(This book is easy to understand and comprehensively introduces computer algorithms. The analysis of each algorithm is easy to understand and very interesting, and adheres to mathematical rigor. The scheme of this book is comprehensive and applicable to multiple purposes. The contents include: the effect of algorithm in calculation, probability analysis and introduction of random algorithm. This book specifically reviews linear schemes, introduces two uses of dynamic schemes, randomization and approximate algorithms of linear scheme technology, as well as differentiation methods and desired linear time order statistics algorithms used in related recursive solutions and quick sorting, and comments on greedy algorithm elements. The book also introduces the verification of the correctness of the strongly connected subgraph algorithm and the NP completeness of the Hamilton circuit and subset sum problem. The book provides more than 900 exercise questions, thinking questions and more specific case studies. This book is rich in content and is a practical textbook for undergraduate data framework courses and graduate algorithm courses. This book is also a mathematical reference book or engineering practice manual on the desk in the reader's industry career. Table of Contents Part I Introduction to Basic Knowledge Chapter 1 Effect of Algorithms in Calculation 1.1 Algorithm 1.2 Algorithm as a Technology Chapter 2 Introduction to Algorithms 2.1 Insertion Sorting 2.2 Algorithm Analysis 2.3 Algorithm Scheme 2.3.1 Divided and Conquered Method 2.3.2 Divided and Conquered Method Analysis Chapter 3 Addition of Functions 3.1 Asymptotic Mark 3.2 Normative Mark and Common Functions Chapter 4 Regression 4.1 Substitution 4.2 Recursive Tree Method 4.3 Main Method 4.4 Confirmation of Main Theorem 4.4.1 Confirmation of Taking Positive Power 4.4.2 Rounding Function Number sum rounding function Chapter 5 Probability analysis and random algorithm 5.1 Recruitment question 5.2 Indicator random variable 5.3 Random algorithm 5.4 Further use of probability analysis and indicator random variable 5.4.1 Birthday paradox 5.4.2 Ball and box 5.4.3 Sequence Chapter 11 Hash List Chapter 12 Binary Search Tree Chapter 13 Red and Black Tree Chapter 14 Data Frame Extension Part 4 Introduction to High end Schemes and Analysis Techniques Chapter 15 Dynamic Schemes Chapter 16 Greedy Algorithms Chapter 17 Equal Sharing Analysis Part 5 Overview of High end Data Frames Chapter 18 B Tree Chapter 19 Binomial Stack Chapter 20 Fibonacci Stack Chapter 21 Data Framework on Disjoint Sets Part 6 Introduction to Graph Algorithms Chapter 22 Basic Algorithm of Graph Chapter 23 Minimum Spanning Tree Chapter 24 Single Source Shortest Path Chapter 25 Shortest Path between Each Pair of Item Points Chapter 26 Maximum Flow Part 7 Selected Introduction to Algorithm Research Problems Chapter 27 Sorting Network Chapter 28 Matrix Operation Chapter 29 Linear Scheme Chapter 30 Polynomial and Fast Fourier Transform Chapter 31 Algorithm of Related Number Theory Chapter 32 String Matching Chapter 33 Computational Geometry Chapter 34 NP Completeness Chapter 35 Approximate Algorithms Part 8 Appendix: Introduction to Basic Mathematical Knowledge A Sum B Set Discrete Mathematics Framework C Counting and Probability References
Introduction to Algorithms Second Edition. pdf)
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