# 21.5: Glossary


analysis of algorithms:
A way to compare algorithms in terms of their run time and/or space requirements.
machine model:
A simplified representation of a computer used to describe algorithms.
worst case:
The input that makes a given algorithm run slowest (or require the most space).
In a polynomial, the term with the highest exponent.
crossover point:
The problem size where two algorithms require the same run time or space.
order of growth:
A set of functions that all grow in a way considered equivalent for purposes of analysis of algorithms. For example, all functions that grow linearly belong to the same order of growth.
Big-Oh notation:
Notation for representing an order of growth; for example, $$O(n)$$ represents the set of functions that grow linearly.
linear:
An algorithm whose run time is proportional to problem size, at least for large problem sizes.
An algorithm whose run time is proportional to $$n^2$$, where $$n$$ is a measure of problem size.