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<MIT's Introduction to Algorithms, Lecture 15: Dynamic Programming ⁄ >




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This is the tenth post in an article series about MIT's lecture course "Introduction to Algorithms." In this post I will review lecture fifteen, which introduces the concept of Dynamic Programming and applies it to the Longest Common Subsequence problem.

Dynamic programming is a design technique similar to divide and conquer. Divide-and-conquer algorithms partition the problem into independent subproblems, solve the subproblems recursively, and then combine their solutions to solve the original problem. Dynamic programming is applicable when the subproblems are not independent, that is, when subproblems share subsubproblems. A dynamic-programming algorithm solves every subsubproblem just once and then saves its answer in a table, thereby avoiding the work of recomputing the answer every time the subsubproblem is encountered.

Dynamic programming was systematized by Richard E. Bellman. He began the systematic study of dynamic programming in 1955. The word "programming," both here and in linear programming, refers to the use of a tabular solution method and not to writing computer code.



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this is just a small excerpt from the article, to access the full article please click in the link below:

http://www.catonmat.net/blog/mit-introduction-to-algorithms-part-ten/




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