EBK EXPERIENCING MIS,
8th Edition
ISBN: 9780134792729
Author: BOYLE
Publisher: PEARSON CUSTOM PUB.(CONSIGNMENT)
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Expert Solution & Answer
Chapter 4.2, Problem 1SW
Explanation of Solution
Trends of the televisions:
The recent developments of the TV in CES 2016 are given below:
- OLED TVs offers more brands with low price.
- The first UHD (Ultra High Definition) Blu-ray models TV.
- This type of TV displays the pictures in this range 4K (2160 pixels) and 8K (4320 pixels).
- HDR (High Dynamic Range) and wider color TV.
- The arrival of 4K UHD Blu-ray may push TV industry by adding the HDR and wider color gamuts.
- It defines the wider range among the whitest whites and blackest blacks in image.
- Chinese brands TV.
- Many companies surprised with 65-inch ULED TV – Hisense 65H10B television.
- CES shows the recent brand of Hisense and sharp branded Television.
- Another brand is TCL which shows the 55-inche UHD TV – TCL 55H9700...
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Students have asked these similar questions
Design a dynamic programming algorithm for the Longest Alternating Subsequence problem
described below:
Input: A sequence of n integers
Output: The length of the longest subsequence where the numbers alternate between being larger and
smaller than their predecessor
The algorithm must take O(n²) time. You must also write and explain the recurrence.
Example 1:
Input: [3, 5, 4, 1, 3, 6, 5, 7, 3, 4]
Output: 8 ([3, 5, 4, 6, 5, 7, 3, 4])
Example 2:
Input: [4,7,2,5,8, 3, 8, 0, 4, 7, 8]
Output: 8 ([4, 7, 2, 5, 3, 8, 0,4])
(Take your time with this for the subproblem for this one)
Design a dynamic programming algorithm for the Coin-change problem described below:
Input: An amount of money C and a set of n possible coin values with an unlimited supply of each
kind of coin.
Output: The smallest number of coins that add up to C exactly, or output that no such set exists.
The algorithm must take O(n C) time. You must also write and explain the recurrence.
Example 1:
Input: C24, Coin values = = [1, 5, 10, 25, 50]
Output: 6 (since 24 = 10+ 10+1+1 +1 + 1)
Example 2:
Input: C = 86, Coin values = [1, 5, 6, 23, 35, 46, 50]
Output: 2 (since 86 = 46+35+5)
Design a dynamic programming algorithm for the Longest Common Subsequence problem de-
scribed below
Input: Two strings x = x1x2 xm and y = Y1Y2... Yn
Output: The length of the longest subsequence that is common to both x and y.
.
The algorithm must take O(m n) time. You must also write and explain the recurrence.
(I want the largest k such that there are 1 ≤ i₁ < ... < ik ≤ m and 1 ≤ j₁ < ... < jk ≤ n such that
Xi₁ Xi2 Xik = Yj1Yj2 ··· Yjk)
Example 1:
Input: x = 'abcdefghijklmnopqrst' and y = 'ygrhnodsh ftw'
Output: 6 ('ghnost' is the longest common subsequence to both strings)
Example 2:
Input: x = 'ahshku' and y = ‘asu'
Output: 3 ('asu' is the longest common subsequence to both strings)
Chapter 4 Solutions
EBK EXPERIENCING MIS,
Ch. 4.2 - Prob. 1SWCh. 4.2 - Prob. 2SWCh. 4.2 - Prob. 3SWCh. 4.2 - Prob. 4SWCh. 4.2 - Prob. 5SWCh. 4.2 - Prob. 6SWCh. 4 - Prob. 1EGDQCh. 4 - Prob. 2EGDQCh. 4 - Prob. 3EGDQCh. 4 - Prob. 4EGDQ
Ch. 4 - Prob. 1ARQCh. 4 - Prob. 2ARQCh. 4 - Prob. 3ARQCh. 4 - Prob. 4ARQCh. 4 - Prob. 1UYKCh. 4 - Prob. 2UYKCh. 4 - Prob. 3UYKCh. 4 - Prob. 4CECh. 4 - Prob. 5CECh. 4 - Prob. 6CECh. 4 - Prob. 7CECh. 4 - Prob. 8CECh. 4 - Prob. 9CSCh. 4 - Prob. 10CSCh. 4 - Prob. 11CSCh. 4 - Prob. 12CSCh. 4 - Prob. 13CSCh. 4 - Prob. 14MLMCh. 4 - Prob. 15MLM
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