Data Structure and Algorithm

Handbook of Design analysis of Algorithms, learn a topic in a minute

The App Data Structure and Algorithm is designed for quick learning, revisions, references at the time of exams and interviews.

This App has 130 topics in 5 chapters, totally based on practical as well as a strong base of theoretical knowledge with notes written in very simple and understandable English.

This app cover most of related topics and Detailed explanation with all the basics topics.

Some of the topics Covered in the app are :

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

This app will useful for quick reference. The revision of all concepts can be finished within Several hour using this app.

Algorithms is part of computer science & software engineering education courses and information technology degree programs of various universities.

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