breadth-first-search

The results below are measured using perf on a compute server with 32 cores. I know my implementation is unoptimized but purposely as I want to make comparisons. I understand that graph algorithms tend to have low locality which researchers try to address. I'm unclear of the results, though. The time elapsed is misleading. My implementation runs through a graph with about 4mm nodes in about 10 seconds and the rest of the time pre processing. The optimized version uses the same input and traverses about 10 times with each less than a second each so it's really just pre-processing time. I'm not

First of all this is an assingment and I am not looking for direct answers but instead the complexity of the best solution as you might be thinking it . This is the known problem of shortest path between 2 points in a matrix (Start and End) while having obstacles in the way. Moves acceptables is up,down,left and right . Lets say when moving i carry sth and the cost of each movement is 2 . There are points in the matrix (lets name them B points) where I can leave this sth in one B point and pick it up from another B point . Cost of dumping sth in B point is 1 and cost of picking sth up from a B

Convert a number m to n with minimum operations. The operations allowed were subtraction by 1 and multiplication by 2. For Eg : 4 and 6. Answer is 2. 1st operation : -1 -> 4-1 = 3. 2nd operation : * -> 3 * 2 =6. I am using BFS approach for a particular input (src=26, dst= 5) it is taking long time. Am I doing something wrong? from queue_class import queue class node: def __init__(self, value, level, parent): self.level = level self.value = value self.parent = parent def get_minimum_distance(src, target, q): if src == target: return 0 seen_list = [] data = node(src, 0, -1) q.enqueue(data) while

I have read about DFS and BFS many times but I have this doubt lingering my mind since long. In a lot of articles it is mentioned that DFS can get stuck in infinite loops. As far as I know, this limitation can easily be removed by keeping track of the visited nodes. In fact, in all the books that I have read, this little check is a part of DFS. So why are 'infinite loops' mentioned as a disadvantage of DFS? Is it just because the original DFS algorithm did not have this check for visited nodes? Please explain. (1) In graph search algorithms [used frequently on AI], DFS's main advantage is

I just taught myself some OpenMP and this could be stupid. Basically I'm trying to parallelize a breadth first search program in c++, with each node taking a long time to process. Here's an example code: queue<node*> q; q.push(head); while (!q.empty()) { qSize = q.size(); for (int i = 0; i < qSize; i++) { node* currNode = q.front(); q.pop(); doStuff(currNode); q.push(currNode); } } The processing function doStuff() is quite expensive and I want to parallelize it. However if I parallelize the for loop by putting #pragma omp parallel for right before the for line, all kinds of weird error pop up

I'm trying to get breadth first enumeration working with Gremlin, however I'm having trouble finding a way to output all the steps observed during the enumeration. I can only print out the result of the last iteration. My question would be, given a starting node like this, how can I follow all paths (without knowing the overall depth) using Gremlin and print out everything I find along the way? study=g.v('myId') I have tried the scatter approach, looping approach (although both seem to require knowledge about the actual length of the path beforehand if I understand correctly) Many thanks! You