数据结构图投资组合分配
使用邻接矩阵或邻接表和各种算法（例如深度优先搜索，广度优先搜索和Dijkstra算法）存储的无向和有向图抽象数据类型的Python3实现。 该项目在2021年冬季季度完成，在Tim Alcon教授的指导下，在OSU的CS 261数据结构课程中完成。
这两个文件中已经提供了各种测试用例。 只需运行：
$ ./d_graph.py
或者
$ ./ud_graph.py
2022-10-08 20:29:02
7KB
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PDG：程序依赖图
由以下规则定义的非常基本的命令性程序的程序依赖关系图创建器。
assignment := x=Aexpr;
conditional1 := if(Bexpr){ stmts; } else {stmts; } endif
conditional2 := if(Bexpr){ stmts; } endif
iterative := while(Bexpr){stmts; }
stmt := assignment | conditionsal1 | conditional2 | iterative
stmts := stmt | stmts; stmt
Aexpr: = constant | variable | Aexpr + Aexpr | Aexpr – Aexpr | Aexpr * Aexpr | Aexpr /
2022-10-06 13:03:18
294KB
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深度优先遍历，宽度优先遍历. 程序从图文本中读取图的矩阵。 矩阵包括有向图或无向图
2022-09-24 17:00:22
6KB
一种新的基于特征选择的图同构算法在网络模体发现中的应用，呼加璐，孙玲，模体是在真实网络中出现频率明显高于随机网络中出现频率的子图。模体查找需要对出现的子图计数，因此引发了子图同构的问题。但是
2022-09-24 15:11:01
327KB
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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
2022-09-22 08:37:18
18.54MB
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Kinect VFX图形
这是一个如何将Kinect中的深度相机提要与Unity的VFX图形一起使用的示例。 该项目中的代码在很大程度上受到高桥敬二郎（ Takahashi）的的启发。
要求
支持VFX图形的Unity版本
Unity软件包依赖于Kinect for Windows Unity软件包，可在单独下载。
如何使用
将Kinect VFX预制件添加到场景中。 在您的VFX图形中，使用KinectPointCloudMap渲染纹理作为“从地图设置位置”节点的输入。 KinectColorMap渲染纹理中提供了每个深度点的颜色数据。
另外，您也可以使用Kinect VFX映射的预制
2022-09-20 18:47:36
579KB
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