A general description of a task that can (or cannot) be solved with an algorithm.
A finite set of instructions that accomplish a task.
Putting steps in an order.
Deciding which steps to do next.
Doing some steps over and over.
A measure of how many steps are needed to complete an algorithm.
A search algorithm which checks each element of a list, in order, until the desired value is found or all elements in the list have been checked.
A search algorithm that starts at the middle of a sorted set of numbers and removes half of the data; this process repeats until the desired value is found or all elements have been eliminated.
Algorithms with a polynomial efficiency or lower (constant, linear, square, cube, etc.) are said to run in a reasonable amount of time.
Algorithms with exponential or factorial efficiencies are examples of algorithms that run in an unreasonable amount of time.
Provides a "good enough" solution to a problem when an actual solution is impractical or impossible.
A problem with a yes/no answer (e.g., is there a path from A to B?).
A problem with the goal of finding the "best" solution among many (e.g., what is the shortest path from A to B?).
A problem for which no algorithm can be constructed that is always capable of providing a correct yes-or-no answer.
A model in which programs run in order, one command at a time.
A model in which programs are broken into small pieces, some of which are run simultaneously.
A model in which programs are run by multiple devices.
The time used to complete a task sequentially divided by the time to complete a task in parallel.