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3.4. Glossary¶

Algorithm: A generic, step-by-step list of instructions for solving a problem.

Average Case: Refers to when an algorithm performs between its worst and best case given a certain data set or circumstance.

Best Case: Refers to when an algorithm performs especially good given a certain data set or circumstance.

Big-O Notation: Another term for order of magnitude; written as $O (f (n))$ .

Brute Force: Technique that tries to exhaust all possibilities of a problem.

Contiguous: Adjacent or next to.

Dynamic Size: Able to change size automatically.

Exponential: Function represented as a number being raised to a power that increases like $f (n) = 2^{n}$ .

Linear: Function that grows in a one to one relationship with its input like $f (n) = n$ .

Logarithmic: Functions that are the inverse of exponential functions usually presented as $f (n) = l o g n$ .

Order of Magnitude: Function describing the part $T (n)$ that increases the fastest as the value of n increases (a function describing an algorithm’s steps as the size of the problem increases).

Quadratic: Function describing a relationship who’s highest order is a number squared:

Simplified: $f (n) = x^{2}$

Complex: $a x^{2} + b x + c$

Worst Case: Refers to when an algorithm performs especially poorly given a certain data set or circumstance.