运筹与管理科学丛书 9 锥优化的基于核函数的内点算法 英文版 2010年版

运筹与管理科学丛书 9 锥优化的基于核函数的内点算法 英文版
作　者： 袁亚湘 主编
出版时间： 2010年版
内容简介
　　《锥优化的基于核函数的内点算法》共分七章，第1章介绍锥优化理论方法的发展历程，第2章介绍核函数及其性质、由核函数确定的障碍函数的性质，第3～6章分别介绍中心路径的概念、锥的代数性质，给出求解线性规划问题、P*（k）线性互补问题、半正半优化问题、二阶锥优化问题的基于核函数的内点算法，分析了大步算法、小步内点算法的计算复杂性。《锥优化的基于核函数的内点算法》可作为运筹学专业的本科生、研究生关于内点算法的入门书，同时也可作为研究人员的关于内点算法的参考书。
目录
Preface
Chapter 1 Introduction
1.1 Conic optimization problems
1.2 Conic duality
1.3 From the dual cone to the dual problem
1.4 Development of the interior-point methods
1.5 Scope of the book
Chapter 2 Kernel Functions
2.1 Definition of kernel functions and basic properties
2.2 The further conditions of kernel functions
2.3 Properties of kernel functions
2.4 Examples of kernel functions
2.5 Barrier functions based on kernel functions
2.6 Generalization of kernel function
2.6.1 Finite kernel function
2.6.2 Parametric kernel function
Chapter 3 Kernel Function-based Interior-point Algorithm for LO
3.1 The central path for LO
3.2 The search directions for LO
3.3 The generic primal-dual interior-point algorithm for LO
3.4 Analysis of the algorithm
3.4.1 Decrease of the barrier function during an inner iteration
3.4.2 Choice of the step size
3.5 Iteration bounds
3.6 Summary of computation for complexity bound
3.7 Complexity analysis based on kernel functions
3.8 Summary of results
Chapter 4 Kernel Function-based Interior-point Algorithm for P*(k) LCP
4.1 The P*(k)-LCP
4.2 The central path for P*(k)-LCP
4.3 The new search directions for P*(k)-LCP
4.4 The generic primal-dual interior-point algorithm for P*(k)-LCP...
4.5 The properties of the barrier function
4.6 Analysis of the algorithm
4.6.1 Growth behavior of the barrier function
4.6.2 Determining the default step size
4.7 Decrease of the barrier function during an inner iteration
4.8 Complexity of the algorithm
4.8.1 Iteration bound for the large-update methods
4.8.2 Iteration bound for the small-update methods
Chapter 5 Kernel Function-based Interior-point Algorithm for SDO
5.1 Special matrix functions
5.2 The central path for SDO
5.3 The new search directions for SDO
5.4 The generic primal-dual interior-point algorithm for SDO
5.5 The properties of the barrier function
5.6 Analysis of the algorithm
5.6.1 Decrease of the barrier function during an inner iteration
5.6.2 Choice of the step size
5.7 Iteration bounds
5.8 Kernel function-based schemes
5.9 The example
5.10 Numerical results
Chapter 6 Kernel Function-based Interior-point Algorithm for SOCO
6.1 Algebraic properties of second-order cones
6.2 Barrier functions defined on second-order cone
6.3 Rescaling the cone
6.4 The central path for SOCO
6.5 The new search directions for SOCO
6.6 The generic primal-dual interior-point algorithm for SOCO
6.7 Analysis of the algorithm
6.8 The crucial inequality
6.9 Decrease of the barrier function during an inner iteration
6.10 Increase of the barrier function during a μ-update
6.11 Iteration-bounds
6.12 Numerical results
6.13 Some technical lemmas
Appendix Three Technical Lemmas
Reference