'Increasing subsequence' presentation slideshows

Dynamic-Programming Strategies for Analyzing Biomolecular Sequences

Dynamic-Programming Strategies for Analyzing Biomolecular Sequences. Dynamic Programming. Dynamic programming is a class of solution methods for solving sequential decision problems with a compositional cost structure. Richard Bellman was one of the principal founders of this approach.

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Multiple Sequence Alignments

Multiple Sequence Alignments. Progressive Alignment. x. When evolutionary tree is known: Align closest first, in the order of the tree In each step, align two sequences x, y, or profiles p x , p y , to generate a new alignment with associated profile p result Weighted version:

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Instructor: Shengyu Zhang

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Rapid Global Alignments

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Longest Increasing Subsequence

Longest Increasing Subsequence. By Adri Wessels. IOI Training Camp 3 (9-10 March 2019). The Problem:. Given a sequence (e.g. 10, 2, 6, 13, 4, 5) find a subsequence (e.g. 2, 13, 4) such that the subsequence is the longest (strictly) increasing subsequence. The Solution:. The solution uses DP.

360 views • 32 slides

A Note on Useful Algorithmic Strategies

A Note on Useful Algorithmic Strategies. Kun-Mao Chao ( ??? ) Department of Computer Science and Information Engineering National Taiwan University, Taiwan WWW: http://www.csie.ntu.edu.tw/~kmchao. Greedy Algorithm. A greedy method always makes a locally optimal (greedy) choice.

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Real-world protein aligners

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A Note on Useful Algorithmic Strategies

A Note on Useful Algorithmic Strategies. Kun-Mao Chao ( ??? ) Department of Computer Science and Information Engineering National Taiwan University, Taiwan WWW: http://www.csie.ntu.edu.tw/~kmchao. Greedy Algorithm. A greedy method always makes a locally optimal (greedy) choice.

367 views • 29 slides

A Note on Useful Algorithmic Strategies

A Note on Useful Algorithmic Strategies. Kun-Mao Chao ( ??? ) Department of Computer Science and Information Engineering National Taiwan University, Taiwan WWW: http://www.csie.ntu.edu.tw/~kmchao. Greedy Algorithm. A greedy method always makes a locally optimal (greedy) choice.

374 views • 29 slides

Dynamic Programming

Dynamic Programming. Kun-Mao Chao ( ??? ) Department of Computer Science and Information Engineering National Taiwan University, Taiwan E-mail: kmchao@csie.ntu.edu.tw WWW: http://www.csie.ntu.edu.tw/~kmchao. Dynamic Programming.

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Xiaoming Sun Tsinghua University David Woodruff MIT

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Longest Common Subsequence

Longest Common Subsequence. Definition: The longest common subsequence or LCS of two strings S1 and S2 is the longest subsequence common between two strings. S1 : A -- A T -- G G C C -- A T A n=10

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CSE 417: Algorithms and Computational Complexity

CSE 417: Algorithms and Computational Complexity. Winter 2001 Lecture 7 Instructor: Paul Beame TA: Gidon Shavit. Three Steps to Dynamic Programming. Formulate the answer as a recurrence relation or recursive algorithm

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The Pigeonhole Principle

The Pigeonhole Principle. Rosen 4.2. Pigeonhole Principle. If k+1 or more objects are placed into k boxes, then there is at least one box containing two or more objects. Generalized Pigeonhole Principle.

362 views • 12 slides