Categories
Technical
-- Database/SQL
-- Multimedia
-- Internet/Networking
-- Operating System
-- Programming
-- Security/Hacking
-- Science/Engineering
-- Web/HTML/CSS/Ajax
-- Unix/Linux
-- Windows
-- Mac OS X
-- Office
-- Perl/PHP/Python
-- C/C++/C#
-- .NET
-- Java
-- Hardware
-- Game Development
-- Embedded Systems
-- Graphics and Design
-- Network Programming
Study
Novel
Nonfiction
Health
Tutorial
Entertainment
Business
Magazine
Arts & Design
Audiobooks & Video Training
Cultures & Languages
Family & Home
Law & Politics
Lyrics & Music
Software Related
eBook Torrents
Uncategorized
-- Database/SQL
-- Multimedia
-- Internet/Networking
-- Operating System
-- Programming
-- Security/Hacking
-- Science/Engineering
-- Web/HTML/CSS/Ajax
-- Unix/Linux
-- Windows
-- Mac OS X
-- Office
-- Perl/PHP/Python
-- C/C++/C#
-- .NET
-- Java
-- Hardware
-- Game Development
-- Embedded Systems
-- Graphics and Design
-- Network Programming
Study
Novel
Nonfiction
Health
Tutorial
Entertainment
Business
Magazine
Arts & Design
Audiobooks & Video Training
Cultures & Languages
Family & Home
Law & Politics
Lyrics & Music
Software Related
eBook Torrents
Uncategorized
Share With Friends
Archive by Date
2012-08-06
2012-08-05
2012-08-04
2012-08-03
2012-08-02
2012-08-01
2012-07-31
2012-07-30
2012-07-29
2012-07-28
2012-08-05
2012-08-04
2012-08-03
2012-08-02
2012-08-01
2012-07-31
2012-07-30
2012-07-29
2012-07-28
Search Tag
Rudel
汤姆斯威夫特
Luxo
epic
Configurator
UNITEXT
Abandonment
Polskie
Laver
Jeanson
Continuation
Loyalism
Sufficiency
Pixie
Rodrigues
Ahern
Bauen
Comet
Aivazovsky
情报的艺术
乌托邦
Organe
Allen
Qt6开发及实例
Kosten
Afghan
President
PAEDIATRICS
Toxicology
Suspense
Reframing
毛泽东
customize
Allyn
Qt实战
Cards
茶馆之殇
Christians
Mansfield
Peaces
Comparative
Complexities
DVD5
Ramadan
Inaccessible
Pecan
difficult
Nesb
Rental
Rhode
Newest
Learning Core Audio: A Hands-On Guide to Audio Programming for Mac and iOS
Introduction to Programming with Fortran: with coverage of Fortran 90, 95, 2003 and 77
Introduction to Programming with Fortran - with coverage of Fortran 90, 95, 2003 and 77
MATLAB - Modelling, Programming and Simulations
-MATLAB: Modelling, Programming and Simulations- ed. by Emilson Pereira Leite (Repost)
Introduction to Programming with Fortran: with coverage of Fortran 90, 95, 2003 and 77 [Repost]
An Introduction to Programming and Numerical Methods in MATLAB [Repost]
"MATLAB: Modelling, Programming and Simulations" ed. by Emilson Pereira Leite (Repost)
Programming Social Applications: Building Viral Experiences with OpenSocial, OAuth, OpenID, and Distributed Web... (repost)
MATLAB Programming for Engineers (2nd edition) [Repost]
Sriranga Veeraraghavan, "Sams Teach Yourself Shell Programming in 24 Hours" (Repost)
Programming Computer Vision with Python - Tools and algorithms for analyzing images
Matlab: A Practical Introduction to Programming and Problem Solving (2nd edition) [Repost]
ECOOP 2011 - Object-Oriented Programming
Mobile JavaScript Application Development - Bringing Web Programming to Mobile Devices [Paperback]
Concurrent Programming on Windows (repost)
Expert WSS 3.0 and MOSS 2007 Programming (repost)
Embedded Software Design and Programming of Multiprocessor System-on-Chip (repost)
OpenCV 2 Computer Vision Application Programming Cookbook
Programming Computer Vision with Python: Tools and algorithms for analyzing images
Introduction to Programming with Fortran: with coverage of Fortran 90, 95, 2003 and 77
Introduction to Programming with Fortran - with coverage of Fortran 90, 95, 2003 and 77
MATLAB - Modelling, Programming and Simulations
-MATLAB: Modelling, Programming and Simulations- ed. by Emilson Pereira Leite (Repost)
Introduction to Programming with Fortran: with coverage of Fortran 90, 95, 2003 and 77 [Repost]
An Introduction to Programming and Numerical Methods in MATLAB [Repost]
"MATLAB: Modelling, Programming and Simulations" ed. by Emilson Pereira Leite (Repost)
Programming Social Applications: Building Viral Experiences with OpenSocial, OAuth, OpenID, and Distributed Web... (repost)
MATLAB Programming for Engineers (2nd edition) [Repost]
Sriranga Veeraraghavan, "Sams Teach Yourself Shell Programming in 24 Hours" (Repost)
Programming Computer Vision with Python - Tools and algorithms for analyzing images
Matlab: A Practical Introduction to Programming and Problem Solving (2nd edition) [Repost]
ECOOP 2011 - Object-Oriented Programming
Mobile JavaScript Application Development - Bringing Web Programming to Mobile Devices [Paperback]
Concurrent Programming on Windows (repost)
Expert WSS 3.0 and MOSS 2007 Programming (repost)
Embedded Software Design and Programming of Multiprocessor System-on-Chip (repost)
OpenCV 2 Computer Vision Application Programming Cookbook
Programming Computer Vision with Python: Tools and algorithms for analyzing images
Useful Links
Programming Fundamentals of the Average Case Analysis of Particular Algorithms (Wiley-Teubner Series in Computer Science): Rainer Kemp
Posted on 2010-03-16
|
More Fundamentals of the Average Case Analysis of Particular Algorithms (Wiley-Teubner Series in Computer Science): Rainer Kemp 'Analysis of algorithms' is quite important in computer programming, because there are generally several algorithms available for a particular application and we would like to measure and compare the time and storage requirements. Time may be measured by counting steps, statements, or the number of times some given operation is performed; space may be measured by bits, words, or the number of registers and cells required during execution of the algorithm. The analysis usually consists of determining the behaviour of an algorithm in the best case, in the worst case and in the average case. The best (worst) case is characterized by the minimum (maximum) total amount of time or space requirements taken over all inputs of some fixed size. To characterize the average case, it is necessary to define what we mean by the average; in general, we must make some assumptions about the expected characteristics of the inputs of some fixed size. If an input x of size n has the probability px and requires the total amount of time or space kx, then the average case is characterized by the behaviour of the expected value (Appendix A) of the random variable which has th^ value kx with probability px. In most problems we will make the reasonable assumption that each of the inputs of size n is equally likely, but the analysis can also be carried out under other assumptions. To obtain a quantitative indication of how close to the average we may expect the amount of time or storage requirements to be, we will compute further characteristics of the given distribution such as the variance, the standard deviation, the moments about the origin, or the (cumulative) distribution function (Appendix A). Now an important problem is to compare the time and space requirements of algorithms available for a particular application. Sometimes we want to decide which is best. But this is easier said than done. In many cases we may only compare the time requirements or the storage requirements of two algorithms, because the one algorithm requires less time but more space than the other. Similarly, comparing two algorithms in the best, worst, or average case, the same situation can occur. For example, the sorting algorithm 'Heapsort' is faster than the algorithm 'Quicksort' in the worst case, but not in the average case. Summing up, a comparison of two algorithms should be made only for the time or storage requirements in the best or worst or average case. (Nevertheless, there are other criteria of goodness of algorithms such as the product of time and space requirements or the adaptability to computers.) The classical complexity theory deals with the time and storage requirements of algorithms in the worst case. In practice, there are some objections to the measuring of the goodness of an algorithm by these quantities, although their computation can be an extremely difficult task. If an algorithm requires time or space of order O(f(n)) in the worst case, then the constant in the 0-term can be fantastically large and the result is only of theoretical interest. Furthermore, if the inputs corresponding to the worst case have a probability which tends to zero for large input sizes, then it is hard to see why the goodness of the algorithm is measured by its worst case. Therefore, the importance of the worst case can be reduced by the knowledge of its probability. But the computation of this probability can be rather difficult, unless impossible. In practice, an algorithm requiring time n on the average in 99 per cent of all possible inputs of size n should be preferred to an algorithm for the same problem which needs time n2 in the worst and average case, even though the former algorithm needs time n3 in the worst case. Study of the behaviour of an algorithm on the average is accompanied by many mathematical calculations; we need to use the results of complex variable theory, number theory, probability theory, discrete mathenutics and combina- combinatorics. The principal techniques involved in the analysis of igorithms consist of counting of certain objects, solving of recurrences, working with finite summations, handling of generating functions and asymptotic evaluating of expressions. The last part of this introductory section is devoted to some simple examples elucidating the above ideas and concepts. To see my other books, click Download Link (Here).
Rating:
2.5 out of 5 by Book123 |
Download Links | |
Server | Status |
---|---|
Direct Download Link 1 | Alive |
Direct Download Link 2 | Alive |
Download Link ([email protected]) | Alive |
Download Link ([email protected]) | Alive |
Buy This Book at Best Price >> |
Like this article?! Give us +1: