Big Oh notation does not mention constant value

I am a programmer and have just started reading Algorithms. I am not completely convinced with the notations namely Bog Oh, Big Omega and Big Theta. The reason is by definition of Big Oh, it states that there should be a function g(x) such that it is always greater than or equal to f(x). Or f(x) <= c.n for all values of n >n0.

Does it matter the direction of a Huffman’s tree child node?

So, I’m on my quest about creating a Java implementation of Huffman’s algorithm for compressing/decompressing files (as you might know, ever since Why create a Huffman tree per character instead of a Node?) for a school assignment.

need explanation on amortization in algorithm

I am a learning algorithm analysis and came across a analysis tool for understanding the running time of an algorithm with widely varying performance which is called as amortization.

How to discriminate from two nodes with identical frequencies in a Huffman’s tree?

Still on my quest to compress/decompress files with a Java implementation of Huffman’s coding (http://en.wikipedia.org/wiki/Huffman_coding) for a school assignment.

Help with algorithmic complexity in custom merge sort implementation

I’ve got an implementation of the merge sort in C++ using a custom doubly linked list. I’m coming up with a big O complexity of n^2, based on the merge_sort() > slice operation. But, from what I’ve read, this algorithm should be n*log(n), where the log has a base of two.

Sublinear Extra Space MergeSort

I am reviewing basic algorithms from a book called Algorithms by Robert Sedgewick, and I came across a problem in MergeSort that I am, sad to say, having difficulty solving. The problem is below:

Finding the time complexity of the following program that uses recursion

I need to find the time complexity in terms of Big Oh notation for the following program which computes the factorial of a given number: The program goes like this:

How much times command executed? Looking for mistake

I have following piece of code:

How to compute amoritized cost for a dynamic array?

I am trying to understand how to do the amortized cost for a dynamic table. Suppose we are using the accounting method.

A* Algorithm Completeness Proof

The A* Algorithm is the optimal (provided the heuristic function is underestimated), complete & admissible (provided some conditions). I know the proofs of admissibility & optimality.