bi g oh notati on classification of algorithms
play

BI G OH NOTATI ON Classification of Algorithms The running time of - PDF document

BI G OH NOTATI ON Classification of Algorithms The running time of most algorithms is proportional to one of the following functions : instructions run only once constant solve a big problem by log N transforming it into a smaller problem


  1. BI G OH NOTATI ON

  2. Classification of Algorithms The running time of most algorithms is proportional to one of the following functions : instructions run only once constant solve a big problem by log N transforming it into a smaller problem each input element is N processed solve a problem by breaking it into a N log N number of smaller problems, solve them independently, and combine the solutions process all pairs of data items N 2 brute-force 2 N S. Prasitjutrakul 1994

  3. Polynomial vs. Exponential size n f(n) 10 20 30 40 50 n .00001 .00002 .00003 .00004 .00005 sec sec sec sec sec 2 n .0001 .0004 .0009 .0016 .0025 sec sec sec sec sec 3 n .001 .008 .027 .064 .125 5 sec sec sec sec sec n n .1 3.2 24.3 1.7 5.2 sec sec sec min min n 8 S. Prasitjutrakul 1994

  4. Polynomial vs. Exponential Time present 100 times 1000 times complexity computer faster faster n T 100 T 1000 T 1 1 1 2 n T 10 T 31.6 T 2 2 2 3 n T 4.64 T 10 T 3 3 3 5 n T 2.5 T 3.98 T 4 4 4 n 2 T T + 6.64 T + 9.97 5 5 5 n 3 T T + 4.19 T + 6.29 6 6 6 S. Prasitjutrakul 1994

  5. Asymptotics ! The study of functions of a parameter n , as n becomes larger and larger without bound. ! Frequency of basic actions is much more important than a total counts of all operations including housekeeping. – Houskeeping is too dependent on - programming language - programmer's particular style ! Change in fundamental method can make a vital difference (e.g. sequential vs. binary search ). S. Prasitjutrakul 1994

  6. Big Oh Notation จํ านวนการเปรียบเทียบเฉลี่ย Algorithms กรณีที่หาพบ กรณีที่หาไมพบ Sequential 0.5(n+1) n O( n ) Binary1 log n + 1 log n + 1 O( log n ) 2 2 Binary2 2log n 2log n - 3 O( log n ) 2 2 S. Prasitjutrakul 1994

  7. Big Oh Notation Definition If f(n) and g(n) are functions defined for positive integers, then f(n) is O( g(n) ) means than there exists a constant c such that f(n) ? ≤ c ? g(n) ? for all sufficiently large positive integers n Example : 2n + 4n - 6 → O( n ) 7n - 4n + 1 → O( n ) 3 3 2 + 4n - 7 → O( 2 ) 2 3 3 n n S. Prasitjutrakul 1994

  8. Growth Rates of Common Functions n 2 n 3 n 2 S. Prasitjutrakul 1994

Download Presentation
Download Policy: The content available on the website is offered to you 'AS IS' for your personal information and use only. It cannot be commercialized, licensed, or distributed on other websites without prior consent from the author. To download a presentation, simply click this link. If you encounter any difficulties during the download process, it's possible that the publisher has removed the file from their server.

Recommend


More recommend