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UNT / Education and Teacher Studies / TEA 4110 / What is Fibonacci Sequence?

What is Fibonacci Sequence?

What is Fibonacci Sequence?

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School: University of North Texas
Department: Education and Teacher Studies
Course: Algorithms
Professor: Dr. farhad
Term: Spring 2017
Tags:
Cost: Free
Name: CSCE 4110-Algorithms Week 1 Notes
Description: These notes cover the first week of information covered in this course. It includes some important review information
Uploaded: 01/23/2017
2 Pages 91 Views 8 Unlocks
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How many recursions is this?




What makes up a good algorithm?




What is an algorithm?



Week 1 Tuesday,  January 17, 2017 2:22 PMCourse website: http://cse.unt.edu/~4110s001/ What is an algorithm? • A set of finite instructions What makes up a good algorithm? • Running time is a polynomial of the input. (exponential vs. polynomial) • Fibonacci Sequence (Bad Algorithm) Code: fibo(N) ○ If (N=0) or (N=1) then fibo <-- N Else fibo <-- fibo(N-1) + fibo(N) Bad functiDon't forget about the age old question of georgia tech linear algebra 1554
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Don't forget about the age old question of What is meant by Margin of Error?
on because (2N ○ ) How many recursions is this? ○ ▪ Depicted as a tree. ▪ Left branch has a depth of N ▪ Right branch has a depth of N/2 For a complete tree 2N/2  ▪ recursions Computers Grew Out of Multiple Disciplines • Math - Numerical Analysis • Biology - Trees and Hierarchies • Psychology - Clique (subgraphs/graph theory) • Linguistics - compilers (Chomsky's hierarchy) Recurrence Notation: T(n) Sorting Insertion Sort ϴ(n2) Bubble Sort  ϴ(n2) T(n) = i=n∑1i  = i=1∑n • i = n(n+1)/2 • The above formula is known as the Arithmetic Sum. Merge Sort ϴ(nlogn) • Power of divide and conquer • T(n) = 2t(n/2) + n, n>1, T(1) = 1 Assume : n = 2k ○ ○ T(n) = 2[2T(n/2) +n/2] + n         = 22 ○ T(n/4) + n + n         = 22 ○ T(n/4) +  2n   =3 • T(n) = 2t(n/2) + n, n>1, T(1) = 1 Assume : n = 2k ○ ○ T(n) = 2[2T(n/2) +n/2] + n         = 22 ○ T(n/4) + n + n         = 22 ○ T(n/4) +  2n           = 23 ○ T(n/8) +  3n         = 2kT(n/2k ○ ) + kn         = 2k ○ + kn ○         = n + kn ○         = n + nlogn ○         = ϴ(nlogn) • Ideally at the end of merge sort you would use Insertion Sort to fully use the  Divide and Conquer Paradigm. • Why would someone not just split merge sort in 3 rather than 2, to speed up  sort time? ○ Solution to the recurrence relation found on practice  sheet (problem #6) Division by 2 is a simple shift and uses less merges while division by 3 is no  ○ longer trivial programmatically and uses more merges.  Prim's Algorithm • ϴ(mlogn) given a priority queue data structure ϴ(n2 • ) given a array data structure • Minimal Spanning Tree Solution Kruskal's Algorithm • Minimal Spanning Tree  solution Elementary Algorithms • Depth First Sort • Topological Sort The first assignment will be assigned next week.

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