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Design & Analysis of Algorithm

Brand: Ashish Kumar
MPN: com.faadooengineers.free_designanalysisofalgorithm
Category: App (Science)
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Features

  • 1. Introduction to Algorithms
  • 2. Efficiency of algorithm
  • 3. Analysis of insertion sort
  • 4. Insertion sort
  • 5. The divide-and-conquer approach
  • 6. Analyzing divide-and-conquer algorithms
  • 7. Asymptotic notation
  • 8. Asymptotic notation in equations and inequalities
  • 9. Standard notations and common functions
  • 10. The hiring problem
  • 11. Indicator random variables
  • 12. Balls and bins
  • 13. Probabilistic analysis and further uses of indicator random variables
  • 14. Streaks
  • 15. The on-line hiring problem
  • 16. Overview of Recurrences
  • 17. The substitution method for recurrences
  • 18. The recursion-tree method
  • 19. The master method
  • 20. Proof of the master theorem
  • 21. The proof for exact powers
  • 22. Floors and ceilings
  • 23. Randomized algorithms
  • 24. Heaps
  • 25. Maintaining the heap property
  • 26. Building a heap
  • 27. The heapsort algorithm
  • 28. Priority queues
  • 29. Description of quicksort
  • 30. Performance of quicksort
  • 31. A randomized version of quicksort
  • 32. Analysis of quicksort
  • 33. Lower bounds for sorting
  • 34. Counting sort
  • 35. Radix sort
  • 36. Minimum and maximum
  • 37. Selection in expected linear time
  • 38. Bucket sort
  • 39. Selection in worst-case linear time
  • 40. Stacks and queues
  • 41. Linked lists
  • 42. Implementing pointers and objects
  • 43. Representing rooted trees
  • 44. Direct-address tables
  • 45. Hash tables
  • 46. Hash functions
  • 47. Open addressing
  • 48. Perfect hashing
  • 49. introduction to binary search tree
  • 50. Querying a binary search tree
  • 51. Insertion and deletion
  • 52. Randomly built binary search trees
  • 53. Red-Black Trees
  • 54. Rotations of red black tree
  • 55. Insertion in red black tree
  • 56. Deletion in red black tree
  • 57. Dynamic order statistics
  • 58. Augmenting a Data Structure
  • 59. Interval Trees
  • 60. Overview of Dynamic Programming
  • 61. Assembly-line scheduling
  • 62. Matrix-chain multiplication
  • 63. Elements of dynamic programming
  • 64. Longest common subsequence
  • 65. Optimal binary search trees
  • 66. Greedy Algorithms
  • 67. Elements of the greedy strategy
  • 68. Huffman codes
  • 69. Theoretical foundations for greedy methods
  • 70. A task-scheduling problem
  • 71. Aggregate analysis
  • 72. The accounting method
  • 73. The potential method
  • 74. Dynamic tables
  • 75. B-Trees
  • 76. Definition of B-trees
  • 77. Basic operations on B-trees
  • 78. Deleting a key from a B-tree
  • 79. Binomial Heaps
  • 80. Operations on binomial heaps
  • 81. Fibonacci Heaps
  • 82. Mergeable-heap operations
  • 83. Decreasing a key and deleting a node
  • 84. Bounding the maximum degree
  • 85. Data Structures for Disjoint Sets
  • 86. Linked-list representation of disjoint sets
  • 87. Disjoint-set forests
  • 88. Analysis of union by rank with path compression
  • 89. Representations of graphs
  • 90. Breadth-first search
  • 91. Depth-first search
  • 92. Topological sort
  • 93. Strongly connected components
  • 94. Minimum Spanning Trees
  • 95. Growing a minimum spanning tree
  • 96. The algorithms of Kruskal and Prim
  • 97. Single-Source Shortest Paths
  • 98. The Bellman-Ford algorithm
  • 99. Single-source shortest paths in directed acyclic graphs
  • 100. Dijkstra's algorithm
  • 101. Difference constraints and shortest paths
  • 102. Shortest paths and matrix multiplication
  • 103. The Floyd-Warshall algorithm
  • 104. Johnson's algorithm for sparse graphs
  • 105. Flow networks
  • 106. The Ford-Fulkerson method
  • 107. Maximum bipartite matching
  • 108. Push-relabel algorithms
  • 109. The relabel-to-front algorithm
  • 110. Comparison networks

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