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  • evaluating a code of a graph [migrated]

    - by mazen.r.f
    This is relatively a long code,if you have the tolerance and the will to find out how to make this code work then take a look please, i will appreciate your feed back. i have spent two days trying to come up with a code to represent a graph , then calculate the shortest path using dijkastra algorithm , but i am not able to get the right result , even the code runs without errors , but the result is not correct , always i am getting 0. briefly,i have three classes , Vertex, Edge, Graph , the Vertex class represents the nodes in the graph and it has id and carried ( which carry the weight of the links connected to it while using dijkastra algorithm ) and a vector of the ids belong to other nodes the path will go through before arriving to the node itself , this vector is named previous_nodes. the Edge class represents the edges in the graph it has two vertices ( one in each side ) and a wight ( the distance between the two vertices ). the Graph class represents the graph , it has two vectors one is the vertices included in this graph , and the other is the edges included in the graph. inside the class Graph there is a method its name shortest takes the sources node id and the destination and calculates the shortest path using dijkastra algorithm, and i think that it is the most important part of the code. my theory about the code is that i will create two vectors one for the vertices in the graph i will name it vertices and another vector its name is ver_out it will include the vertices out of calculation in the graph, also i will have two vectors of type Edge , one its name edges for all the edges in the graph and the other its name is track to contain temporarily the edges linked to the temporarily source node in every round , after the calculation of every round the vector track will be cleared. in main() i created five vertices and 10 edges to simulate a graph , the result of the shortest path supposedly to be 4 , but i am always getting 0 , that means i am having something wrong in my code , so if you are interesting in helping me find my mistake and how to make the code work , please take a look. the way shortest work is as follow at the beginning all the edges will be included in the vector edges , we select the edges related to the source and put them in the vector track , then we iterate through track and add the wight of every edge to the vertex (node ) related to it ( not the source vertex ) , then after we clear track and remove the source vertex from the vector vertices and select a new source , and start over again select the edges related to the new source , put them in track , iterate over edges in tack , adding the weights to the corresponding vertices then remove this vertex from the vector vertices, and clear track , and select a new source , and so on . here is the code. #include<iostream> #include<vector> #include <stdlib.h> // for rand() using namespace std; class Vertex { private: unsigned int id; // the name of the vertex unsigned int carried; // the weight a vertex may carry when calculating shortest path vector<unsigned int> previous_nodes; public: unsigned int get_id(){return id;}; unsigned int get_carried(){return carried;}; void set_id(unsigned int value) {id = value;}; void set_carried(unsigned int value) {carried = value;}; void previous_nodes_update(unsigned int val){previous_nodes.push_back(val);}; void previous_nodes_erase(unsigned int val){previous_nodes.erase(previous_nodes.begin() + val);}; Vertex(unsigned int init_val = 0, unsigned int init_carried = 0) :id (init_val), carried(init_carried) // constructor { } ~Vertex() {}; // destructor }; class Edge { private: Vertex first_vertex; // a vertex on one side of the edge Vertex second_vertex; // a vertex on the other side of the edge unsigned int weight; // the value of the edge ( or its weight ) public: unsigned int get_weight() {return weight;}; void set_weight(unsigned int value) {weight = value;}; Vertex get_ver_1(){return first_vertex;}; Vertex get_ver_2(){return second_vertex;}; void set_first_vertex(Vertex v1) {first_vertex = v1;}; void set_second_vertex(Vertex v2) {second_vertex = v2;}; Edge(const Vertex& vertex_1 = 0, const Vertex& vertex_2 = 0, unsigned int init_weight = 0) : first_vertex(vertex_1), second_vertex(vertex_2), weight(init_weight) { } ~Edge() {} ; // destructor }; class Graph { private: std::vector<Vertex> vertices; std::vector<Edge> edges; public: Graph(vector<Vertex> ver_vector, vector<Edge> edg_vector) : vertices(ver_vector), edges(edg_vector) { } ~Graph() {}; vector<Vertex> get_vertices(){return vertices;}; vector<Edge> get_edges(){return edges;}; void set_vertices(vector<Vertex> vector_value) {vertices = vector_value;}; void set_edges(vector<Edge> vector_ed_value) {edges = vector_ed_value;}; unsigned int shortest(unsigned int src, unsigned int dis) { vector<Vertex> ver_out; vector<Edge> track; for(unsigned int i = 0; i < edges.size(); ++i) { if((edges[i].get_ver_1().get_id() == vertices[src].get_id()) || (edges[i].get_ver_2().get_id() == vertices[src].get_id())) { track.push_back (edges[i]); edges.erase(edges.begin()+i); } }; for(unsigned int i = 0; i < track.size(); ++i) { if(track[i].get_ver_1().get_id() != vertices[src].get_id()) { track[i].get_ver_1().set_carried((track[i].get_weight()) + track[i].get_ver_2().get_carried()); track[i].get_ver_1().previous_nodes_update(vertices[src].get_id()); } else { track[i].get_ver_2().set_carried((track[i].get_weight()) + track[i].get_ver_1().get_carried()); track[i].get_ver_2().previous_nodes_update(vertices[src].get_id()); } } for(unsigned int i = 0; i < vertices.size(); ++i) if(vertices[i].get_id() == src) vertices.erase(vertices.begin() + i); // removing the sources vertex from the vertices vector ver_out.push_back (vertices[src]); track.clear(); if(vertices[0].get_id() != dis) {src = vertices[0].get_id();} else {src = vertices[1].get_id();} for(unsigned int i = 0; i < vertices.size(); ++i) if((vertices[i].get_carried() < vertices[src].get_carried()) && (vertices[i].get_id() != dis)) src = vertices[i].get_id(); //while(!edges.empty()) for(unsigned int round = 0; round < vertices.size(); ++round) { for(unsigned int k = 0; k < edges.size(); ++k) { if((edges[k].get_ver_1().get_id() == vertices[src].get_id()) || (edges[k].get_ver_2().get_id() == vertices[src].get_id())) { track.push_back (edges[k]); edges.erase(edges.begin()+k); } }; for(unsigned int n = 0; n < track.size(); ++n) if((track[n].get_ver_1().get_id() != vertices[src].get_id()) && (track[n].get_ver_1().get_carried() > (track[n].get_ver_2().get_carried() + track[n].get_weight()))) { track[n].get_ver_1().set_carried((track[n].get_weight()) + track[n].get_ver_2().get_carried()); track[n].get_ver_1().previous_nodes_update(vertices[src].get_id()); } else if(track[n].get_ver_2().get_carried() > (track[n].get_ver_1().get_carried() + track[n].get_weight())) { track[n].get_ver_2().set_carried((track[n].get_weight()) + track[n].get_ver_1().get_carried()); track[n].get_ver_2().previous_nodes_update(vertices[src].get_id()); } for(unsigned int t = 0; t < vertices.size(); ++t) if(vertices[t].get_id() == src) vertices.erase(vertices.begin() + t); track.clear(); if(vertices[0].get_id() != dis) {src = vertices[0].get_id();} else {src = vertices[1].get_id();} for(unsigned int tt = 0; tt < edges.size(); ++tt) { if(vertices[tt].get_carried() < vertices[src].get_carried()) { src = vertices[tt].get_id(); } } } return vertices[dis].get_carried(); } }; int main() { cout<< "Hello, This is a graph"<< endl; vector<Vertex> vers(5); vers[0].set_id(0); vers[1].set_id(1); vers[2].set_id(2); vers[3].set_id(3); vers[4].set_id(4); vector<Edge> eds(10); eds[0].set_first_vertex(vers[0]); eds[0].set_second_vertex(vers[1]); eds[0].set_weight(5); eds[1].set_first_vertex(vers[0]); eds[1].set_second_vertex(vers[2]); eds[1].set_weight(9); eds[2].set_first_vertex(vers[0]); eds[2].set_second_vertex(vers[3]); eds[2].set_weight(4); eds[3].set_first_vertex(vers[0]); eds[3].set_second_vertex(vers[4]); eds[3].set_weight(6); eds[4].set_first_vertex(vers[1]); eds[4].set_second_vertex(vers[2]); eds[4].set_weight(2); eds[5].set_first_vertex(vers[1]); eds[5].set_second_vertex(vers[3]); eds[5].set_weight(5); eds[6].set_first_vertex(vers[1]); eds[6].set_second_vertex(vers[4]); eds[6].set_weight(7); eds[7].set_first_vertex(vers[2]); eds[7].set_second_vertex(vers[3]); eds[7].set_weight(1); eds[8].set_first_vertex(vers[2]); eds[8].set_second_vertex(vers[4]); eds[8].set_weight(8); eds[9].set_first_vertex(vers[3]); eds[9].set_second_vertex(vers[4]); eds[9].set_weight(3); unsigned int path; Graph graf(vers, eds); path = graf.shortest(2, 4); cout<< path << endl; return 0; }

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  • Evaluating code for a graph [migrated]

    - by mazen.r.f
    This is relatively long code. Please take a look at this code if you are still willing to do so. I will appreciate your feedback. I have spent two days trying to come up with code to represent a graph, calculating the shortest path using Dijkstra's algorithm. But I am not able to get the right result, even though the code runs without errors. The result is not correct and I am always getting 0. I have three classes: Vertex, Edge, and Graph. The Vertex class represents the nodes in the graph and it has id and carried (which carry the weight of the links connected to it while using Dijkstra's algorithm) and a vector of the ids belong to other nodes the path will go through before arriving to the node itself. This vector is named previous_nodes. The Edge class represents the edges in the graph and has two vertices (one in each side) and a width (the distance between the two vertices). The Graph class represents the graph. It has two vectors, where one is the vertices included in this graph, and the other is the edges included in the graph. Inside the class Graph, there is a method named shortest() that takes the sources node id and the destination and calculates the shortest path using Dijkstra's algorithm. I think that it is the most important part of the code. My theory about the code is that I will create two vectors, one for the vertices in the graph named vertices, and another vector named ver_out (it will include the vertices out of calculation in the graph). I will also have two vectors of type Edge, where one is named edges (for all the edges in the graph), and the other is named track (to temporarily contain the edges linked to the temporary source node in every round). After the calculation of every round, the vector track will be cleared. In main(), I've created five vertices and 10 edges to simulate a graph. The result of the shortest path supposedly is 4, but I am always getting 0. That means I have something wrong in my code. If you are interesting in helping me find my mistake and making the code work, please take a look. The way shortest work is as follow: at the beginning, all the edges will be included in the vector edges. We select the edges related to the source and put them in the vector track, then we iterate through track and add the width of every edge to the vertex (node) related to it (not the source vertex). After that, we clear track and remove the source vertex from the vector vertices and select a new source. Then we start over again and select the edges related to the new source, put them in track, iterate over edges in track, adding the weights to the corresponding vertices, then remove this vertex from the vector vertices. Then clear track, and select a new source, and so on. #include<iostream> #include<vector> #include <stdlib.h> // for rand() using namespace std; class Vertex { private: unsigned int id; // the name of the vertex unsigned int carried; // the weight a vertex may carry when calculating shortest path vector<unsigned int> previous_nodes; public: unsigned int get_id(){return id;}; unsigned int get_carried(){return carried;}; void set_id(unsigned int value) {id = value;}; void set_carried(unsigned int value) {carried = value;}; void previous_nodes_update(unsigned int val){previous_nodes.push_back(val);}; void previous_nodes_erase(unsigned int val){previous_nodes.erase(previous_nodes.begin() + val);}; Vertex(unsigned int init_val = 0, unsigned int init_carried = 0) :id (init_val), carried(init_carried) // constructor { } ~Vertex() {}; // destructor }; class Edge { private: Vertex first_vertex; // a vertex on one side of the edge Vertex second_vertex; // a vertex on the other side of the edge unsigned int weight; // the value of the edge ( or its weight ) public: unsigned int get_weight() {return weight;}; void set_weight(unsigned int value) {weight = value;}; Vertex get_ver_1(){return first_vertex;}; Vertex get_ver_2(){return second_vertex;}; void set_first_vertex(Vertex v1) {first_vertex = v1;}; void set_second_vertex(Vertex v2) {second_vertex = v2;}; Edge(const Vertex& vertex_1 = 0, const Vertex& vertex_2 = 0, unsigned int init_weight = 0) : first_vertex(vertex_1), second_vertex(vertex_2), weight(init_weight) { } ~Edge() {} ; // destructor }; class Graph { private: std::vector<Vertex> vertices; std::vector<Edge> edges; public: Graph(vector<Vertex> ver_vector, vector<Edge> edg_vector) : vertices(ver_vector), edges(edg_vector) { } ~Graph() {}; vector<Vertex> get_vertices(){return vertices;}; vector<Edge> get_edges(){return edges;}; void set_vertices(vector<Vertex> vector_value) {vertices = vector_value;}; void set_edges(vector<Edge> vector_ed_value) {edges = vector_ed_value;}; unsigned int shortest(unsigned int src, unsigned int dis) { vector<Vertex> ver_out; vector<Edge> track; for(unsigned int i = 0; i < edges.size(); ++i) { if((edges[i].get_ver_1().get_id() == vertices[src].get_id()) || (edges[i].get_ver_2().get_id() == vertices[src].get_id())) { track.push_back (edges[i]); edges.erase(edges.begin()+i); } }; for(unsigned int i = 0; i < track.size(); ++i) { if(track[i].get_ver_1().get_id() != vertices[src].get_id()) { track[i].get_ver_1().set_carried((track[i].get_weight()) + track[i].get_ver_2().get_carried()); track[i].get_ver_1().previous_nodes_update(vertices[src].get_id()); } else { track[i].get_ver_2().set_carried((track[i].get_weight()) + track[i].get_ver_1().get_carried()); track[i].get_ver_2().previous_nodes_update(vertices[src].get_id()); } } for(unsigned int i = 0; i < vertices.size(); ++i) if(vertices[i].get_id() == src) vertices.erase(vertices.begin() + i); // removing the sources vertex from the vertices vector ver_out.push_back (vertices[src]); track.clear(); if(vertices[0].get_id() != dis) {src = vertices[0].get_id();} else {src = vertices[1].get_id();} for(unsigned int i = 0; i < vertices.size(); ++i) if((vertices[i].get_carried() < vertices[src].get_carried()) && (vertices[i].get_id() != dis)) src = vertices[i].get_id(); //while(!edges.empty()) for(unsigned int round = 0; round < vertices.size(); ++round) { for(unsigned int k = 0; k < edges.size(); ++k) { if((edges[k].get_ver_1().get_id() == vertices[src].get_id()) || (edges[k].get_ver_2().get_id() == vertices[src].get_id())) { track.push_back (edges[k]); edges.erase(edges.begin()+k); } }; for(unsigned int n = 0; n < track.size(); ++n) if((track[n].get_ver_1().get_id() != vertices[src].get_id()) && (track[n].get_ver_1().get_carried() > (track[n].get_ver_2().get_carried() + track[n].get_weight()))) { track[n].get_ver_1().set_carried((track[n].get_weight()) + track[n].get_ver_2().get_carried()); track[n].get_ver_1().previous_nodes_update(vertices[src].get_id()); } else if(track[n].get_ver_2().get_carried() > (track[n].get_ver_1().get_carried() + track[n].get_weight())) { track[n].get_ver_2().set_carried((track[n].get_weight()) + track[n].get_ver_1().get_carried()); track[n].get_ver_2().previous_nodes_update(vertices[src].get_id()); } for(unsigned int t = 0; t < vertices.size(); ++t) if(vertices[t].get_id() == src) vertices.erase(vertices.begin() + t); track.clear(); if(vertices[0].get_id() != dis) {src = vertices[0].get_id();} else {src = vertices[1].get_id();} for(unsigned int tt = 0; tt < edges.size(); ++tt) { if(vertices[tt].get_carried() < vertices[src].get_carried()) { src = vertices[tt].get_id(); } } } return vertices[dis].get_carried(); } }; int main() { cout<< "Hello, This is a graph"<< endl; vector<Vertex> vers(5); vers[0].set_id(0); vers[1].set_id(1); vers[2].set_id(2); vers[3].set_id(3); vers[4].set_id(4); vector<Edge> eds(10); eds[0].set_first_vertex(vers[0]); eds[0].set_second_vertex(vers[1]); eds[0].set_weight(5); eds[1].set_first_vertex(vers[0]); eds[1].set_second_vertex(vers[2]); eds[1].set_weight(9); eds[2].set_first_vertex(vers[0]); eds[2].set_second_vertex(vers[3]); eds[2].set_weight(4); eds[3].set_first_vertex(vers[0]); eds[3].set_second_vertex(vers[4]); eds[3].set_weight(6); eds[4].set_first_vertex(vers[1]); eds[4].set_second_vertex(vers[2]); eds[4].set_weight(2); eds[5].set_first_vertex(vers[1]); eds[5].set_second_vertex(vers[3]); eds[5].set_weight(5); eds[6].set_first_vertex(vers[1]); eds[6].set_second_vertex(vers[4]); eds[6].set_weight(7); eds[7].set_first_vertex(vers[2]); eds[7].set_second_vertex(vers[3]); eds[7].set_weight(1); eds[8].set_first_vertex(vers[2]); eds[8].set_second_vertex(vers[4]); eds[8].set_weight(8); eds[9].set_first_vertex(vers[3]); eds[9].set_second_vertex(vers[4]); eds[9].set_weight(3); unsigned int path; Graph graf(vers, eds); path = graf.shortest(2, 4); cout<< path << endl; return 0; }

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  • Graph Isomorphism > What kind of Graph is this?

    - by oodavid
    Essentially, this is a variation of Comparing Two Tree Structures, however I do not have "trees", but rather another type of graph. I need to know what kind of Graph I have in order to figure out if there's a Graph Isomorphism Special Case... As you can see, they are: Not Directed Not A Tree Cyclic Max 4 connections But I still don't know the correct terminology, nor the which Isomorphism algorithm to pursue, guidance appreciated.

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  • Finding most Important Node(s) in a Directed Graph

    - by Srikar Appal
    I have a large (˜ 20 million nodes) directed Graph with in-edges & out-edges. I want to figure out which parts of of the graph deserve the most attention. Often most of the graph is boring, or at least it is already well understood. The way I am defining "attention" is by the concept of "connectedness" i.e. How can i find the most connected node(s) in the graph? In what follows, One can assume that nodes by themselves have no score, the edges have no weight & they are either connected or not. This website suggest some pretty complicated procedures like n-dimensional space, Eigen Vectors, graph centrality concepts, pageRank etc. Is this problem that complex? Can I not do a simple Breadth-First Traversal of the entire graph where at each node I figure out a way to find the number of in-edges. The node with most in-edges is the most important node in the graph. Am I missing something here?

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  • Class instance clustering in object reference graph for multi-entries serialization

    - by Juh_
    My question is on the best way to cluster a graph of class instances (i.e. objects, the graph nodes) linked by object references (the -directed- edges of the graph) around specifically marked objects. To explain better my question, let me explain my motivation: I currently use a moderately complex system to serialize the data used in my projects: "marked" objects have a specific attributes which stores a "saving entry": the path to an associated file on disc (but it could be done for any storage type providing the suitable interface) Those object can then be serialized automatically (eg: obj.save()) The serialization of a marked object 'a' contains implicitly all objects 'b' for which 'a' has a reference to, directly s.t: a.b = b, or indirectly s.t.: a.c.b = b for some object 'c' This is very simple and basically define specific storage entries to specific objects. I have then "container" type objects that: can be serialized similarly (in fact their are or can-be "marked") they don't serialize in their storage entries the "marked" objects (with direct reference): if a and a.b are both marked, a.save() calls b.save() and stores a.b = storage_entry(b) So, if I serialize 'a', it will serialize automatically all objects that can be reached from 'a' through the object reference graph, possibly in multiples entries. That is what I want, and is usually provides the functionalities I need. However, it is very ad-hoc and there are some structural limitations to this approach: the multi-entry saving can only works through direct connections in "container" objects, and there are situations with undefined behavior such as if two "marked" objects 'a'and 'b' both have a reference to an unmarked object 'c'. In this case my system will stores 'c' in both 'a' and 'b' making an implicit copy which not only double the storage size, but also change the object reference graph after re-loading. I am thinking of generalizing the process. Apart for the practical questions on implementation (I am coding in python, and use Pickle to serialize my objects), there is a general question on the way to attach (cluster) unmarked objects to marked ones. So, my questions are: What are the important issues that should be considered? Basically why not just use any graph parsing algorithm with the "attach to last marked node" behavior. Is there any work done on this problem, practical or theoretical, that I should be aware of? Note: I added the tag graph-database because I think the answer might come from that fields, even if the question is not.

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  • Finding the shortest path through a digraph that visits all nodes

    - by Boluc Papuccuoglu
    I am trying to find the shortest possible path that visits every node through a graph (a node may be visited more than once, the solution may pick any node as the starting node.). The graph is directed, meaning that being able to travel from node A to node B does not mean one can travel from node B to node A. All distances between nodes are equal. I was able to code a brute force search that found a path of only 27 nodes when I had 27 nodes and each node had a connection to 2 or 1 other node. However, the actual problem that I am trying to solve consists of 256 nodes, with each node connecting to either 4 or 3 other nodes. The brute force algorithm that solved the 27 node graph can produce a 415 node solution (not optimal) within a few seconds, but using the processing power I have at my disposal takes about 6 hours to arrive at a 402 node solution. What approach should I use to arrive at a solution that I can be certain is the optimal one? For example, use an optimizer algorithm to shorten a non-optimal solution? Or somehow adopt a brute force search that discards paths that are not optimal? EDIT: (Copying a comment to an answer here to better clarify the question) To clarify, I am not saying that there is a Hamiltonian path and I need to find it, I am trying to find the shortest path in the 256 node graph that visits each node AT LEAST once. With the 27 node run, I was able to find a Hamiltonian path, which assured me that it was an optimal solution. I want to find a solution for the 256 node graph which is the shortest.

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  • Algorithm for perfect non-binary graph layout

    - by mariki
    I have a complex non-binary graph model. Each tree node can have multiple children&parents (a node can also have a connection to it's "brother"). A node is represented as square on screen with lines to the connected nodes. For that I want to use Draw2D and GEF libraries. The problem I am facing is the graph layout. I need a nice algorithm that can reposition the square nodes and the connections with minimum intersections and also make it symmetric as possible.

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  • Is finding graph minors without single node pinch points possible?

    - by Alturis
    Is it possible to robustly find all the graph minors within an arbitrary node graph where the pinch points are generally not single nodes? I have read some other posts on here about how to break up your graph into a Hamiltonian cycle and then from that find the graph minors but it seems to be such an algorithm would require that each "room" had "doorways" consisting of single nodes. To explain a bit more a visual aid is necessary. Lets say the nodes below are an example of the typical node graph. What I am looking for is a way to automatically find the different colored regions of the graph (or graph minors)

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  • Open Source Graph Layout Library

    - by James Westgate
    I'm looking for an open source (GPL, LGPL etc) graph layout library for .net framework, preferably fully managed code. Im not worried about the visualisation aspect of things. I can find lots of them for Java, but none for .net... Thanks!

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  • Am I right about the differences between Floyd-Warshall, Dijkstra's and Bellman-Ford algorithms?

    - by Programming Noob
    I've been studying the three and I'm stating my inferences from them below. Could someone tell me if I have understood them accurately enough or not? Thank you. Dijkstra's algorithm is used only when you have a single source and you want to know the smallest path from one node to another, but fails in cases like this Floyd-Warshall's algorithm is used when any of all the nodes can be a source, so you want the shortest distance to reach any destination node from any source node. This only fails when there are negative cycles (this is the most important one. I mean, this is the one I'm least sure about:) 3.Bellman-Ford is used like Dijkstra's, when there is only one source. This can handle negative weights and its working is the same as Floyd-Warshall's except for one source, right? If you need to have a look, the corresponding algorithms are (courtesy Wikipedia): Bellman-Ford: procedure BellmanFord(list vertices, list edges, vertex source) // This implementation takes in a graph, represented as lists of vertices // and edges, and modifies the vertices so that their distance and // predecessor attributes store the shortest paths. // Step 1: initialize graph for each vertex v in vertices: if v is source then v.distance := 0 else v.distance := infinity v.predecessor := null // Step 2: relax edges repeatedly for i from 1 to size(vertices)-1: for each edge uv in edges: // uv is the edge from u to v u := uv.source v := uv.destination if u.distance + uv.weight < v.distance: v.distance := u.distance + uv.weight v.predecessor := u // Step 3: check for negative-weight cycles for each edge uv in edges: u := uv.source v := uv.destination if u.distance + uv.weight < v.distance: error "Graph contains a negative-weight cycle" Dijkstra: 1 function Dijkstra(Graph, source): 2 for each vertex v in Graph: // Initializations 3 dist[v] := infinity ; // Unknown distance function from 4 // source to v 5 previous[v] := undefined ; // Previous node in optimal path 6 // from source 7 8 dist[source] := 0 ; // Distance from source to source 9 Q := the set of all nodes in Graph ; // All nodes in the graph are 10 // unoptimized - thus are in Q 11 while Q is not empty: // The main loop 12 u := vertex in Q with smallest distance in dist[] ; // Start node in first case 13 if dist[u] = infinity: 14 break ; // all remaining vertices are 15 // inaccessible from source 16 17 remove u from Q ; 18 for each neighbor v of u: // where v has not yet been 19 removed from Q. 20 alt := dist[u] + dist_between(u, v) ; 21 if alt < dist[v]: // Relax (u,v,a) 22 dist[v] := alt ; 23 previous[v] := u ; 24 decrease-key v in Q; // Reorder v in the Queue 25 return dist; Floyd-Warshall: 1 /* Assume a function edgeCost(i,j) which returns the cost of the edge from i to j 2 (infinity if there is none). 3 Also assume that n is the number of vertices and edgeCost(i,i) = 0 4 */ 5 6 int path[][]; 7 /* A 2-dimensional matrix. At each step in the algorithm, path[i][j] is the shortest path 8 from i to j using intermediate vertices (1..k-1). Each path[i][j] is initialized to 9 edgeCost(i,j). 10 */ 11 12 procedure FloydWarshall () 13 for k := 1 to n 14 for i := 1 to n 15 for j := 1 to n 16 path[i][j] = min ( path[i][j], path[i][k]+path[k][j] );

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  • Best Planar graph program

    - by brian
    In graph theory, a planar graph is a graph that can be embedded in the plane, i.e., it can be drawn on the plane in such a way that its edges intersect only at their endpoints. What is the best open source program for drawing the planar graph with support of input nodes size and fixed drawing boundary region

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  • Generic Adjacency List Graph implementation

    - by DmainEvent
    I am trying to come up with a decent Adjacency List graph implementation so I can start tooling around with all kinds of graph problems and algorithms like traveling salesman and other problems... But I can't seem to come up with a decent implementation. This is probably because I am trying to dust the cobwebs off my data structures class. But what I have so far... and this is implemented in Java... is basically an edgeNode class that has a generic type and a weight-in the event the graph is indeed weighted. public class edgeNode<E> { private E y; private int weight; //... getters and setters as well as constructors... } I have a graph class that has a list of edges a value for the number of Vertices and and an int value for edges as well as a boolean value for whether or not it is directed. The brings up my first question, if the graph is indeed directed, shouldn't I have a value in my edgeNode class? Or would I just need to add another vertices to my LinkedList? That would imply that a directed graph is 2X as big as an undirected graph wouldn't it? public class graph { private List<edgeNode<?>> edges; private int nVertices; private int nEdges; private boolean directed; //... getters and setters as well as constructors... } Finally does anybody have a standard way of initializing there graph? I was thinking of reading in a pipe-delimited file but that is so 1997. public graph GenereateGraph(boolean directed, String file){ List<edgeNode<?>> edges; graph g; try{ int count = 0; String line; FileReader input = new FileReader("C:\\Users\\derekww\\Documents\\JavaEE Projects\\graphFile"); BufferedReader bufRead = new BufferedReader(input); line = bufRead.readLine(); count++; edges = new ArrayList<edgeNode<?>>(); while(line != null){ line = bufRead.readLine(); Object edgeInfo = line.split("|")[0]; int weight = Integer.parseInt(line.split("|")[1]); edgeNode<String> e = new edgeNode<String>((String) edges.add(e); } return g; } catch(Exception e){ return null; } } I guess when I am adding edges if boolean is true I would be adding a second edge. So far, this all depends on the file I write. So if I wrote a file with the following Vertices and weights... Buffalo | 18 br Pittsburgh | 20 br New York | 15 br D.C | 45 br I would obviously load them into my list of edges, but how can I represent one vertices connected to the other... so on... I would need the opposite vertices? Say I was representing Highways connected to each city weighted and un-directed (each edge is bi-directional with weights in some fictional distance unit)... Would my implementation be the best way to do that? I found this tutorial online Graph Tutorial that has a connector object. This appears to me be a collection of vertices pointing to each other. So you would have A and B each with there weights and so on, and you would add this to a list and this list of connectors to your graph... That strikes me as somewhat cumbersome and a little dismissive of the adjacency list concept? Am I wrong and that is a novel solution? This is all inspired by steve skiena's Algorithm Design Manual. Which I have to say is pretty good so far. Thanks for any help you can provide.

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  • A new mission statement for my school's algorithms class

    - by Eric Fode
    The teacher at Eastern Washington University that is now teaching the algorithms course is new to eastern and as a result the course has changed drastically mostly in the right direction. That being said I feel that the class could use a more specific, and industry oriented (since that is where most students will go, though suggestions for an academia oriented class are also welcome) direction, having only worked in industry for 2 years I would like the community's (a wider and much more collectively experienced and in the end plausibly more credible) opinion on the quality of this as a statement for the purpose an algorithms class, and if I am completely off target your suggestion for the purpose of a required Jr. level Algorithms class that is standalone (so no other classes focusing specifically on algorithms are required). The statement is as follows: The purpose of the algorithms class is to do three things: Primarily, to teach how to learn, do basic analysis, and implement a given algorithm found outside of the class. Secondly, to teach the student how to model a problem in their mind so that they can find a an existing algorithm or have a direction to start the development of a new algorithm. Third, to overview a variety of algorithms that exist and to deeply understand and analyze one algorithm in each of the basic algorithmic design strategies: Divide and Conquer, Reduce and Conquer, Transform and Conquer, Greedy, Brute Force, Iterative Improvement and Dynamic Programming. The Question in short is: do you agree with this statement of the purpose of an algorithms course, so that it would be useful in the real world, if not what would you suggest?

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  • C# Algorithms for * Operator

    - by Harsha
    I was reading up on Algorithms and came across the Karatsuba multiplication algorithm and a little wiki-ing led to the Schonhage-Strassen and Furer algorithms for multiplication. I was wondering what algorithms are used on the * operator in C#? While multiplying a pair of integers or doubles, does it use a combination of algorithms with some kind of strategy based on the size of the numbers? How could I find out the implementation details for C#?

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  • I don't understand why algorithms are so special

    - by Jessica
    I'm a student of computer science trying to soak up as much information on the topic as I can during my free time. I keep returning to algorithms time and again in various formats (online course, book, web tutorial), but the concept fails to sustain my attention. I just don't understand: why are algorithms so special? I can tell you why fractals are awesome, why the golden ratio is awesome, why origami is awesome and scientific applications of all the above. Heck I even love Newton's laws and conical sections. But when it comes to algorithms, I'm just not astounded. They are not insightful in new ways about human cognition at all. I was expecting algorithms to be shattering preconceptions and mind-altering but time and time again they fail miserably. What am I doing wrong in my approach? Can someone tell me why algorithms are so awesome?

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  • How can I find the shortest path between two subgraphs of a larger graph?

    - by Pops
    I'm working with a weighted, undirected multigraph (loops not permitted; most node connections have multiplicity 1; a few node connections have multiplicity 2). I need to find the shortest path between two subgraphs of this graph that do not overlap with each other. There are no other restrictions on which nodes should be used as start/end points. Edges can be selectively removed from the graph at certain times (as explained in my previous question) so it's possible that for two given subgraphs, there might not be any way to connect them. I'm pretty sure I've heard of an algorithm for this before, but I can't remember what it's called, and my Google searches for strings like "shortest path between subgraphs" haven't helped. Can someone suggest a more efficient way to do this than comparing shortest paths between all nodes in one subgraph with all nodes in the other subgraph? Or at least tell me the name of the algorithm so I can look it up myself? For example, if I have the graph below, the nodes circled in red might be one subgraph and the nodes circled in blue might be another. The edges would all have positive integer weights, although they're not shown in the image. I'd want to find whatever path has the shortest total cost as long as it starts at a red node and ends at a blue node. I believe this means the specific node positions and edge weights cannot be ignored. (This is just an example graph I grabbed off Wikimedia and drew on, not my actual problem.)

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  • Given an XML which contains a representation of a graph, how to apply it DFS algorithm? [on hold]

    - by winston smith
    Given the followin XML which is a directed graph: <?xml version="1.0" encoding="iso-8859-1" ?> <!DOCTYPE graph PUBLIC "-//FC//DTD red//EN" "../dtd/graph.dtd"> <graph direct="1"> <vertex label="V0"/> <vertex label="V1"/> <vertex label="V2"/> <vertex label="V3"/> <vertex label="V4"/> <vertex label="V5"/> <edge source="V0" target="V1" weight="1"/> <edge source="V0" target="V4" weight="1"/> <edge source="V5" target="V2" weight="1"/> <edge source="V5" target="V4" weight="1"/> <edge source="V1" target="V2" weight="1"/> <edge source="V1" target="V3" weight="1"/> <edge source="V1" target="V4" weight="1"/> <edge source="V2" target="V3" weight="1"/> </graph> With this classes i parsed the graph and give it an adjacency list representation: import java.io.IOException; import java.util.HashSet; import java.util.LinkedList; import java.util.Collection; import java.util.Iterator; import java.util.logging.Level; import java.util.logging.Logger; import practica3.util.Disc; public class ParsingXML { public static void main(String[] args) { try { // TODO code application logic here Collection<Vertex> sources = new HashSet<Vertex>(); LinkedList<String> lines = Disc.readFile("xml/directed.xml"); for (String lin : lines) { int i = Disc.find(lin, "source=\""); String data = ""; if (i > 0 && i < lin.length()) { while (lin.charAt(i + 1) != '"') { data += lin.charAt(i + 1); i++; } Vertex v = new Vertex(); v.setName(data); v.setAdy(new HashSet<Vertex>()); sources.add(v); } } Iterator it = sources.iterator(); while (it.hasNext()) { Vertex ver = (Vertex) it.next(); Collection<Vertex> adyacencias = ver.getAdy(); LinkedList<String> ls = Disc.readFile("xml/graphs.xml"); for (String lin : ls) { int i = Disc.find(lin, "target=\""); String data = ""; if (lin.contains("source=\""+ver.getName())) { Vertex v = new Vertex(); if (i > 0 && i < lin.length()) { while (lin.charAt(i + 1) != '"') { data += lin.charAt(i + 1); i++; } v.setName(data); } i = Disc.find(lin, "weight=\""); data = ""; if (i > 0 && i < lin.length()) { while (lin.charAt(i + 1) != '"') { data += lin.charAt(i + 1); i++; } v.setWeight(Integer.parseInt(data)); } if (v.getName() != null) { adyacencias.add(v); } } } } for (Vertex vert : sources) { System.out.println(vert); System.out.println("adyacencias: " + vert.getAdy()); } } catch (IOException ex) { Logger.getLogger(ParsingXML.class.getName()).log(Level.SEVERE, null, ex); } } } This is another class: import java.util.Collection; import java.util.Objects; public class Vertex { private String name; private int weight; private Collection ady; public Collection getAdy() { return ady; } public void setAdy(Collection adyacencias) { this.ady = adyacencias; } public String getName() { return name; } public void setName(String nombre) { this.name = nombre; } public int getWeight() { return weight; } public void setWeight(int weight) { this.weight = weight; } @Override public int hashCode() { int hash = 7; hash = 43 * hash + Objects.hashCode(this.name); hash = 43 * hash + this.weight; return hash; } @Override public boolean equals(Object obj) { if (obj == null) { return false; } if (getClass() != obj.getClass()) { return false; } final Vertex other = (Vertex) obj; if (!Objects.equals(this.name, other.name)) { return false; } if (this.weight != other.weight) { return false; } return true; } @Override public String toString() { return "Vertice{" + "name=" + name + ", weight=" + weight + '}'; } } And finally: /** * * @author user */ /* -*-jde-*- */ /* <Disc.java> Contains the main argument*/ import java.io.*; import java.util.LinkedList; /** * Lectura y escritura de archivos en listas de cadenas * Ideal para el uso de las clases para gráficas. * * @author Peralta Santa Anna Victor Miguel * @since Julio 2011 */ public class Disc { /** * Metodo para lectura de un archivo * * @param fileName archivo que se va a leer * @return El archivo en representacion de lista de cadenas */ public static LinkedList<String> readFile(String fileName) throws IOException { BufferedReader file = new BufferedReader(new FileReader(fileName)); LinkedList<String> textlist = new LinkedList<String>(); while (file.ready()) { textlist.add(file.readLine().trim()); } file.close(); /* for(String linea:textlist){ if(linea.contains("source")){ //String generado = linea.replaceAll("<\\w+\\s+\"", ""); //System.out.println(generado); } }*/ return textlist; }//readFile public static int find(String linea,String palabra){ int i,j; boolean found = false; for(i=0,j=0;i<linea.length();i++){ if(linea.charAt(i)==palabra.charAt(j)){ j++; if(j==palabra.length()){ found = true; return i; } }else{ continue; } } if(!found){ i= -1; } return i; } /** * Metodo para la escritura de un archivo * * @param fileName archivo que se va a escribir * @param tofile la lista de cadenas que quedaran en el archivo * @param append el bit que dira si se anexa el contenido o se empieza de cero */ public static void writeFile(String fileName, LinkedList<String> tofile, boolean append) throws IOException { FileWriter file = new FileWriter(fileName, append); for (int i = 0; i < tofile.size(); i++) { file.write(tofile.get(i) + "\n"); } file.close(); }//writeFile /** * Metodo para escritura de un archivo * @param msg archivo que se va a escribir * @param tofile la cadena que quedaran en el archivo * @param append el bit que dira si se anexa el contenido o se empieza de cero */ public static void writeFile(String msg, String tofile, boolean append) throws IOException { FileWriter file = new FileWriter(msg, append); file.write(tofile); file.close(); }//writeFile }// I'm stuck on what can be the best way to given an adjacency list representation of the graph how to apply it Depth-first search algorithm. Any idea of how to aproach to complete the task?

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  • What's a good, quick algorithms refresh?

    - by Casey Patton
    I have programming interviews coming up in a couple weeks. I took an algorithms class a while ago but likely forgot some key concepts. I'm looking for something like a very short book (< 100 pages) on algorithms to get back up to speed. Sorting algorithms, data structures, and any other essentials should be included. It doesn't have to be a book...just looking for a great way to get caught up in about a week. What's the best tool for a quick algorithms intro or refresher?

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  • What are the algorithms that are used for working with large data in popular web applications

    - by Moss Farmer
    I am looking for some well known algorithms that can be considered while handling very large amount of data.(Edit- By large amount of data I refer to records in a database excluding blobs). These algorithms if not in totality but in parts may be used in big web applications like Twitter, Last.fm , Amazon ,etc. Specifically, I'm looking for names or links to such algorithms. My primary interest lies in developing a very deep understanding on working with large database records and writing efficient code for working with the same.

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  • C++: Error in Xcode; "Graph::Coordinate::Coordinate()", referenced from: ...

    - by Alexandstein
    In a program I am writing, I wrote for two classes (Coordinate, and Graph), with one of them taking the other as constructor arguments. When I try to compile it I get the following error for Graph.cpp: Undefined symbols: "Graph::Coordinate::Coordinate(double)", referenced from: Graph::Graph() in Graph.o Graph::Graph() in Graph.o "Graph::Coordinate::Coordinate()", referenced from: Graph::Graph(Graph::Coordinate, Graph::Coordinate, Graph::Coordinate)in Graph.o Graph::Graph(Graph::Coordinate, Graph::Coordinate, Graph::Coordinate)in Graph.o Graph::Graph(Graph::Coordinate, Graph::Coordinate, Graph::Coordinate)in Graph.o Graph::Graph(Graph::Coordinate, Graph::Coordinate, Graph::Coordinate)in Graph.o Graph::Graph(Graph::Coordinate, Graph::Coordinate, Graph::Coordinate)in Graph.o Graph::Graph(Graph::Coordinate, Graph::Coordinate, Graph::Coordinate)in Graph.o Graph::Graph(Graph::Coordinate, Graph::Coordinate)in Graph.o Graph::Graph(Graph::Coordinate, Graph::Coordinate)in Graph.o Graph::Graph(Graph::Coordinate, Graph::Coordinate)in Graph.o Graph::Graph(Graph::Coordinate, Graph::Coordinate)in Graph.o Graph::Graph(Graph::Coordinate, Graph::Coordinate)in Graph.o Graph::Graph(Graph::Coordinate, Graph::Coordinate)in Graph.o Graph::Graph(Graph::Coordinate)in Graph.o Graph::Graph(Graph::Coordinate)in Graph.o Graph::Graph(Graph::Coordinate)in Graph.o Graph::Graph(Graph::Coordinate)in Graph.o Graph::Graph(Graph::Coordinate)in Graph.o Graph::Graph(Graph::Coordinate)in Graph.o Graph::Graph() in Graph.o Graph::Graph() in Graph.o Graph::Graph() in Graph.o Graph::Graph() in Graph.o Graph::Graph() in Graph.o Graph::Graph() in Graph.o ld: symbol(s) not found collect2: ld returned 1 exit status I checked the code and couldn't find anything out of the ordinary. Here are the four class files: (Sorry if it's a lot of code to sift through.) Coordinate.h class Graph{ #include "Coordinate.h" public: Graph(); Graph(Coordinate); Graph(Coordinate, Coordinate); Graph(Coordinate, Coordinate, Coordinate); void setXSize(int); void setYSize(int); void setX(int); //int corresponds to coordinates 1, 2, or 3 void setY(int); void setZ(int); int getXSize(); int getYSize(); double getX(int); //int corresponds to coordinates 1, 2, or 3 double getY(int); double getZ(int); void outputGraph(); void animateGraph(); private: int xSize; int ySize; Coordinate coord1; Coordinate coord2; Coordinate coord3; }; Coordinate.cpp #include <iostream> #include "Coordinate.h" Coordinate::Coordinate() { xCoord = 1; yCoord = 1; zCoord = 1; xVel = 1; yVel = 1; zVel = 1; } Coordinate::Coordinate(double xCoo) { xCoord = xCoo; yCoord = 1; zCoord = 1; xVel = 1; yVel = 1; zVel = 1; } Coordinate::Coordinate(double xCoo,double yCoo) { xCoord = xCoo; yCoord = yCoo; zCoord = 1; xVel = 1; yVel = 1; zVel = 1; } Coordinate::Coordinate(double xCoo,double yCoo,double zCoo) { xCoord = xCoo; yCoord = yCoo; zCoord = zCoo; xVel = 1; yVel = 1; zVel = 1; } void Coordinate::setXCoord(double xCoo) { xCoord = xCoo; } void Coordinate::setYCoord(double yCoo) { yCoord = yCoo; } void Coordinate::setZCoord(double zCoo) { zCoord = zCoo; } void Coordinate::setXVel(double xVelo) { xVel = xVelo; } void Coordinate::setYVel(double yVelo) { yVel = yVelo; } void Coordinate::setZVel(double zVelo) { zVel = zVelo; } double Coordinate::getXCoord() { return xCoord; } double Coordinate::getYCoord() { return yCoord; } double Coordinate::getZCoord() { return zCoord; } double Coordinate::getXVel() { return xVel; } double Coordinate::GetYVel() { return yVel; } double Coordinate::GetZVel() { return zVel; } Graph.h class Graph{ #include "Coordinate.h" public: Graph(); Graph(Coordinate); Graph(Coordinate, Coordinate); Graph(Coordinate, Coordinate, Coordinate); void setXSize(int); void setYSize(int); void setX(int); //int corresponds to coordinates 1, 2, or 3 void setY(int); void setZ(int); int getXSize(); int getYSize(); double getX(int); //int corresponds to coordinates 1, 2, or 3 double getY(int); double getZ(int); void outputGraph(); void animateGraph(); private: int xSize; int ySize; Coordinate coord1; Coordinate coord2; Coordinate coord3; }; Graph.cpp #include "Graph.h" #include "Coordinate.h" #include <iostream> #include <ctime> using namespace std; Graph::Graph() { Coordinate coord1(0); } Graph::Graph(Coordinate cOne) { coord1 = cOne; xSize = 20; ySize = 20; } Graph::Graph(Coordinate cOne, Coordinate cTwo) { coord1 = cOne; coord2 = cTwo; xSize = 20; ySize = 20; } Graph::Graph(Coordinate cOne, Coordinate cTwo, Coordinate cThree) { coord1 = cOne; coord2 = cTwo; coord3 = cThree; xSize = 20; ySize = 20; } void Graph::setXSize(int size) { xSize = size; } void Graph::setYSize(int size) { ySize = size; } int Graph::getXSize() { return xSize; } int Graph::getYSize() { return ySize; } void Graph::outputGraph() { } void Graph::animateGraph() { } Thanks very much for any help!

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  • How to use Boost 1.41.0 graph layout algorithmes

    - by daniil-k
    Hi I have problem using boost graph layout algorithmes. boost verision 1_41_0 mingw g++ 4.4.0. So there are issues I have encountered Can you suggest me with them? The function fruchterman_reingold_force_directed_layout isn't compiled. The kamada_kawai_spring_layout compiled but program crashed. Boost documentation to layout algorithms is wrong, sample to fruchterman_reingold_force_directed_layout isn't compiled. This is my example. To use function just uncomment one. String 60, 61, 63. #include <boost/config.hpp> #include <boost/graph/adjacency_list.hpp> #include <boost/graph/graph_utility.hpp> #include <boost/graph/simple_point.hpp> #include <boost/property_map/property_map.hpp> #include <boost/graph/circle_layout.hpp> #include <boost/graph/fruchterman_reingold.hpp> #include <boost/graph/kamada_kawai_spring_layout.hpp> #include <iostream> //typedef boost::square_topology<>::point_difference_type Point; typedef boost::square_topology<>::point_type Point; struct VertexProperties { std::size_t index; Point point; }; struct EdgeProperty { EdgeProperty(const std::size_t &w):weight(w) {} double weight; }; typedef boost::adjacency_list<boost::listS, boost::listS, boost::undirectedS, VertexProperties, EdgeProperty > Graph; typedef boost::property_map<Graph, std::size_t VertexProperties::*>::type VertexIndexPropertyMap; typedef boost::property_map<Graph, Point VertexProperties::*>::type PositionMap; typedef boost::property_map<Graph, double EdgeProperty::*>::type WeightPropertyMap; typedef boost::graph_traits<Graph>::vertex_descriptor VirtexDescriptor; int main() { Graph graph; VertexIndexPropertyMap vertexIdPropertyMap = boost::get(&VertexProperties::index, graph); for (int i = 0; i < 3; ++i) { VirtexDescriptor vd = boost::add_vertex(graph); vertexIdPropertyMap[vd] = i + 2; } boost::add_edge(boost::vertex(1, graph), boost::vertex(0, graph), EdgeProperty(5), graph); boost::add_edge(boost::vertex(2, graph), boost::vertex(0, graph), EdgeProperty(5), graph); std::cout << "Vertices\n"; boost::print_vertices(graph, vertexIdPropertyMap); std::cout << "Edges\n"; boost::print_edges(graph, vertexIdPropertyMap); PositionMap positionMap = boost::get(&VertexProperties::point, graph); WeightPropertyMap weightPropertyMap = boost::get(&EdgeProperty::weight, graph); boost::circle_graph_layout(graph, positionMap, 100); // boost::fruchterman_reingold_force_directed_layout(graph, positionMap, boost::square_topology<>()); boost::kamada_kawai_spring_layout(graph, positionMap, weightPropertyMap, boost::square_topology<>(), boost::side_length<double>(10), boost::layout_tolerance<>(), 1, vertexIdPropertyMap); std::cout << "Coordinates\n"; boost::graph_traits<Graph>::vertex_iterator i, end; for (boost::tie(i, end) = boost::vertices(graph); i != end; ++i) { std::cout << "ID: (" << vertexIdPropertyMap[*i] << ") x: " << positionMap[*i][0] << " y: " << positionMap[*i][1] << "\n"; } return 0; }

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  • test cases for common algorithms [on hold]

    - by Alexey
    I need samples of test inputs and correct outputs for common algorithms for sorting, searching, data structures, graphs, etc. to check for mistakes in my future implementations. Can you advice resources with test cases? Or a website with community that implements algorithms and shares with results? Thanks! Edit: to clarify: I am going to implement forementioned algorithms for studying purposes and need inputs including large ones and correct outputs to better find mistakes in my implementations, since test cases that I can come up with on my own with might not be enough to reveal mistakes.

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  • Genetic algorithms with large chromosomes

    - by Howie
    I'm trying to solve the graph partitioning problem on large graphs (between a billion and trillion elements) using GA. The problem is that even one chromosome will take several gigs of memory. Are there any general compression techniques for chromosome encoding? Or should I look into distributed GA? NOTE: using some sort of evolutionary algorithm for this problem is a must! GA seems to be the best fit (although not for such large chromosomes). EDIT: I'm looking for state-of-the-art methods that other authors have used to solved the problem of large chromosomes. Note that I'm looking for either a more general solution or a solution particular to graph partitioning. Basically I'm looking for related works, as I, too, am attempting using GA for the problem of graph partitioning. So far I haven't found anyone that might have this problem of large chromosomes nor has tried to solve it.

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  • Algorithms and Programmer's day-to-day job

    - by Lior Kogan
    As of July 10, 2012, Stack Overflow contains 3,345,864 questions, out of which 20,840 questions are tagged as "Algorithm" - this is less than 0.6% ! I find it disturbing. Many programmers have several years of academic education in computer science / software engineering. Most of them are smart... When asked, most would say that they love algorithms. Computer programming is generally about solving problems using algorithms... Yet, only 1 of 160 questions is tagged as algorithm related. What does it say about our profession?

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  • Easy to understand and interesting book on algorithms

    - by gasan
    Please advise me a book on algorithms, that would be easier to read and understand than Cormen's book1. It may be not so big and deep in explanation. I even want it to not be that big, however it shouldn't contain misconceptions or errors or inaccuracies. It should be a some kind of pre-Cormen's book, that will help later to understand more sophisticated conceptions. A beginner book (but still worth to read). 1 Introduction to Algorithms by Thomas H. Cormen, Charles E. Leiserson, Ronald L. Rivest, and Clifford Stein

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