Wiki User
∙ 11y agoYou can write any algorithm in any way you like. Many prefer pseudocode or flowcharts, others use prose or more formalized methods.
For example, if you wanted to describe an algorithm to count the number of occurrences of a given item I in a given list L, I would propose the following pseudocode:
let counter be 0.
let the current item C be the first item in list L.
while C == valid {
if C matches I then
increment counter set C to the next item in the list
}
return counter.
Wiki User
∙ 11y agoMerge sort (or mergesort) is an algorithm. Algorithms do not have running times since running times are determined by the algorithm's performance/complexity, the programming language used to implement the algorithm and the hardware the implementation is executed upon. When we speak of algorithm running times we are actually referring to the algorithm's performance/complexity, which is typically notated using Big O notation. Mergesort has a worst, best and average case performance of O(n log n). The natural variant which exploits already-sorted runs has a best case performance of O(n). The worst case space complexity is O(n) auxiliary.
#include#includesize_t count_char (const std::string& s, const char c){size_t count = 0;size_t offset = 0;while ((offset = s.find(c, offset)) != s.npos){++count;++offset;}return count;}int main(){std::string str {"Hello world!"};std::cout
n-1 times
There are lots of factors to consider. Some important ones are what are the best, worst, and average times it will take for the sorting method to complete given a certain amount of elements to sort. Also important is how much memory the algorithm will use, what he distribution of the data it is working on is, and whether you want the algorithm to ensure that if stopped part way though sorting that the data is not in a less sorted state than when it started.
Assuming you're talking about comparison-based sorting algorithms, the number of passes is the number of comparisons that the algorithm makes internally while sorting. In a programming language, this would be the total number of times the loop executes. This number is defined by the computational complexity (Big-O notation), which defines an upper bound.
The frequency of the event over that time period.
An example of finiteness in algorithm is when a loop within the algorithm has a predetermined number of iterations, meaning it will only run a specific number of times before completing. This ensures that the algorithm will eventually terminate and not run indefinitely.
frequency
2,890,789,456
The number 7 occurs once. The digit 7 occurs 20 times.
It is its frequency.
24 times 21= in algorithm standard
the answer is frequency. the answer is frequency.
the square root of the given number
There is no simple answer to this question.
exponent
If you are given the area you will have to think what do you times with the number you have to get it.