# 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.

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 $$f(n) = logn$$.

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: $$ax^{2} + bx + c$$

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