depth-first-search

I'm using BGL to store my DAG. Vertices have states. Given a change in state in one of the vertices i want to update dependent vertices. This i'm able to do using boost::depth_first_search and a custom visitor. Now the logic is that i dont want to update a searched vertex and its dependent if the vertex is in a particular state. Basically i want to control over en-queuing of vertices in either dfs or bfs. What is the best way to achieve this in BGL. Thanks. It seems that boost::depth_first_search does not support this, but the underlying boost::depth_first_visit does, through its 2nd overload

I have a android application called Islands and bridges also known as Hashiwokakero The application uses A 2 Dimensional array that spawns the Islands randomly everytime the user restarts the game It form a Matrix with number from 0 to 4 where 0=null and 1-4 = Island There can be 2 bridges comming out of one Island to connect the other , The map at the moment is not solvable. To solve the game the user needs to connect the islands using bridges so if an island = 4 it needs 4 connection to it if an island = 2 it needs 2 connection and so on.. in my research i found out that the best algorithm

I want to make a GUI using Java in which a user can select a bean, edit its fields, and then add an instance of the created bean to a queue. My question though is about accessing the fields. I have a class MyCompositeObject that inherits from MyParentObject. The MyParentObject is composed of multiple beans, each being composed of more beans. The class MyCompositeObject is also composed of beans. I want to find all accessible fields from MyCompositeObject. Class MyParentObject { MyObjectOne fieldOne; MyObjectTwo fieldTwo; String name; ... } Class MyCompositeObject extends MyParentObject {

Thanks to everyone replying with ideas and alternate solutions. More efficient ways of solving problems are always welcome, as well as reminders to question my assumptions. That said, I'd like you to ignore for a moment what problem I'm trying to solve with the algorithm, and just help me analyze the big-Oh complexity of my algorithm as I've written it -- an all simple paths in a graph using depth-limited search as described here , and implemented here . Thanks! Edit: This is homework, but I've already submitted this assignment and I'd just like to know if my answer correct. I get a little

I know we can use DFS for maze exploration. But I think we can also use BFS for maze exploration. I'm little bit confused here because most of the books and articles that I've read had used DFS for this problem. What I think is that the Best Case time complexity of DFS will be better as compared to BFS. But Average and Worst Case time complexity will be same for both BFS & DFS and thats why we prefer DFS over BFS. Am I right or I'm having some misconception They have similar running time , but either may greatly outperform the other on any given problem simply due to the order in which the

I want to divide a graph with N weighted-vertices and N-1 edges into three parts such that the maximum of the sum of weights of all the vertices in each of the parts is minimized. This is the actual problem i am trying to solve, http://www.iarcs.org.in/inoi/contests/jan2006/Advanced-1.php I considered the following method /*Edges are stored in an array E, and also in an adjacency matrix for depth first search. Every edge in E has two attributes a and b which are the nodes of the edge*/ min-max = infinity for i -> 0 to length(E): for j -> i+1 to length(E): /*Call depth first search on the nodes