangent Plane
1.3.First—Order Necessary Conditions（Equality Constraints）
1.4.Examples
1.5.Second—Order Conditions
1.6.Eigenvalues in Tangent Subspace
1.7.Sensitivity
1.8.Inequality Constraints
1.9.Zero—Order Conditions and Lagrange Multipliers
1.10.Summary
1.11.Exercises
Chapter 12.Primal Methods
12.1.Advantage of Primal Methods
12.2.Feasible Direction Methods
12.3.Active Set Methods
12.4.The Gradient Projection Method
12.5.Convergence Rate of the Gradient Projection Method
12.6.The Reduced Gradient Method
12.7.Convergence Rate of the Reduced Gradient Method
12.8.Variations
12.9.Summary
12.10.Exercises
Chapter 13.Penalty and Barrier Methods
13.1.Penalty Methods
13.2.Barrier Methods
13.3.Properties of Penalty and Barrier Functions
13.4.Newton's Method and Penalty Functions
13.5.Conjugate Gradients and Penalty Methods
13.6.Normalization of Penalty Functions
13.7.Penalty Functions and Gradient Projection
13.8.Exact Penalty Functions
13.9.Summary
13.10.Exercises
Chapter 14.Dual and Cutting Plane Methods
14.1.Global Duality
14.2.Local Duality
14.3.Dual Canonical Convergence Rate
14.4.Separable Problems
14.5.Augmented Lagrangians
14.6.The Dual Viewpoint
14.7.Cutting Plane Methods
14.8.Kelley's Convex Cutting Plane Algorithm
14.9.Modifications
14.10.Exercises
Chapter 15.Primal—Dual Methods
15.1.The Standard Problem
15.2.Strategies
15.3.A Simple Merit Function
15.4.Basic Primal—Dual Methods
15.5.Modified Newton Methods
15.6.Descent Properties
15.7.Rate of Convergence
15.8.Interior Point Methods
15.9.Semidefinite Programming
15.10.Summary
15.11.Exercises
Appendix A.Mathematical Review
A.1.Sets
A.2.Matrix Notation
A.3.Spaces
A.4.Eigenvalues and Quadratic Forms
A.5.Topological Concepts
A.6.Functions
Appendix B.Convex Sets
B.1.Basic Definitions
B.2.Hyperplanes and Polytopes
B.3.Separating and Supporting Hyperplanes
B.4.Extreme Points
Appendix C.Gaussian Elimination
Bibliography
Index
^ 收 起
1.1.Optimization
1.2.Types of Problems
1.3.Size of Problems
1.4.Iterative Algorithms and Convergence
PART Ⅰ Linear Programming
Chapter 2.Basic Properties of Linear Programs
2.1.Introduction
2.2.Examples of Linear Programming Problems
2.3.Basic Solutions
2.4.The Fundamental Theorem of Linear Programming
2.5.Relations to Convexity
2.6.Exercises
Chapter 3.The Simplex Method
3.1.Pivots
3.2.Adjacent Extreme Points
3.3.Determining a Minimum Feasible Solution
3.4.Computational Procedure—Simplex Method
3.5.Artificial Variables
3.6.Matrix Form of the Simplex Method
3.7.The Revised Simplex Method
3.8.The Simplex Method and LU Decomposition
3.9.Decomposition
3.10.Summary
3.11.Exercises
Chapter 4.Duality
4.1.Dual Linear Programs
4.2.The Duality Theorem
4.3.Relations to the Simplex Procedure
4.4.Sensitivity and Complementary Slackness
4.5.The Dual Simplex Method
4.6.The—Primal—Dual Algorithm
4.7.Reduction of Linear Inequalities
4.8.Exercises
Chapter 5.Interior—Point Methods
5.1.Elements of Complexity Theory
5.2.The Simplex Method is not Polynomial—Time
5.3.The Ellipsoid Method
5.4.The Analytic Center
5.5.The Central Path
5.6.Solution Strategies
5.7.Termination and Initialization
5.8.Summary
5.9.Exercises
Chapter 6.Transportation and Network Flow Problems
6.1.The Transportation Problem
6.2.Finding a Basic Feasible Solution
6.3.Basis Triangularity
6.4.Simplex Method for Transportation Problems
6.5.The Assignment Problem
6.6.Basic Network Concepts
6.7.Minimum Cost Flow
6.8.Maximal Flow
6.9.Summary
6.10.Exercises
PART Ⅱ Unconstrained Problems
Chapter 7.Basic Properties of Solutions and Algorithms
7.1.First—Order Necessary Conditions
7.2.Examples of Unconstrained Problems
7.3.Second—Order Conditions
7.4.Convex and Concave Functions
7.5.Minimization and Maximization of Convex Functions
7.6.Zero—Order Conditions
7.7.Global Convergence of Descent Algorithms
7.8.Speed of Convergence
7.9.Summary
7.10.Exercises
Chapter 8.Basic Descent Methods
8.1.Fibonacci and Golden Section Search
8.2.Line Search by Curve Fitting
8.3.Global Convergence of Curve Fitting
8.4.Closedness of Line Search Algorithms
8.5.Inaccurate Line Search
8.6.The Method of Steepest Descent
8.7.Applications of the Theory
8.8.Newton's Method
8.9.Coordinate Descent Methods
8.10.Spacer Steps
8.11.Summary
8.12.Exercises
Chapter 9.Conjugate Direction Methods
9.1.Conjugate Directions
9.2.Descent Properties of the Conjugate Direction Method
9.3.The Conjugate Gradient Method
9.4.The C—G Method as an Optimal Process
9.5.The Partial Conjugate Gradient Method
9.6.Extension to Nonquadratic Problems
9.7.Parallel Tangents
9.8.Exercises
Chapter 10.Quasi—Newton Methods
10.1.Modified Newton Method
10.2.Construction of the Inverse
10.3.Davidon—Fletcher—Powell Method
10.4.The Broyden Family
10.5.Convergence Properties
10.6.Scaling
10.7.Memoryless Quasi—Newton Methods
10.8.Combination of Steepest Descent and Newton's Method
10.9.Summary
10.10.Exercises
PART Ⅲ Constrained Minimization
Chapter 11.Constrained Minimization Conditions
1.1.Constraints
1.2.Tangent Plane
1.3.First—Order Necessary Conditions（Equality Constraints）
1.4.Examples
1.5.Second—Order Conditions
1.6.Eigenvalues in Tangent Subspace
1.7.Sensitivity
1.8.Inequality Constraints
1.9.Zero—Order Conditions and Lagrange Multipliers
1.10.Summary
1.11.Exercises
Chapter 12.Primal Methods
12.1.Advantage of Primal Methods
12.2.Feasible Direction Methods
12.3.Active Set Methods
12.4.The Gradient Projection Method
12.5.Convergence Rate of the Gradient Projection Method
12.6.The Reduced Gradient Method
12.7.Convergence Rate of the Reduced Gradient Method
12.8.Variations
12.9.Summary
12.10.Exercises
Chapter 13.Penalty and Barrier Methods
13.1.Penalty Methods
13.2.Barrier Methods
13.3.Properties of Penalty and Barrier Functions
13.4.Newton's Method and Penalty Functions
13.5.Conjugate Gradients and Penalty Methods
13.6.Normalization of Penalty Functions
13.7.Penalty Functions and Gradient Projection
13.8.Exact Penalty Functions
13.9.Summary
13.10.Exercises
Chapter 14.Dual and Cutting Plane Methods
14.1.Global Duality
14.2.Local Duality
14.3.Dual Canonical Convergence Rate
14.4.Separable Problems
14.5.Augmented Lagrangians
14.6.The Dual Viewpoint
14.7.Cutting Plane Methods
14.8.Kelley's Convex Cutting Plane Algorithm
14.9.Modifications
14.10.Exercises
Chapter 15.Primal—Dual Methods
15.1.The Standard Problem
15.2.Strategies
15.3.A Simple Merit Function
15.4.Basic Primal—Dual Methods
15.5.Modified Newton Methods
15.6.Descent Properties
15.7.Rate of Convergence
15.8.Interior Point Methods
15.9.Semidefinite Programming
15.10.Summary
15.11.Exercises
Appendix A.Mathematical Review
A.1.Sets
A.2.Matrix Notation
A.3.Spaces
A.4.Eigenvalues and Quadratic Forms
A.5.Topological Concepts
A.6.Functions
Appendix B.Convex Sets
B.1.Basic Definitions
B.2.Hyperplanes and Polytopes
B.3.Separating and Supporting Hyperplanes
B.4.Extreme Points
Appendix C.Gaussian Elimination
Bibliography
Index
^ 收 起
David G. Luenberger(D.G.吕恩博格,美国)是国际知名学者，在数学和物理学界享有盛誉。本书凝聚了作者多年科研和教学成果，适用于科研工作者、高校教师和研究生。
这部研究运筹学的经典教材，在原来版本的基本上做了大量的修订补充，涵盖了这个运算领域的大量的理论洞见，是各行各业分析学者和运筹学研究人员所必需的。书中将运筹问题的纯分析特性和解决其的算术行为联系起来，将*鲜的手运筹学方法包括其中。目次：导论;（线性规划）:线性规划的基本性质；单纯型方法；对偶；内部点方法；运输和网络流问题；（无条件问题）解的基本特性和运算；基本下降方法；共轭方向法；拟牛顿法；（条件小化）条件小化条件；原始方法；惩罚和柱式开采法；对偶和割平面方法；原始对偶方法；附录A:数学回顾；凸集合；高斯估计。
读者对象：数学、特别是运筹学专业的高年级本科生、研究生和工程人员。
读者对象：数学、特别是运筹学专业的高年级本科生、研究生和工程人员。
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