'Closest pair' presentation slideshows

CSCE 411 Design and Analysis of Algorithms

CSCE 411 Design and Analysis of Algorithms. Set 3: Divide and Conquer Prof. Jennifer Welch Fall 2014. General Idea of Divide & Conquer. Take your problem and divide it up into smaller pieces Solve one or more of the smaller problems Combine solutions to subproblems.

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Chapter 2

Chapter 2. Recursive Algorithms. Chapter Outline. Analyzing recursive algorithms Recurrence relations Closest pair algorithms Convex hull algorithms Generating permutations Recursion and stacks. Prerequisites. Before beginning this chapter, you should be able to:

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CSCE 411H Design and Analysis of Algorithms

CSCE 411H Design and Analysis of Algorithms. Set 3: Divide and Conquer Prof. Evdokia Nikolova* Spring 2013. * Slides adapted from Prof. Jennifer Welch. General Idea of Divide & Conquer. Take your problem and divide it up into smaller pieces Solve one or more of the smaller problems

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CS38 Introduction to Algorithms

CS38 Introduction to Algorithms. Lecture 8 April 24, 2014. Outline. Divide and Conquer design paradigm closest pair (finishing up) the DFT and the FFT polynomial multiplication polynomial division with remainder integer multiplication matrix multiplication. Closest pair in the plane.

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Convex hull

smallest convex set containing all the points. Convex hull. smallest convex set containing all the points. Convex hull. smallest convex set containing all the points. Convex hull. 3. 2. start = 1. 1.next = 2 = 3.prev 2.next = 3 = 4.prev 3.next = 4 = 1.prev 4.next = 1 = 2.prev.

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Lecture 5

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Lecture 4: Divide and Conquer III: Other Applications and Examples

Lecture 4: Divide and Conquer III: Other Applications and Examples. Shang-Hua Teng. Integer Multiplication. When do we need to multiply two very large numbers? In Cryptography and Network Security message as numbers encryption and decryption need to multiply numbers.

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Divide-and-Conquer

Divide-and-Conquer. The most-well known algorithm design strategy: Divide instance of problem into two or more smaller instances Solve smaller instances recursively Obtain solution to original (larger) instance by combining these solutions. Divide-and-Conquer Technique (cont.).

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Lecture 31

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Recurrences /

Recurrences / . Divide&Conquer. HW: 2.4 Quiz: 2.1, 4.1, 4.2, 5.2, 7.3, 7.4 Midterm: 8. given a recursive algorithm, state the recurrence solve a recurrence, using Master theorem given a recurrence and its solution, prove that the solution is correct

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Lecture 36

Lecture 36. Selection (rank) problems Closest pair 19.2.4-19.2.5. Rank. Rank of an element is its position in ascending key order. [2,6,7,8,10,15,18,20,25,30,35,40] rank(2) = 0 rank(15) = 5 rank(20) = 7. Selection Problem.

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Prof. Swarat Chaudhuri

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Rank

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Finding Correlations in Subquadratic Time

Finding Correlations in Subquadratic Time. Gregory Valiant. n. Q1: Find correlated columns ( sub-quadratic time?). Liu L et al. PNAS 2003;100:13167-13172. State of the Art: Correlations/Closest Pair. Q1: Given n random boolean vectors, except with

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Abdullah Mueen Eamonn Keogh University of California, Riverside

Online Discovery and Maintenance of Time Series Motifs. Abdullah Mueen Eamonn Keogh University of California, Riverside. Time Series Motifs. Repeated pattern in a time series Utility: Activity/Event discovery Summarization Classification Application

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Chapter 3: Brute Force

The Design and Analysis of Algorithms. Chapter 3: Brute Force. Chapter 3. Brute Force Algorithms. Basic Idea Brute Force Search and Sort Brute Force String Matching Closest Pair and Convex Hull Problems Exhaustive Search Conclusion. Basic Idea.

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Richard Swinbank 9 th July 2004 Bulk Loading the M-tree to Enhance Query Performance

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Closest Pair I

Divide and Conquer. Closest Pair I. Design and Analysis of Algorithms I. The Closest Pair Problem. Initial Observations. High-Level Approach. The Divide and Conquer Paradigm. ClosestPair ( ).

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Closest Pair

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