I am to show that log(n!) = Θ(n·log(n)). A hint was given that I should show the upper bound with n开发者_高级运维n and show the lower bound with (n/2)(n/2).This does not seem all that intuitive to
Below is a binary search function. int search(int a[], int v, int left, int right) { while (right >= left)
i want to try to calculate the O(n) of my program (in python). there are two problems: 1: i have a very basic knowledge of O(n) [aka: i know O(n) has to do with time 开发者_Python百科and calculation
I am having some trouble determining space and time comp开发者_如何学运维lexities. For example, if I have a tree that has a branching factor b and will have at most a depth d, how can I calculate the
How would Big-O notation help in my day-to-day C# programming?Is it just an academic exercis开发者_如何学编程e?Big-O tells you the complexity of an algorithm in terms of the size of its inputs.This is
According to the definition of big O f(n) <= C*g(n)(which means f(n) = O(g(n)), it could be deduced that:
How large a system is it reasonable to attempt to do a linear regression on? Specifically: I have a system with ~300K sample points a开发者_JS百科nd ~1200 linear terms. Is this computationally feasib
Is there someplace where I can get a Big-O style analysis / comparison of traditional data structures such as linked lists, various 开发者_StackOverflow中文版trees, hashes, etc vs. cache aware data st
I am learning algorithm analysis. I am having trouble understanding the difference between O, Ω, and Θ.
I have a data such that there are many parents each with 0-n children where each child can have 0-n nodes.Each node has a unique identifier (key) Ultimately, the parents are not connected to each othe
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