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, 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?), 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,

Unit 6 Algorithms

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