2015年2月16日 · The closest an algorithm can get to this would be something like a backdoor in an encryption algorithm, where the planned flaw is part of the information relationship established by the algorithm. And lastly, a program, as it is short for a computer program, tautologically requires a computer. An algorithm does not.

2020年12月23日 · For large n, we usually define the time it takes to multiply two n-bit numbers as M(n). That time depends on how clever our algorithm is; what we learned in school has M(n) = O (n^2), the Karatsuba algorithm has M(n) ≈ O (n^1.58), and FFT-based algorithms run …

2022年10月11日 · When executing the procedure, the computer will do whatever the code specifies. For instance, a procedure may specify sorting an array of items using quicksort. So in computing, a procedure is code; an algorithm is not. A procedure may implement an algorithm; it cannot be an algorithm. Algorithms are often described using pieces of code; either ...

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For instance, all polynomial algorithms have runtime in $\cal{O}(2^n)$; therefore, such a bound might not characterise the algorithm well at all. In most cases, only worst-case instances are considered. Often, this is not very representative for the real behaviour of the algorithm. Prominent examples include Quicksort and Simplex algorithm.

An algorithm is a sequence of well-defined steps that defines an abstract solution to a problem. Use this tag when your issue is related to design and analysis of algorithms. Learn more…

An algorithm is, alas, an informal concept, which can not have a precise mathematical definition. One can still attempt at an informal definition, and argue that it conveys the right (informal) intuition.

2020年7月4日 · Dijkstra shortest path algorithm relies heavily on algebraic properties of the way you compare/combine paths (we call it the algebra in this case). Network routing algorithms often use veresions of Dijkstra'a algorithm which are based on a different algebra, and their correctness is guaranteed by the algebraic properties of the compare/combine ...

The fastest algorithm I can think of is applying LCS in a creative way. It can solve this problem in O(N^2) time and O(N^2) space where N is the size of string. LCS (S, reverse (S)) will give you the largest palindromic subsequence, as the largest palindromic subsequence will be the largest common subsequence between the string S and its reverse.

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