Question

I wrote the following code in python to solve
problem 15 from Project Euler:

grid_size = 2
def get_paths(node):
        global paths

        if  node[0]  >= grid_size and node[1] >= grid_size:
                paths += 1
                return
        else:
                if node[0]<grid_size+1 and node[1] < grid_size+1:
                     get_paths((node[0]+1,node[1]))
                     get_paths((node[0],node[1]+1))
        return paths

def euler():
                print get_paths((0,0))

paths = 0
if __name__ == '__main__':
    euler()

Although it runs quite well for a 2 X 2 grid, it’s been running for hours for a 20 X 20 grid. How can I optimise the code so that it can run on larger grids?
Is it a kind of breadth first search problem? (It seems so to me.)

How can I measure the complexity of my solution in its current form?

Answer 1

Your algorithm is exponential, but only because you are re-evaluating get_paths with the same input many times. Adding Memoization to it will make it run in time. Also, you’ll need to get rid of the global variable, and use return values instead. See also Dynamic Programming for a similar idea.