Algorithms and data structures - Info and Reading Options

"Algorithms and data structures" Table Of Contents:

• 1- Appetizer: Integer Arithmetics
• 2- Addition
• 3- Multiplication: The School Method
• 4- Result Checking
• 5- A Recursive version of the School Method
• 6- Karatsuba Multiplication
• 7- Algorithm Engineering
• 8- The Programs
• 9- Proofs of Lemma 1.5 and Theorem 1.7
• 10- Implementation Notes
• 11- Historical Notes and Further Findings
• 12- Introduction
• 13- Asymptotic Notation
• 14- The Machine Model
• 15- Pseudocode
• 16- Designing Correct Algorithms and Programs
• 17- An Example - Binary Search
• 18- Basic Algorithm Analysis
• 19- Average-Case Analysis
• 20- Randomized Algorithms
• 21- Graphs
• 22- P and NP
• 23- Implementation Notes
• 24- Historical Notes and Further Findings
• 25- Representing Sequences by Arrays and Linked Lists
• 26- Linked Lists
• 27- Unbounded Arrays
• 28- *Amortized Analysis
• 29- Stacks and Queues
• 30- Lists Versus Arrays
• 31- Implementation Notes
• 32- Historical Notes and Further Findings
• 33- Hash Tables and Associative Arrays
• 34- Hashing and Chaining
• 35- Universal Hashing
• 36- Hashing with Linear Probing
• 37- Chaining Versus Linear Probing
• 38- *Perfect Hashing
• 39- Implementation Notes
• 40- Historical Notes and Further Findings
• 41- Sorting and Selection
• 42- Simple Sorters
• 43- Mergesort - an O(n log n) Sorting Algorithm
• 44- A Lower Bound
• 45- Quicksort
• 46- Selection
• 47- Breaking the Lower Bound
• 48- *External Sorting
• 49- Implementation Notes
• 50- Historical Notes and Further Findings
• 51- Priority Queues
• 52- Binary Heaps
• 53- Addressable Priority Queues
• 54- *External Memory
• 55- Implementation Notes
• 56- Historical Notes and Further Findings
• 57- Sorted Sequences
• 58- Binary Search Trees
• 59- (a, b)-Trees and Red-Black Trees
• 60- More Operations
• 61- Amortized Analysis of Update Operations
• 62- Augmented Search Trees
• 63- Implementation Notes
• 64- Historical Notes and Further Findings
• 65- Graph Representation
• 66- Unordered Edge Sequences
• 67- Adjacency Arrays - Static Graphs
• 68- Adjacency Lists - Dynamic Graphs
• 69- The Adjacency Matrix Representation
• 70- Implicit Representations
• 71- Implementation Notes
• 72- Historical Notes and Further Findings
• 73- Graph Traversal
• 74- Breadth-First Search
• 75- Depth-First Search
• 76- Implementation Notes
• 77- Historical Notes and Further Findings
• 78- Shortest Paths
• 79- From Basic Concepts to a Generic Algorithm
• 80- Directed Acyclic Graphs
• 81- Nonnegative Edge Costs (Dijkstra's Algorithm)
• 82- *Average-Case Analysis of Dijkstra's Algorithm
• 83- Monotone Integer Priority Queues
• 84- Arbitrary Edge Costs (Bellman-Ford Algorithm)
• 85- All-Pairs Shortest Paths and Node Potentials
• 86- Shortest-Path Queries
• 87- Implementation Notes
• 88- Historical Notes and Further Findings
• 89- Minimum Spanning Trees
• 90- Cut and Cycle Properties
• 91- The Jarnik-Prim Algorithm
• 92- Kruskal's Algorithm
• 93- The Union-Find Data Structure
• 94- *External Memory
• 95- Applications
• 96- Implementation Notes
• 97- Historical Notes and Further Findings
• 98- Generic Approaches to Optimization
• 99- Linear Programming - a Black-Box Solver
• 100- Greedy Algorithms - Never Look Back
• 101- Dynamic Programming - Building It Piece by Piece
• 102- Systematic Search - When in Doubt, Use Brute Force
• 103- Local Search - Think Globally, Act Locally
• 104- Evolutionary Algorithms
• 105- Implementation Notes
• 106- Historical Notes and Further Findings
• 107- Appendix
• 108- Mathematical Symbols
• 109- Mathematical Concepts
• 110- Basic Probability Theory
• 111- Useful Formulae
• 112- References
• 113- Index

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