'Linear time algorithm' presentation slideshows

PH.D candidate: Lan Liu

Some Algorithmic Problems Concerning the Inference and Analysis of TagSNPs, Haplotypes and Pedigrees. PH.D candidate: Lan Liu. Advisor: Tao Jiang. Outline. The haplotype inference problem The tagSNP selection problem The minimum common integer partition problem. Outline.

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My Favorite Ten Complexity Theorems of the Past Decade II

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Sum Selection in Arrays

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Schema Refinement and Normal Forms

Schema Refinement and Normal Forms. Chapter 19. Instructor: Mirsad Hadzikadic. The Evils of Redundancy. Redundancy is at the root of several problems associated with relational schemas: redundant storage, insert/delete/update anomalies

633 views • 42 slides

Going after the -SAT Threshold

Going after the -SAT Threshold. Konstantinos Panagiotou ( with Amin Coja-Oghlan ). -SAT Formulas. Given: boolean variables a Boolean formula in - conjunctive normal form ( -CNF) where is a variable or the negation of a variable

453 views • 37 slides

Finding Dominators in Flowgraphs Linear-Time Algorithm 1 and Experimental Study 2

Finding Dominators in Flowgraphs Linear-Time Algorithm 1 and Experimental Study 2 Loukas Georgiadis 1 joint work with Robert E. Tarjan 2 joint work with Renato F. Werneck , Robert E. Tarjan , Spyridon Triantafyllis and David I. August.

578 views • 36 slides

Algorithms, Programs, and Computers

Algorithms, Programs, and Computers. CS 110 Fall 2005. Problem Solving. You want to make money and you have some simpletons who are willing to help Devise a plan that will make money (write a business plan for starting a restaurant) Tell the simpletons to execute the business plan.

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A fast algorithm for the generalized k-keyword proximity problem given keyword offsets

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A Non-Local Cost Aggregation Method for Stereo Matching Yang, QingXiong ( ??? )

A Non-Local Cost Aggregation Method for Stereo Matching Yang, QingXiong ( ??? ) City University of Hong Kong. 3. 6. 9. 2. 5. 8. 1. 4. 7. =>. a planar graph. A 2D image ( 3x3 ). 3. 6. 9. 2. 5. 8. 1. 4. 7. Computing minimum spanning tree (MST). 6. 3. 5. 9. 1. 2. 4.

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The Substitution method

The Substitution method. T(n) = 2T(n/2) + cn Guess : T(n) = O(n log n) Proof by Mathematical Induction : Prove that T(n) ? d n log n for d>0 T(n) ? 2(d ? n/2 ? log n/2) + cn (where T(n/2) ? d ? n/2 (log n/2) by induction hypothesis) ? dn log n/2 + cn

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CSE 5311 Advanced Algorithms

CSE 5311 Advanced Algorithms. Instructor: Dr. Gautam Das Week 3 Class Notes Submitted By: Abhishek Hemrajani & Pinky Dewani. CSE 5311 Advanced Algorithms. Topics Covered in Week 3: Median Finding Algorithm (Description and Analysis) Binary Search Tree. CSE 5311 Advanced Algorithms.

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CSE 4101/5101

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Knuth-Morris-Pratt Algorithm

Knuth-Morris-Pratt Algorithm. Prepared by: Mayank Agarwal Nitesh Maan. The problem of String Matching.

687 views • 20 slides

Knuth-Morris-Pratt Algorithm

Knuth-Morris-Pratt Algorithm. Prepared by: Mayank Agarwal Nitesh Maan. The problem of String Matching.

2.12k views • 20 slides

Knuth-Morris-Pratt Algorithm

Knuth-Morris-Pratt Algorithm. Prepared by: Mayank Agarwal Nitesh Maan. The problem of String Matching.

516 views • 20 slides

Lower bound for sorting, radix sort

COMP171 Fall 2005. Lower bound for sorting, radix sort. Lower Bound for Sorting. Mergesort and heapsort worst-case running time is O(N log N) Are there better algorithms?

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Dynamic Programming

Dynamic Programming. Nithya Tarek. Dynamic Programming. Dynamic programming solves problems by combining the solutions to sub problems. Paradigms: Divide and conquer Greedy Algorithm Dynamic programming. String Matching. Longest common subsequence Knuth-Morris-Pratt pattern matching.

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Theoretical Aspects of Normalization

Theoretical Aspects of Normalization. Functional Dependencies (FDs). A functional dependency X Y holds over relation R if, for every allowable instance r of R: t1 r, t2 r, ( t1 ) = ( t2 ) implies ( t1 ) = ( t2 )

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Lecture 2

Lecture 2. We have given O(n 3 ), O(n 2 ), O(nlogn) algorithms for the max sub-range problem. This time, a linear time algorithm!

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Construction of Index: (Page 197)

Construction of Index: (Page 197). Objective: Given a document, find the number of occurrences of each word in the document. Example: Computer Science students know computers and computer languages. Keywords: computer, computers, science, students, know, and, languages.

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