Algorithm  -  A computable set of steps to achieve a desired result. An algorithm is set of steps for completing some task. For example, one could develop an algorithm for making a peanut butter and jelly sandwich: 1) Walk to the cupboard and get bread; 2) Take out two pieces of bread from the package; 3) Get a knife and peanut butter and jelly; 4) Using the knife, put jelly onto one piece of bread; etc. Algorithms are often implemented in computer science in the form of functions.

Metric  -  A metric is a method for measuring something. Time in seconds might be a metric used to measure the speed of a runner. Length in feet might be a metric used to measure the strength of a javelin thrower. Abstract time is the metric used to measure complexity of an algorithm.

Platform Dependency  -  Different computers (or platforms) can yield widely varying results when running the same implementation of an algorithm. Computers can have different amounts of memory, different chip designs and clock speeds, different operating systems, etc., all factors which contribute to the time it takes to run an algorithm.

Implementation Dependent  -  Different implementations of the same algorithm can yield widely varying results in terms of how well they work and run. Timing results depend on the language used to write the algorithm, the compiler used to compile it, what data structures and methods the programmer used in coding the algorithm, etc. This is one reason real running time should not be used to measure the efficiency of an algorithm; there are too many variables.

Efficiency  -  The efficiency of an algorithm is the amount of resources used to find an answer. It is usually measured in terms of the abstract computations, such as comparisons or data moves, the memory used, the number of messages passed, the number of disk accesses, etc.

Big-O Notation  -  Big-O notation is a theoretical measure of the execution of an algorithm, usually the time or memory needed, given the problem size n, which is usually the number of items in the input. Informally, saying some equation f (n) = O(g(n)) means it is less than some constant multiple of g(n). More formally, it means there are positive constants c and k, such that 0 < = f (n) < = c*g(n) for all n > = k. The values of c and k must be fixed for the function f and must not depend on n.

Algorithm Cost  -  In computer science, the cost of an algorithm, or how much computing power and time it takes to run, is a central concern.

Abstract Time  -  Abstract time is a count of the number of abstract steps performed in the execution of an algorithm, or a count of the significant operations performed, such as comparisons, multiplications, copies, etc.

Real Running Time  -  The real running time of an algorithm is the time it takes in some real unit of measurement, such as seconds, for the algorithm to run.

Asymptotic Analysis  -  When determining how complex an algorithm is, or how efficiently it runs, we only care about it in abstract terms when the input size gets fairly large. As such, if we have an equation to measure the efficiency of an algorithm, we only really care about the biggest terms in the equation. In other words, we only care about the term with the largest order of magnitude. If we reduce the equation to the biggest term, we end up with a function representing the "asymptotic bound" of the equation. This kind of analysis is known as asymptotic analysis.

Constant Time  -  In terms of abstract time, constant times means a constant number of operations.