Exam 2 with Answers - Object-Oriented Programming and Data Structures | CS 2110, Exams of Computer Science

Download Exam 2 with Answers - Object-Oriented Programming and Data Structures | CS 2110 and more Exams Computer Science in PDF only on Docsity! CS211. Prelim 2, Spring 2004. Sample answers 1. (a) Worst-case time for n = k+1-h: O(n*n); Average-case time: O(n log n) (b) public static void quicksort(int[] b, int h, int k) { if (h+1 – k < 10) { insertionsort(b, h, k); return; } medianOf3(b, h, k); // It is ok to leave this out int j= partition(b, h, k); // { b[h..j–1] <= b[j] <= b[j+1..k] } if (j – h <= k – j) { quicksort(b, h, j–1); quicksort(b,j+1, k); } else { quicksort(b,j+1, k); quicksort(b, h, j–1); } } 2. To save space, we omit the method specs public DList(){ sentinel= new DNode(null, null, null); sentinel.next= sentinel; sentinel.prev= sentinel; current= sentinel; } public void insert(Object i){ DNode temp= new DNode(current, i, current.next); current.next.prev= temp; current.next= temp; current= temp; } public void remove(){ if (current == sentinel) throw new NoSuchElementException(); current.next.prev= current.prev; current.prev.next= current.next; if (current.prev != sentinel) current= current.prev; else current= current.next; } private class DNode { public Object value; // Value in the node public DNode next; // next node public DNode prev; // previous node /** Constructor: a node with value v, successor n, and precedessor p */ public DNode(DNode p, Object v, DNode n) { value= v; next= n; prev= p; } } 3. public static LNode inorder (TNode root, LNode head) { if (root.left != null) { head= inorder(root.left, head); } head.next= new LNode(); head= head.next; head.item= root.data; if (root.right != null) { head= inorder(root.right, head); } return head; } 4a. Function f(n) is O(n) iff there are positive constants c and n0 such that f(n) <= c*n for n ≥ n0. or f(n) is O(n) iff there is a positive constant c uch that f(n) <= c*n for all but a finite number of positive n. 4b. The method of question 3 is O(n) for a tree with n nodes. Each recursive call processes 1 node of the tree, and all the operations in the method body (except the recursive calls themselves) take constant time k (say). Since n calls are made in total, the time is k*n for some positive constant k. 4c. A heap is a binary tree that satisfies: (1) T is complete, i.e. with the nodes numbered in breadth-first order, if node n exists, so do nodes 0..n- 1. (2) The value of each node n of T is at least the values of its children. Note: we have specified a max-heap; in a min-heap, the value of each node would be at most the value of its children. 5a. Suppose we are looking for object ob in a hashtable h of size s. If object ob hashes to x then linear probing says to probe cells with index x, (x+1)% s, (x+2) % s, (x+3) % s, … until ob or an empty cell is found. 5b. (Without having to draw the diagram) after a: {null, (1,T), (8,T), null, (11,T), null, (13,T)} after b: {null, (1,T), (8,F), null, (11,T), null,( 13,T)} after c: {null, (1,T),(15,T), null,( 11,T), null,( 13,T)} or {null, (1,T),(8,F),(15,T),( 11,T), null,( 13,T)} Sorting the larger partition using a tail-recursive call reduces space to O(log n) if the language imple- ments tail recursion nicely.