TAILIEUCHUNG - Ebook A practical introduction to data structures and algorithm analysis: Part 1

(BQ) Part 1 book "A practical introduction to data structures and algorithm analysis" has contents: Preliminaries, data structures and algorithms, mathematical preliminaries, algorithm analysis, binary trees, non binary trees, internal sorting. | A Practical Introduction to Data Structures and Algorithm Analysis Third Edition (C++ Version) Clifford A. Shaffer Department of Computer Science Virginia Tech Blacksburg, VA 24061 January 19, 2010 Copyright c 2009-2010 by Clifford A. Shaffer. This document is made freely available for educational and other non-commercial use. You may make copies of this file and redistribute it without charge. You may extract portions of this document provided that the front page, including the title, author, and this notice are included. Any commercial use of this document requires the written consent of the author. The author can be reached at shaffer@. Further information about this text is available at ˜shaffer/Book/ Contents Preface xiii I Preliminaries 1 1 Data Structures and Algorithms A Philosophy of Data Structures The Need for Data Structures Costs and Benefits Abstract Data Types and Data Structures Design Patterns Flyweight Visitor Composite Strategy Problems, Algorithms, and Programs Further Reading Exercises 3 4 4 6 8 12 13 14 15 16 17 19 21 2 Mathematical Preliminaries Sets and Relations Miscellaneous Notation Logarithms Summations and Recurrences 25 25 29 31 33 iii iv Contents 3 II 4 Recursion Mathematical Proof Techniques Direct Proof Proof by Contradiction Proof by Mathematical Induction Estimating Further Reading Exercises Algorithm Analysis Introduction Best, Worst, and Average Cases A Faster Computer, or a Faster Algorithm? Asymptotic Analysis Upper Bounds Lower Bounds Θ Notation Simplifying Rules Classifying Functions Calculating the Running Time for a Program Analyzing Problems Common Misunderstandings Multiple Parameters Space Bounds Speeding Up Your Programs Empirical Analysis Further Reading Exercises .

• Kỹ thuật lập trình
• Algorithm analysis
• A practical introduction
• Mathematical preliminaries
• Algorithm analysis
• Binary trees
• Non binary trees
• Internal sorting
• Data structures
• Algorithm analysis in C++
• The disjoint sets class
• Algorithm design techniques
• Amortized analysis
• Advanced data structures
• Advanced Algorithms Analysis and Design
• Lecture Advanced Algorithms Analysis and Design
• Bài giảng Phân tích và thiết kế thuật toán nâng cao
• The Floyd Warshall algorithm
• Johnson’s algorithm
• Bellman Ford algorithm
• Algorithm design
• Lecture Algorithm design
• Computational tractability
• Asymptotic order of growth
• Polynomial running time
• Dynamic programming
• Hirschbergs algorithm
• Greedy Algorithms
• Dijkstras algorithm
• Minimum spanning trees
• Network flow
• Ford Fulkerson algorithm
• Max flow min cut theorem
• Analysis in C++
• A general overview
• Extendible hashing
• Hash tables in the standard library
• Performance analysis of a sugar plant using genetic algorithm
• Optimization of crystallization system of a sugar plant using genetic algorithm
• A sugar plant using genetic algorithm
• Refining system fails
• The crystallization system includes crystallization units
• Local search
• Gradient descent
• Metropolis algorithm
• Analysis of Algorithm
• Incorrect algorithms
• Analyzing an algorithm
• nalyzing an algorithm
• The correct output
• Communication bandwidth
• Computational time
• Graph search
• Graph connectivity
• Graph traversal
• Coin changing
• Optimal caching
• Counting inversions
• Closest pair of points
• Randomized quicksort
• Master theorem
• Integer multiplication
• Matrix multiplication
• Weighted interval scheduling
• Knapsack problem
• Bipartite matching
• Disjoint paths
• Assignment problem
• Input queued switching
• Poly time reductions
• Constraint satisfaction problems
• Graph coloring
• Decision problems
• NP complete
• Nondeterministic polynomial
• PSPACE complexity class
• Quantified satisfiability
• PSPACE complete
• Vertex covers
• Bipartite graphs
• Limits of tractability
• Approximation Algorithms
• Vertex cover
• Randomized Algorithms
• Contention resolution
• Linearity of expectation
• Performance analysis of a real time adaptive prediction algorithm
• Real time adaptive prediction algorithm for traffic congestion
• Lower Mean Square Error
• The actual traffic congestion states