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Pictured above are graphs representing algorithmic complexity of Big Oh notation functions. Assign the following labels to the appropriate graph. There will be two labels per graph. O(n) is done as an example. Dark Blue - Light Blue - Yellow Purple Green Brown Gray Big Oh notation O(n) Algorithmic growth linear

Pictured above are graphs representing algorithmic complexity of Big Oh notation functions. Assign the following labels to the appropriate graph. There will be two labels per graph. O(n) is done as an example. Dark Blue - Light Blue - Yellow Purple Green Brown Gray Big Oh notation O(n) Algorithmic growth linear

Computer Networking: A Top-Down Approach (7th Edition)
Computer Networking: A Top-Down Approach (7th Edition)
7th Edition
ISBN:9780133594140
Author:James Kurose, Keith Ross
Publisher:James Kurose, Keith Ross
Chapter1: Computer Networks And The Internet
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The image displays a graph illustrating the growth of different functions with respect to input size, commonly used in analyzing algorithm complexity in computer science. ### Key Components of the Graph: - **Axes**: - The horizontal axis represents the input size, labeled as "Input (number)." - The vertical axis represents time complexity or computation time, labeled as "Time." - **Curves**: The graph contains several curves, each representing a different complexity class: - **Red Curve**: Typically represents constant time complexity, O(1), where execution time is unaffected by input size. - **Green Curve**: Often used for logarithmic time complexity, O(log n), which grows slowly as input size increases. - **Yellow Curve**: Depicts linear time complexity, O(n), where time grows linearly with input size. - **Blue Curve**: Demonstrates quadratic time complexity, O(n²), showing a parabolic growth pattern. - **Violet Curve**: Sometimes used for exponential time complexity, O(2^n), indicating rapid growth as input size increases. - **Annotations**: - The graph includes a label "O(n)" near the violet curve, indicating a linear complexity line for reference. These complexity classes are fundamental in understanding how algorithms scale and perform with larger inputs. Assessing the growth of these curves helps in evaluating the efficiency and feasibility of algorithms in practical applications.
Transcribed Image Text:The image displays a graph illustrating the growth of different functions with respect to input size, commonly used in analyzing algorithm complexity in computer science. ### Key Components of the Graph: - **Axes**: - The horizontal axis represents the input size, labeled as "Input (number)." - The vertical axis represents time complexity or computation time, labeled as "Time." - **Curves**: The graph contains several curves, each representing a different complexity class: - **Red Curve**: Typically represents constant time complexity, O(1), where execution time is unaffected by input size. - **Green Curve**: Often used for logarithmic time complexity, O(log n), which grows slowly as input size increases. - **Yellow Curve**: Depicts linear time complexity, O(n), where time grows linearly with input size. - **Blue Curve**: Demonstrates quadratic time complexity, O(n²), showing a parabolic growth pattern. - **Violet Curve**: Sometimes used for exponential time complexity, O(2^n), indicating rapid growth as input size increases. - **Annotations**: - The graph includes a label "O(n)" near the violet curve, indicating a linear complexity line for reference. These complexity classes are fundamental in understanding how algorithms scale and perform with larger inputs. Assessing the growth of these curves helps in evaluating the efficiency and feasibility of algorithms in practical applications.
Pictured above are graphs representing algorithmic complexity of Big Oh notation functions. Assign the following labels to the appropriate graph. There will be two labels per graph. O(n) is done as an example. | Big Oh notation | Algorithmic growth | |-----------------|---------------------| | Dark Blue | O(n) | linear | | Light Blue | | | | Yellow | | | | Purple | | | | Green | | | | Brown | | | | Gray | | |
Transcribed Image Text:Pictured above are graphs representing algorithmic complexity of Big Oh notation functions. Assign the following labels to the appropriate graph. There will be two labels per graph. O(n) is done as an example. | Big Oh notation | Algorithmic growth | |-----------------|---------------------| | Dark Blue | O(n) | linear | | Light Blue | | | | Yellow | | | | Purple | | | | Green | | | | Brown | | | | Gray | | |
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