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Algorithms & Data Structures David Vernon Copyright ©2007 David Vernon (www.vernon.eu) Course Content Introduction to Complexity of Algorithms • – Performance of algorithms – Time and space tradeoff – Worst case and average case performance – The big O notation – Example calculations of complexity Complexity and Intractability • – NP Completeness and Approximation Algorithms Copyright ©2007 David Vernon (www.vernon.eu) Course Content Simple Searching Algorithms • – Linear Search – Binary Search Simple Sorting Algorithms • – Bubblesort – Quicksort Copyright ©2007 David Vernon (www.vernon.eu) Course Content Abstract Data Types (ADTs) • Lists, Stacks, and Queues • – ADT specification – Array implementation – Linked-list implementation Copyright ©2007 David Vernon (www.vernon.eu) Course Content Trees • – Binary Trees – Binary Search Trees – Traversals – Applications of Trees » Huffman Coding – Height-balanced Trees » AVL Trees » Red-Black Trees Copyright ©2007 David Vernon (www.vernon.eu) Introduction to Analysis of Algorithms & Complexity Theory Copyright ©2007 David Vernon (www.vernon.eu) Complexity Analysis of complexity of programs • – Time complexity – Space complexity – Big-Oh Notation Introduction to complexity theory • – P, NP, and NP-Complete classes of algorithm Copyright ©2007 David Vernon (www.vernon.eu) Complexity Suppose there is an assignment • statement x := x +1 in your program. We’d like to determine: • – The time a single execution would take – The number of times it is executed FFrreeqquueennccyy CCoouunntt Copyright ©2007 David Vernon (www.vernon.eu) Complexity Product of time and frequency is the • total time taken Frequency count will vary from data set • to data set Copyright ©2007 David Vernon (www.vernon.eu) Complexity Since the execution time will be very • machine dependent (and compiler dependent), we neglect it and concentrate on the frequency count Consider the following three examples: • Copyright ©2007 David Vernon (www.vernon.eu)

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Algorithms & Data Structures. David Vernon case performance. – The big O notation Big-Oh Notation .. + c. 1 n + c. 0 where the c i are constants, c k are non- zero, and n is a parameter .. exhibit the same properties as the monkey

Algorithms & Data Structures - David Vernon PDF

617 Pages·2007·0.75 MB·English

by David Vernon

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Algorithms & Data Structures. David Vernon case performance. – The big O notation Big-Oh Notation .. + c. 1 n + c. 0 where the c i are constants, c k are non- zero, and n is a parameter .. exhibit the same properties as the monkey

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Author:	David Vernon
Publication Year:	2007
Pages:	617
Language:	English
File Size:	0.75
Format:	PDF
Price:	FREE

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