Answer:
ooh I have a I phone computer
Answer:
O(N!), O(2N), O(N2), O(N), O(logN)
Explanation:
N! grows faster than any exponential functions, leave alone polynomials and logarithm. so O( N! ) would be slowest.
2^N would be bigger than N². Any exponential functions are slower than polynomial. So O( 2^N ) is next slowest.
Rest of them should be easier.
N² is slower than N and N is slower than logN as you can check in a graphing calculator.
NOTE: It is just nitpick but big-Oh is not necessary about speed / running time ( many programmers treat it like that anyway ) but rather how the time taken for an algorithm increase as the size of the input increases. Subtle difference.
Hmm... I feel like this query is much broader than it should be. However, I will start my initial answer, then another potential solution.
My initial answer to your query was: A condition controlled loop is used to control the number of times a loop iterates.
The potential answer, my secondary one, is: A count controlled loop iterates a specific number of times.
Two results, but my initial answer is the solution I opted when understanding this.
Answer:
Means no matter how many processors you use, speed up never increase from 10 times.
Explanation:
If a problem of size W has a serial component Ws,then performance using parallelism:
Using Amdahl's Law:
Tp = (W - Ws )/ N + Ws
Here, Ws = .1,
W - Ws = .9
Performance Tp = (.9 / N) + .1
---------------------------------------------------------
Speed Up = 1 / ( (.9 / N) + .1)
If N -> infinity, Speed Up <= 10
Means no matter how many processors you use, speed up never increase from 10 times.
Answer:
Computer ethics is a field of applied ethics that addresses ethical issues in the use, design and management of information technology and in the formulation of ethical policies for its regulation in society.
