I am trying to get a grasp on Big O notations.It seems pretty abstract.I selected the most common data structures - array, hash, linkedl list (single and double) and a binary search tree and guessed s
Big O Notation Arrays vs. Linked List insertions: According to academic literature for arrays it is constant O(1) and for Linked Lists it is linear O(n).
I\'m confused on how to create a function T(n) to measure computing time for a nested infinite loop.Here is the code:
It seems like the best complexity would be linear O(n). Doesn\'t matter the case really, I\'m speaking of greedy algorithms in general.
Interview Question: Propose a data structure that holds elements from 0 to n − 1 and supports all of the following operations in O(1) time: initialization, insertion of an element, deletion
How can I prove the following: 10 n log n ∈ O(2n2) n log n + 40 · 2n - 6n ∈ O(2n) In the first one, I\'m using this math:
I am trying to find complexity of Fibonacci series using a recursion tree and concluded height of tree = O(n) worst case, cost of each level = cn, hence complexity = n*n=n^2
I\'ve been tinkering around with code (Basic, Python, C++, PHP, JavaScript) on and off for almost two decades, but have only recently begun to开发者_StackOverflow get more \"serious\" about it (using
Say you had this DTO class: public class MyObj{ private int id; private String displayName; private String backendData;
If I have some algorithm that runs at best n time and at worst n^2 time, is it fair to say that the algorithm is Big Omega (n)?
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