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Finding algorithm complexity when using inner-most loop condition. (Dynamic Programming)
I am working on a dynamic programming problem where the inner most loop condition is checking if an element is in the open list, and then checking if it is also in the closed list.
All these are toggled until the open list is empty or the open list is completely open.
In order to find the time complexity of this algorithm, it would seem that you have to use the number of positions that you are flipping, but I am unclear if the loop condition that is within the number of positions or how the number of positions is calculated.
Does anyone have any advice?
It seems like the loop isn’t reducing the size of the open list. So you can do it in O(N) time, because an iteration has at worst O(1) time.
If you look at the complexity of the problem, when you talk about the number of iterations being the complexity, that’s the number of times you decrease the size of the open list, so you have O(n*(1-n)) iterations at worst.
The number of positions would correspond to the number of times you decrease the size of the open list.
Protein-Protein and Nuclear Pore Complexes interact with bacterial DNA
Jan 9, 2015
by Arpana Kulkarni
Proteins have been implicated in several cellular processes through an interaction with nucleic acids. However, the protein-protein interactions with nucleic acids are less understood than the protein-DNA interactions. Here