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A class of algorithms we likely won't work with but are fun to explore are those with exponential runtimes.
Correction
At approximately 4:38 in the video, instead of reading 2E20 as "two raised to the 20th power", the statement from the instructor should have been "two followed by 20 zeroes".
Glossary
Polynomial runtimes - O(n ^ k): An algorithm is considered to have a polynomial runtime if, for a given value of n
, it's worst case runtime is in the form of n raise to the k power. Examples of this are O(n^2)
and O(n^3)
. Algorithms with polynomial runtimes are considered efficient.
Exponential runtime - O(k^n): A runtime where number of operations increases exponentially as the size of the data set increases. Exponential runtimes are considered inefficient.
Combinatorial or factorial runtime - O(n!) - Runtimes where as the size of n
increases the number of operations increases by n!
where n!
is the factorial of n
.
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