資工研二 翁靜美

L 1 : 1, 2, 5, 6L 2 : 3,

Input:Two sorted lists, L 1 = (a 1, a 2,..., a n1 ) and L 2 = (b 1, b 2,..., b n2 ). Output:A sorted list consisting of elements in L 1 and L 2. Begin i : = 1 j : = 1 do compare a i and b j if a i > b j then output b j and j : = j + 1 else output a i and i : = i + 1 while (i  n 1 and j  n 2 ) if i > n 1 then output b j, b j+1,..., b n2, else output a i, a i+1,..., a n1. End. The worst case of merge sort L 1 : L 2 : The number of comparison required is m+n-1

 If more than two sorted lists are to be merged, we can still apply the linear merge algorithm.  These merging processes are called 2-way merge because each merging step only merges two sorted lists.

L2L2 30 elementsL3L3 10 elementsL1L1 50 elements L 1 +L 2 80 elements L 1 +L 2 +L 3 90 elements = = 89 the number of comparisons required in this merging sequence is 168

L2L2 30 elementsL3L3 10 elementsL1L1 50 elements L 2 +L 3 40 elements L 2 +L 3 +L 1 90 elements = = 89 the number of comparisons required is only 128

 There are m sorted lists.  Each of them consists of n i elements.  What is the optimal sequence of merging process to merge these sorted lists together by using the minimum number of comparisons?

Input:m sorted lists, L i, i = 1, 2,..., m, each L i consisting of n i elements. Output:An optimal 2-way merge tree. Step 1: Generate m trees, where each tree has exactly one node (external node) with weight n i. Step 2: Choose two trees T 1 and T 2 with minimal weights. Step 3: Create a new tree T whose root has T 1 and T 2 as its subtrees and weight is equal to the sum of weights T 1 and T 2. Step 4: Replace T 1 and T 2 by T. Step 5: If there is only one tree left, stop and return; otherwise, go to Step 2.

 For the given m numbers n 1, n 2, …, n m, we can construct a min-heap to represent these numbers where the root value is smaller than the values of its sons.  The main loop is executed (n-1) times:  Tree reconstruction after removing the root, which has the smallest value, can be done in O(log n) time.  The insertion of a new node into a min-heap also can be done in O(log n) time.  The total time to generate an optimal extended binary tree is O(n log n).

 In telecommunication, how do we represent a set of messages, each with an access frequency, by a sequence of 0’s and 1’s?  To minimize the transmission and decoding costs, we may use short strings to represent more frequently used messages.  This problem can by solved by using the 2-way merge algorithm.

A:2 B:3 E:13 G:18 C:5 D:8 F:15 A:2 B:3 5 5 C:5 10 D:8 18 E:13 F:15 28 G: A Huffman Code Tree Huffman Codes A10100 B10101 C1011 D100 E00 F01 G11

SymbolsfrequenciesHuffmanASCIIReduced Bits A B C D E F G