Introduction to Algorithms
3rd Edition
ISBN: 9780262033848
Author: Thomas H. Cormen, Ronald L. Rivest, Charles E. Leiserson, Clifford Stein
Publisher: MIT Press
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Question
Chapter 22.1, Problem 8E
Program Plan Intro
To determine the time complexity of algorithmic procedure of knowing that whether the edge is the part of graph G or not. Also, tell the consequences of this procedure and suggest any other alternative.
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Students have asked these similar questions
Consider a hash table of size 7 with hash
function h (k) = k mod 7.
the following values: 44, 45, 79, 55, 91, 18,
63 are inserted into this hash table
Note: collisions are handled by linear
probing.
Finally, where is key 18 is located?
Select one:
a. 6
b. None
c. 3
d. 4
e. 5
If we represent a priority queue using a
sorted LinkList in decreasing order?
what is the time complexity for
1) add O (n)
2) peekMin O ( n/2)
3) removeMin operations O (log n)
Suppose we have a hash table with N slots containing n keys. Suppose that instead of a linked list in open
hashing, each slot is implemented as a binary search tree. Give the worst and the best time complexity of
adding an entry to this hash table. Explain your answers.
Worst Case
Best Case
As all the values are now lumped together, we have a hash map that was not constructed with great care (that is, they are all in the same LinkedList). Defend your position by detailing how this negates the need for hash maps.
Chapter 22 Solutions
Introduction to Algorithms
Ch. 22.1 - Prob. 1ECh. 22.1 - Prob. 2ECh. 22.1 - Prob. 3ECh. 22.1 - Prob. 4ECh. 22.1 - Prob. 5ECh. 22.1 - Prob. 6ECh. 22.1 - Prob. 7ECh. 22.1 - Prob. 8ECh. 22.2 - Prob. 1ECh. 22.2 - Prob. 2E
Ch. 22.2 - Prob. 3ECh. 22.2 - Prob. 4ECh. 22.2 - Prob. 5ECh. 22.2 - Prob. 6ECh. 22.2 - Prob. 7ECh. 22.2 - Prob. 8ECh. 22.2 - Prob. 9ECh. 22.3 - Prob. 1ECh. 22.3 - Prob. 2ECh. 22.3 - Prob. 3ECh. 22.3 - Prob. 4ECh. 22.3 - Prob. 5ECh. 22.3 - Prob. 6ECh. 22.3 - Prob. 7ECh. 22.3 - Prob. 8ECh. 22.3 - Prob. 9ECh. 22.3 - Prob. 10ECh. 22.3 - Prob. 11ECh. 22.3 - Prob. 12ECh. 22.3 - Prob. 13ECh. 22.4 - Prob. 1ECh. 22.4 - Prob. 2ECh. 22.4 - Prob. 3ECh. 22.4 - Prob. 4ECh. 22.4 - Prob. 5ECh. 22.5 - Prob. 1ECh. 22.5 - Prob. 2ECh. 22.5 - Prob. 3ECh. 22.5 - Prob. 4ECh. 22.5 - Prob. 5ECh. 22.5 - Prob. 6ECh. 22.5 - Prob. 7ECh. 22 - Prob. 1PCh. 22 - Prob. 2PCh. 22 - Prob. 3PCh. 22 - Prob. 4P
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Similar questions
- A hash-map has been constructed with quadratic-hashing. The hashing function is h(k;) = (3 * k; + 7) mod 17 and the table length is N = 17. How many cells will be probed by the call of insert(2) 1 4 5 6 7 8 10 11 12 13 14 15 16 26 89 11 35 70 59 Note: First row contains the indices and the second row contains the items.arrow_forwardIn this case, we have a hash map that was not constructed with a lot of care since all of the values have been lumped together (that is, they are all in the same LinkedList). In your own words, please elaborate on why this undermines the use of a hash map.arrow_forwardFor now, we're making do with a rudimentary hash map in which all the values are stored in a single bucket (that is, they are all in the same LinkedList). Once a hash map's goal is met, it is considered successful.arrow_forward
- We have a poorly-designed hash map where all of the stored values are in one bucket (meaning they’re all in one LinkedList). Explain why this undermines the advantages of a hash map.arrow_forwardIs there a set limit on the number of linked lists that may be included in a hash table of size m? I've been trying to get my head around the purpose of a hash function, but I just can't seem to get it. Provide an example that helps to demonstrate how your theory is correct.arrow_forwardDoes a hash table of size m always have the same number of linked lists? No matter how long I try, I just can't put my finger on the function of a hash function. Give a case study to demonstrate your point.arrow_forward
- The values have been put into a single pool, and a hash map that was not constructed with much care has resulted (that is, they are all in the same LinkedList). Give a justification for why this is counterproductive to using a hash map.arrow_forwardImplement a commonly used hash table in a program that handles collision using linear probing. Using (K mod 13) as the hash function, store the following elements in the table: {1, 5, 21, 26, 39, 14, 15, 16, 17, 18, 19, 20, 111, 145, 146}.arrow_forwardWe have a poorly designed hash map in which all values have been placed in the same bucket (that is, they are all in the same LinkedList). Explain how this goes against the purpose of utilizing a hash map in the first place.arrow_forward
- We use Linked Lists to store all of our hash map data due to the sloppy manner in which the map was originally constructed. This substantially reduces the efficacy of a hash map.arrow_forwardWe have a badly built hash map in which all of the stored values are in the same bucket (that is, they are all in the same LinkedList). Explain why this negates the benefits of a hash map.arrow_forwardHowever, our hash map is badly built, thus all values end up in the same slot (that is, they are all in the same LinkedList). Provide an explanation as to why doing so would undermine the goal of using a hash map.arrow_forward
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