Assume you have a 2D matrix. Values are sorted in ascending order from left to write and top to bottom, e.g., 1 7 16 21 34 4 9 20 25 41 6. 16 31 33 44 11 19 49 50 61 I'm curious if my value x is in this matrix. I could take a brute force approach, considering all cells via the following code: def find in matrix(m, num rows, num cols, x): for į in range(num rows): for j in range(num cols): if m[i]li)==x: return True return False What would be the big O estimate of the run-time for this brute force approach? How could I take a divide-and-conquer approach? Briefly describe (in words) how you could use what is known about the structure of the 2D matrix to divide and conquer.
Assume you have a 2D matrix. Values are sorted in ascending order from left to write and top to bottom, e.g., 1 7 16 21 34 4 9 20 25 41 6. 16 31 33 44 11 19 49 50 61 I'm curious if my value x is in this matrix. I could take a brute force approach, considering all cells via the following code: def find in matrix(m, num rows, num cols, x): for į in range(num rows): for j in range(num cols): if m[i]li)==x: return True return False What would be the big O estimate of the run-time for this brute force approach? How could I take a divide-and-conquer approach? Briefly describe (in words) how you could use what is known about the structure of the 2D matrix to divide and conquer.
Database System Concepts
7th Edition
ISBN:9780078022159
Author:Abraham Silberschatz Professor, Henry F. Korth, S. Sudarshan
Publisher:Abraham Silberschatz Professor, Henry F. Korth, S. Sudarshan
Chapter1: Introduction
Section: Chapter Questions
Problem 1PE
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![Assume you have a 2D matrix. Values are sorted in ascending order from left to write and top to bottom,
e.g.,
1
7
16
21
34
4
9
20
25
41
16
31
33
44
11
19
49
50
61
I'm curious if my value x is in this matrix.
I could take a brute force approach, considering all cells via the following code:
def find in matrix(m, num rows, num cols, x):
for į in range(num rows):
for j in range(num cols):
if m[i]i)==x:
return True
return False
What would be the big O estimate of the run-time for this brute force approach?
How could I take a divide-and-conquer approach? Briefly describe (in words) how you could use what is
known about the structure of the 2D matrix to divide and conquer.](/v2/_next/image?url=https%3A%2F%2Fcontent.bartleby.com%2Fqna-images%2Fquestion%2F6a936ea8-df89-45c2-b756-f5ec8064b5e2%2F732a7800-7249-4b5a-ba8b-e334fff69a00%2Frfaw62s_processed.png&w=3840&q=75)
Transcribed Image Text:Assume you have a 2D matrix. Values are sorted in ascending order from left to write and top to bottom,
e.g.,
1
7
16
21
34
4
9
20
25
41
16
31
33
44
11
19
49
50
61
I'm curious if my value x is in this matrix.
I could take a brute force approach, considering all cells via the following code:
def find in matrix(m, num rows, num cols, x):
for į in range(num rows):
for j in range(num cols):
if m[i]i)==x:
return True
return False
What would be the big O estimate of the run-time for this brute force approach?
How could I take a divide-and-conquer approach? Briefly describe (in words) how you could use what is
known about the structure of the 2D matrix to divide and conquer.
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