Mastering Data Structures & Algorithms with Java

1. Course Review 15-211 Fundamental Structures of Computer Science Ananda Guna May 04, 2006

2. This course was about • How to solve computing problems using: • Problem analysis, to abstract away details and divide into smaller subproblems. • Mathematical foundations for precise formulations of problems and solutions. • Data structures and algorithms to solve problems correctly and efficiently. • Java programming and modular software construction for good implementations.

3. Data Structures/Algorithms • -Complexity • big-O, little-O, Omega etc.. • Lists • linked, doubly, singly, multi-linked • Trees • general, B-trees, BST, splay, AVL, etc. • Stacks and Queues • operations, analysis • Heaps • binary, binomial • Hash Tables • collisions, implementation • Graphs • traversals, shortest paths • FSM • regular expressions, KMP • String Matching • KMP, Boyer-Moore, Rabin-Karp .

4. Data Structures/Algorithms • Dynamic Programming • table(bottom-up), recursion(top-down) • memoization • Game Trees • mini-max, alpha-beta, iterative deepening • - Sorting • bubble, quick, insertion, selection • Algorithms • findMax, findMedian, reverse, MST, compression • Traversals, shortest path

5. Java Programming Object oriented programming. Correctness (loop invariants, induction). Time and space complexity. Debugging (test harness).

6. Studying for the Final Exam • Review all material first • Lectures, programs, quizzes • Do sample finals – under time restrictions. • Old finals are in blackboard/course material • Types and difficulty of problems may be similar (some topics not covered)

7. Complexity

8. Complexity f(n)=n2 g(n) = n h(n) = log n f(n) is O(g(n)) iff f(n) is o(g(n)) iff f(n) is Ω(g(n)) iff f(n) is θ(g(n)) iff

9. Complexity True or False? If true prove it, else give counter example

10. Space Complexity • How much memory is needed to run the program • quicksort • merge Sort • insertion sort • Prim’s • Dijkstra’s Key Point: consider the sizes of the data structures used

11. Solving Recurrences, Asymptotics • Solve T(n) = 3 T(n/3) + 1 • Prove the correctness • By induction • Invariants • Hold initially • Preserved after each iteration of loop or method call • Or other methods

12. Complexity A is an array of integers

13. Complexity of known algorithms

14. Complexity of known algorithms

15. Complexity of known algorithms

16. Sorting

17. Sorting and lower bounds Insertion Sort Selection Sort Heapsort Mergesort Quicksort Radix Sort

18. Sorting and lower bounds

19. Questions

20. Trees

21. Balanced Binary Trees

22. AVL Trees • Traverse the tree inorder • Do exactly two rotations so that the tree is balanced • 3. Consider a binary tree of height h • What are the max and min number of nodes? • What are the max and min number of leaves? • 4. Perfect, full, complete trees

23. Splay Trees

24. Full, Complete and Perfect Trees • Definitions • Full, complete and perfect • Ex:Show that a full binary tree with n leaves has (n-1) internal nodes. (use induction on n) Therefore a full binary tree with n leaves has (2n-1) nodes • Ex: Start from the root and do a preorder traversal as follows. Write 1 for a node with 2 children. Visit left child, then right child. Write 0 if the node is a leaf. Therefore a full binary tree with 3 nodes can be represented by bit sequence 100. • What is the bit sequence representing a full binary tree with 8 leaves?

25. Code Examples

26. Dictionaries

27. Dictionaries

28. Hashing • Describe why hashing a string to sum of its characters is not a good idea. • Assume S = a1a2….an is a string • Define H(S) =  ai2i-1 (i=1..n) • Describe a way to efficiently calculate H(S)

29. Heaps

30. Priority Queues and Heaps • Suppose you implement a priority queue using following • Unsorted array • Linked list (smallest at front) • Heap • What is the complexity in each case for • deleteMin • insert

31. Binary heaps

32. Compression

33. Compression

34. LZW compression

35. Data Compression • Encode “I AM SAM SAM I AM SAM SAM” using • Huffman compression • LZW • In each case calculate the compression ratio • Is it possible to apply Huffman after applying LZW? • If so apply Huffman to output generated by LZW above

36. Graphs

37. Graphs Adjacency lists and matrices Traversal (DFS, BFS) Reachability (DFS, BFS, Warshall) Shortest Paths (Dijkstra, Bellman-Ford) MST (Prim, Kruskal)

38. Graphs • What is breadth first traversal(BFS)? • What is depth first traversal(DFS)? • What data structures can be used to implement each one? • Reachability • Connected components • Topological sort

39. Graphs

40. Greedy Algorithms 60 60 50 40 20 50 50 40 40 60 40 10 100 20 20 30 20 50 40 150 Find the Shortest Path from Node 1 to every other node

41. Dijkstra’s Algorithm

42. MST • Find the MST using • a. Prim’s Algorithm • b. Kruskal’s algorithm

43. Homework • A graph G is bipartite if the vertex set V can be partitioned into two subsets L and R, such that every edge has one vertex in L the other in R. Give an example of a bipartite graph. • G is a tree if it is connected and acyclic. What is the number is edges in a tree with n nodes? • 3. Draw all possible connected graphs G on 4 vertices and 3 edges. • 4. Draw all possible connected graphs G on 4 vertices and 4 edges. • 5. Suppose G is not connected. What the maximum number of edges in G? • 5. Prove or give a counterexample to the following claim: Any tree is bipartite.

44. Dynamic Programming

45. Dynamic Programming Dependent subproblems, recursive solutions, memoizing, explicit tables. Knapsack Longest Common Subsequence Matrix Multiplication

47. Game Trees

48. Game trees

49. Games 2-person, deterministic, complete information, ... Backtracking MiniMax Alpha-beta pruning Heuristics, iterative deepening, move order, tricks, ...

50. Disjoint Sets