Big O notation allocate array of N element
In Big O notation, allocate an array of N element is defined by O(1) or O(n) ?
For example in C#, if I allocate an array like this :
Handling the process of large-scale lists [closed]
Closed 9 years ago.
What would be the best sort to use on a something with sorted “chunks”?
Imagine a sorted deck of N cards that has been cut K times by moving X cards from the top to the bottom (X is a different / random amount each time, X is always < N) – what would be the best way to sort this, knowing that there are “chunks” of sorted cards?
What would be the best sort to use on a something with sorted “chunks”?
Imagine a sorted deck of N cards that has been cut K times by moving X cards from the top to the bottom (X is a different / random amount each time, X is always < N) – what would be the best way to sort this, knowing that there are “chunks” of sorted cards?
Algorithm to merge two sorted arrays with minimum number of comparisons
Given are two sorted arrays a, b of type T with size n and m. I am looking for an algorithm that merges the two arrays into a new array (of maximum size n+m).
Algorithm to merge two sorted arrays with minimum number of comparisons
Given are two sorted arrays a, b of type T with size n and m. I am looking for an algorithm that merges the two arrays into a new array (of maximum size n+m).
Big-O notation for other cases
I was just reading answers to a question Plain English explanation of Big O
From that i came to know that Big-O notation is just an “upper bound” of the complexity of an algorithm?
Big-O notation for other cases
I was just reading answers to a question Plain English explanation of Big O
From that i came to know that Big-O notation is just an “upper bound” of the complexity of an algorithm?
Big-O notation for other cases
I was just reading answers to a question Plain English explanation of Big O
From that i came to know that Big-O notation is just an “upper bound” of the complexity of an algorithm?
Big-O notation for other cases
I was just reading answers to a question Plain English explanation of Big O
From that i came to know that Big-O notation is just an “upper bound” of the complexity of an algorithm?