graph-algorithm

问题 I have a planar graph embedded on a plane (plane graph ) and want to search its faces. The graph is not connected but consists of several connected graphs, which are not separately adressable (e.g. a subgraph can be contained in the face of another graph) I want to find the polygons (faces) which include a certain 2d point. The polygons are formed by the faces of the graphs. As the number of faces is quite big I would like to avoid to determine them beforehand. What is the general complexity

问题 This is an interview question I came across: You are given a the map of airplanes between the cities in a country, basically a directed graph was given with weights as the cost of planes between the cities. You were also given a single coupon to get 50% on any single flight. Find the minimum cost with which you can travel between two cities? 回答1: The base algorithm for this problem is Dijkstra's algorithm for shortest paths. However, we need to find a way to include the coupon. We can do this

问题 I was trying shortest path finder using dijkstra algorithm but It seems not working. Can't figure out what the problem is. Here are the code and the error message. (I'm working on Python 3.5. https://www.youtube.com/watch?v=LHCVNtxb4ss) graph = { 'A': {'B': 10, 'D': 4, 'F': 10}, 'B': {'E': 5, 'J': 10, 'I': 17}, 'C': {'A': 4, 'D': 10, 'E': 16}, 'D': {'F': 12, 'G': 21}, 'E': {'G': 4}, 'F': {'E': 3}, 'G': {'J': 3}, 'H': {'G': 3, 'J': 3}, 'I': {}, 'J': {'I': 8}, } def dijkstra(graph, start, end):

问题 I was trying shortest path finder using dijkstra algorithm but It seems not working. Can't figure out what the problem is. Here are the code and the error message. (I'm working on Python 3.5. https://www.youtube.com/watch?v=LHCVNtxb4ss) graph = { 'A': {'B': 10, 'D': 4, 'F': 10}, 'B': {'E': 5, 'J': 10, 'I': 17}, 'C': {'A': 4, 'D': 10, 'E': 16}, 'D': {'F': 12, 'G': 21}, 'E': {'G': 4}, 'F': {'E': 3}, 'G': {'J': 3}, 'H': {'G': 3, 'J': 3}, 'I': {}, 'J': {'I': 8}, } def dijkstra(graph, start, end):

问题 I'm trying to make a small public transport routing application. My data is represented in a following structure: graph = {'A': {'B':3, 'C':5}, 'B': {'C':2, 'D':2}, 'C': {'D':1}, 'D': {'C':3}, 'E': {'F':8}, 'F': {'C':2}} Where: graph dict key is a node subdict key is an edge between 2 nodes subdict value is an edge weight I was using find_shortest_path algorithm described here https://www.python.org/doc/essays/graphs/ but it is rather slow because of recursion and has no support of weights.

问题 The traditional* implementation of Dijkstra's does not handle this case well. I think I've come up with some solutions that will work, but they are not particularly elegant**. Is this is a known problem with a standard solution? This is assuming the non-trivial solution i.e. a path like A->B->C->A rather than just A->A. * When I say traditional I mean marking each node as visited. ** Storing the number of times each node has been visited and basing the terminating conditions on whether the

问题 I was wondering if there is an algorithm which: given a fully connected graph of n-nodes (with different weights)... will give me the cheapest cycle to go from node A (a start node) to all other nodes, and return to node A? Is there a way to alter an algorithm like Primm's to accomplish this? Thanks for your help EDIT: I forgot to mention I'm dealing with a undirected graph so the in-degree = out-degree for each vertex. 回答1: Can you not modify Dijkstra, to find you the shortest path to all

问题 I was wondering if there is an algorithm which: given a fully connected graph of n-nodes (with different weights)... will give me the cheapest cycle to go from node A (a start node) to all other nodes, and return to node A? Is there a way to alter an algorithm like Primm's to accomplish this? Thanks for your help EDIT: I forgot to mention I'm dealing with a undirected graph so the in-degree = out-degree for each vertex. 回答1: Can you not modify Dijkstra, to find you the shortest path to all

问题 I was wondering if there is an algorithm which: given a fully connected graph of n-nodes (with different weights)... will give me the cheapest cycle to go from node A (a start node) to all other nodes, and return to node A? Is there a way to alter an algorithm like Primm's to accomplish this? Thanks for your help EDIT: I forgot to mention I'm dealing with a undirected graph so the in-degree = out-degree for each vertex. 回答1: Can you not modify Dijkstra, to find you the shortest path to all

问题 I asked this question on reddit, but haven't converged on a solution yet. Since many of my searches bring me to Stack Overflow, I decided I would give this a try. Here is a simple formulation of my problem: Given a weighted undirected graph G(V,E,w) and a subset of vertices S in G, find the min/max weight tree that spans S. Adding vertices is not allowed. An extension of the basic model is adding edges with 0 weight, and vertices that must be excluded. This seems similar to the question asked