Mrs Smith CSP Fall 20 - Unit 6-Algorithms

Problem

a general description of a task that can (or cannot) be solved with an algorithm

Algorithm

a finite set of instructions that accomplish a task.

Efficiency

a measure of how many steps are needed to complete an algorithm

Linear Search

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.

Binary Search

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.

Reasonable Time

Algorithms with a polynomial efficiency or lower (constant, linear, square, cube, etc.) are said to run in a reasonable amount of time.

Unreasonable Time

Algorithms with exponential or factorial efficiencies are examples of algorithms that run in an unreasonable amount of time.

Heuristic

provides a "good enough" solution to a problem when an actual solution is impractical or impossible

Undecidable Problem

a problem for which no algorithm can be constructed that is always capable of providing a correct yes-or-no answer

Sequential Computing

a model in which programs run in order, one command at a time.

Parallel Computing

a model in which programs are broken into small pieces, some of which are run simultaneously

Distributed Computing

a model in which programs are run by multiple devices

Speedup

the time used to complete a task sequentially divided by the time to complete a task in parallel

Decision Problem

a problem with a yes/no answer (e.g., is there a path from A to B?)

Optimization Problem

a problem with the goal of finding the "best" solution among many (e.g., what is the shortest path from A to B?)

Sequencing: the application of each step of an algorithm in the order in which the code statements are given