非常好的组合优化书籍，对于全面系统地了解组合优化有很大帮助。

组合博弈论的经典教材，从组合学的角度研究博弈论。 GSM系列146

A course in combinatorial optimization (A. Schrijver) 经典的组合优化教材

The fusion between graph theory and combinatorial optimization has led to theoretically profound and practically useful algorithms, yet there is no book that currently covers both areas together. Handbook of Graph Theory, Combinatorial Optimization, and Algorithms is the first to present a unified, comprehensive treatment of both graph theory and combinatorial optimization. Divided into 11 cohesive sections, the handbook’s 44 chapters focus on graph theory, combinatorial optimization, and algorithmic issues. The book provides readers with the algorithmic and theoretical foundations to: Understand phenomena as shaped by their graph structures Develop needed algorithmic and optimization tools for the study of graph structures Design and plan graph structures that lead to certain desirable behavior With contributions from more than 40 worldwide experts, this handbook equips readers with the necessary techniques and tools to solve problems in a variety of applications. Readers gain exposure to the theoretical and algorithmic foundations of a wide range of topics in graph theory and combinatorial optimization, enabling them to identify (and hence solve) problems encountered in diverse disciplines, such as electrical, communication, computer, social, transportation, biological, and other networks. Table of Contents SECTION I - Basic Concepts and Algorithms CHAPTER 1 - Basic Concepts in Graph Theory and Algorithms CHAPTER 2 - Basic Graph Algorithms CHAPTER 3 - Depth-First Search and Applications SECTION II - Flows in Networks CHAPTER 4 - Maximum Flow Problem CHAPTER 5 - Minimum Cost Flow Problem CHAPTER 6 - Multicommodity Flows SECTION III - Algebraic Graph Theory CHAPTER 7 - Graphs and Vector Spaces CHAPTER 8 - Incidence, Cut, and Circuit Matrices of a Graph CHAPTER 9 - Adjacency Matrix and Signal Flow Graphs CHAPTER 10 - Adjacency Spectrum and the Laplacian Spectrum of a Graph CHAPTER 11 - Resistance Networks, Random Walks, and Network Theorems SECTION IV - Structural Graph Theory CHAPTER 12 - Connectivity CHAPTER 13 - Connectivity Algorithms CHAPTER 14 - Graph Connectivity Augmentation CHAPTER 15 - Matchings CHAPTER 16 - Matching Algorithms CHAPTER 17 - Stable Marriage Problem CHAPTER 18 - Domination in Graphs CHAPTER 19 - Graph Colorings SECTION V - Planar Graphs CHAPTER 20 - Planarity and Duality CHAPTER 21 - Edge Addition Planarity Testing Algorithm CHAPTER 22 - Planarity Testing Based on PC-Trees CHAPTER 23 - Graph Drawing SECTION VI - Interconnection Networks CHAPTER 24 - Introduction to Interconnection Networks CHAPTER 25 - Cayley Graphs CHAPTER 26 - Graph Embedding and Interconnection Networks SECTION VII - Special Graphs CHAPTER 27 - Program Graphs CHAPTER 28 - Perfect Graphs CHAPTER 29 - Tree-Structured Graphs SECTION VIII - Partitioning CHAPTER 30 - Graph and Hypergraph Partitioning SECTION IX - Matroids CHAPTER 31 - Matroids CHAPTER 32 - Hybrid Analysis and Combinatorial Optimization SECTION X - Probabilistic Methods, Random Graph Models, and Randomized Algorithms CHAPTER 33 - Probabilistic Arguments in Combinatorics CHAPTER 34 - Random Models and Analyses for Chemical Graphs CHAPTER 35 - Randomized Graph Algorithms: Techniques and Analysis SECTION XI - Coping with NP-Completeness CHAPTER 36 - General Techniques for Combinatorial Approximation CHAPTER 37 - ε-Approximation Schemes for the Constrained Shortest Path Problem CHAPTER 38 - Constrained Shortest Path Problem: Lagrangian Relaxation-Based Algorithmic Approaches CHAPTER 39 - Algorithms for Finding Disjoint Paths with QoS Constraints CHAPTER 40 - Set-Cover Approximation CHAPTER 41 - Approximation Schemes for Fractional Multicommodity Flow Problems CHAPTER 42 - Approximation Algorithms for Connectivity Problems CHAPTER 43 - Rectilinear Steiner Minimum Trees CHAPTER 44 - Parameter Algorithms and Complexity

LEDA与cgal类似，也是开源的计算几何算法库，相比较而言，leda的源码更直观。

Papadimitriou.C.H,.Steiglitz.K..Combinatorial.Optimization..Algorithms.and.Complexity.djvu 不是俄文版了

关于模型预测控制简化数据，可以简化模型预测控制算法的计算量。

Metaheuristicsin Combinatorial Optimization Overview and Conceptual Comparison.pdf

这本书可以看作是一本教程，并且可以作为凸集，多面体，多面体，组合拓扑，Voronoi图和Delaunay三角剖分的一组注释。

随着社会的工业化，数学建模和仿真在产品设计中变得越来越重要。 当前，使用Modelica进行多域统一建模是复杂系统领域的主流技术。 使用Modelica对复杂物理系统进行建模通常会产生一个高指数微分代数方程（DAE）系统。 解决之前，需要先将其转换为低指数DAE。 结构索引约简算法是流行的索引约简方法之一。 但是在某些特殊情况下，其解决方案可能不正确。 目前，组合松弛算法是解决该问题的一种广泛使用的方法。 解决最大加权匹配是组合松弛算法的重要问题之一。 本文介绍了组合松弛算法，并针对最大加权匹配问题提出了匈牙利算法的三种不同实现。 理论结果与实验结果吻合。