21 lines
1.4 KiB
Markdown
21 lines
1.4 KiB
Markdown
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---
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title: Asymptotic Notation
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---
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## Asymptotic Notation
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How do we measure the performance value of algorithms?
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Consider how time is one of our most valuable resources. In computing, we can measure performance with the amount of time a process takes to complete. If the data processed by two algorithms is the same, we can decide on the best implementation to solve a problem.
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We do this by defining the mathematical limits of an algorithm. These are the big-O, big-omega, and big-theta, or the asymptotic notations of an algorithm. On a graph the big-O would be the longest an algorithm could take for any given data set, or the "upper bound". Big-omega is like the opposite of big-O, the "lower bound". That's where the algorithm reaches its top-speed for any data set. Big theta is either the exact performance value of the algorithm, or a useful range between narrow upper and lower bounds.
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Some examples:
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- "The delivery will be there within your lifetime." (big-O, upper-bound)
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- "I can pay you at least one dollar." (big-omega, lower bound)
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- "The high today will be 25ºC and the low will be 19ºC." (big-theta, narrow)
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- "It's a kilometer walk to the beach." (big-theta, exact)
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#### More Information:
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<!-- Please add any articles you think might be helpful to read before writing the article -->
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- <a href='https://learnxinyminutes.com/docs/asymptotic-notation/' target='_blank' rel='nofollow'>Asymptotic Notation</a>
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