'Average case running time' presentation slideshows

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CS 3343: Analysis of Algorithms

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Order Statistics

Order Statistics. David Kauchak cs302 Spring 2012. Medians. The median of a set of numbers is the number such that half of the numbers are larger and half smaller How might we calculate the median of a set? Sort the numbers, then pick the n/2 element. A = [50, 12, 1, 97, 30]. Medians.

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Chapter 5

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CS 332: Algorithms

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UMass Lowell Computer Science 91.404 Analysis of Algorithms Prof. Karen Daniels Fall, 2004

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Outline

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15-211 Fundamental Structures of Computer Science

15-211 Fundamental Structures of Computer Science. April 29, 2003. Announcements. HW6 due on Thursday at 11:59pm Final exam review on Sunday 4:30-6:00pm in WeH 7500 Othello tournament on May 7 4:30-6:00pm in WeH 7500 Final exam on May 8 8:30-11:30am in UC McConomy

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UMass Lowell Computer Science 91.404 Analysis of Algorithms Prof. Karen Daniels Spring, 2001

UMass Lowell Computer Science 91.404 Analysis of Algorithms Prof. Karen Daniels Spring, 2001. Final Review Mon. 5/14-Wed. 5/16. Overview of Next 2 Lectures. Review of some key course material Final Exam: Course Grade Logistics, Coverage, Format Handout for basis of 40% of test

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i206: Lecture 7: Analysis of Algorithms, continued

i206: Lecture 7: Analysis of Algorithms, continued. Marti Hearst Spring 2012. Algorithms. A clearly defined procedure for solving a problem. A clearly described procedure is made up of simple clearly defined steps. The procedure can be complex, but the steps must be simple and unambiguous.

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Algorithms and Data Structures

Algorithms and Data Structures. Lecture 1. Agenda:. Course Overview Goals of the Course Syllabus Notation Basics of Algorithms Algorithms Evaluation. Course Overview:. Lectures, 32 hours, 16 weeks Practical classes, 32 hours, 16 weeks Grading consists of home works and examination

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Foundations of Software Design Fall 2002 Marti Hearst

Foundations of Software Design Fall 2002 Marti Hearst. Lecture 11: Analysis of Algorithms, cont. Function Pecking Order. In increasing order. Where does n log n fit in?. Adapted from Goodrich & Tamassia. Plot them!. Both x and y linear scales. Convert y axis to log scale.

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i206: Lecture 8: Sorting and Analysis of Algorithms

i206: Lecture 8: Sorting and Analysis of Algorithms. Marti Hearst Spring 2012. Definition of Big-Oh A running time is O (g( n) ) if there exist constants n 0 > 0 and c > 0 such that for all problem sizes n > n 0 , the running time for a problem of size n is at most c( g (n)) .

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Sorting & Searching

Sorting & Searching. 27 10 12 20. divide. 27 10. 12 20. divide. divide. 10. 12. 20. 27. merge. merge. 10 27. 12 20. merge. 10 12 20 27. Mergesort. Merge two sorted arrays. first array. second array. result of merge. 10 27. 12 20. 10. 10 27.

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