# Dijkstra edge weights

The idea is to use BFS. One important observation about BFS is, the path used in BFS always has least number of edges between any two vertices. So if all edges are of same weight, we can use BFS to find the shortest path. For this problem, we can modify the graph and split all edges of weight 2 into two edges of weight 1 each.

In the modified graph, we can use BFS to find the shortest path. How many new intermediate vertices are needed? We need to add a new intermediate vertex for every source vertex. The reason is simple, if we add a intermediate vertex x between u and v and if we add same vertex between y and z, then new paths u to z and y to v are added to graph which might have note been there in original graph. Therefore in a graph with V vertices, we need V extra vertices. This way we make sure that a different intermediate vertex is added for every source vertex.

This article is contributed by Aditya Goel. Please write comments if you find anything incorrect, or you want to share more information about the topic discussed above. Writing code in comment? Please use ide. Minimum value of distance of farthest node in a Graph Find count of pair of nodes at even distance Minimum cost to traverse from one index to another in the String Find the minimum spanning tree with alternating colored edges Minimum number of edges that need to be added to form a triangle Find all cliques of size K in an undirected graph Maximal Clique Problem Recursive Solution Check if given path between two nodes of a graph represents a shortest paths Shortest path with exactly k edges in a directed and weighted graph Set 2 Add and Remove vertex in Adjacency Matrix representation of Graph Count ways to change direction of edges such that graph becomes acyclic Maximum number of edges that N-vertex graph can have such that graph is Triangle free Mantel's Theorem.

Expected time complexity. Graph::Graph int V. The intermediate. It is assumed. Graph g V. SuppressWarnings "unchecked". Graph int V. This class represents a directed graph using adjacency list representation. To print the shortest path stored in parent[]. Mark all the vertices as not visited. Initialize parent[] and visited[]. Create a queue for BFS. Mark the source node as visited and enqueue it. Dequeue a vertex from queue. Get all adjacent vertices of the dequeued vertex s. If a adjacent has not been visited, then mark it.

Create a graph given in the above diagram. This code is contributed by Neelam Yadav.Every path in a weighted digraph has an associated path weightthe value of which is the sum of the weights of that path's edges.

This essential measure allows us to formulate such problems as "find the lowest-weight path between two given vertices. Not only are shortest-paths problems intuitive for many direct applications, but they also take us into a powerful and general realm where we seek efficient algorithms to solve general problems that can encompass a broad variety of specific applications.

Several of the algorithms that we consider in this chapter relate directly to various algorithms that we examined in Chapters 17 through Our basic graph-search paradigm applies immediately, and several of the specific mechanisms that we used in Chapters 17 and 19 to address connectivity in graphs and digraphs provide the basis for us to solve shortest-paths problems.

For economy, we refer to weighted digraphs as networks. Figure We have already developed an ADT interface with adjacency-matrix and adjacency-lists class implementations for networks in Section Sample network and representations. As discussed at length in Chapter 20, we use references to abstract edges for weighted digraphs to broaden the applicability of our implementations.

This approach has certain implications that are different for digraphs than the ones that we considered for undirected graphs in Section First, since there is only one representation of each edge, we do not need to use the from method in the edge class see Program Second, as we saw in Chapter 19, it is often useful when processing a digraph to be able to work with its reverse graph, but we need a different approach than that taken by Program In applications or systems for which we need all types of graphs, it is a textbook exercise in software engineering to define a network ADT from which ADTs for the unweighted undirected graphs of Chapters 17 and 18, the unweighted digraphs of Chapter 19, or the weighted undirected graphs of Chapter 20 can be derived see Exercise When we work with networks, it is generally convenient to keep self-loops in all the representations.

This convention allows algorithms the flexibility to use a sentinel maximum-value weight to indicate that a vertex cannot be reached from itself. In our examples, we use self-loops of weight 0although positive-weight self-loops certainly make sense in many applications.

Many applications also call for parallel edges, perhaps with differing weights. As we mentioned in Section In this chapter, for simplicity, none of our examples use parallel edges, and we do not allow parallel edges in the adjacency-matrix representation; we also do not check for parallel edges or remove them in adjacency lists. All the connectivity properties of digraphs that we considered in Chapter 19 are relevant in networks.

Definition This definition is succinct, but its brevity masks points worth examining. First, if t is not reachable from sthere is no path at all, and therefore there is no shortest path.

For convenience, the algorithms that we consider often treat this case as equivalent to one in which there exists an infinite-weight path from s to t.

Second, as we did for MST algorithms, we use networks where edge weights are proportional to edge lengths in examples, but the definition has no such requirement and our algorithms other than the one in Section Indeed, shortest-paths algorithms are at their best when they discover counterintuitive shortcuts, such as a path between two vertices that passes through several other vertices but has total weight smaller than that of a direct edge connecting those vertices.

Third, there may be multiple paths of the same weight from one vertex to another; we typically are content to find one of them. Shortest-path trees. The restriction in the definition to simple paths is unnecessary in networks that contain edges that have nonnegative weight, because any cycle in a path in such a network can be removed to give a path that is no longer and is shorter unless the cycle comprises zero-weight edges.

But when we consider networks with edges that could have negative weight, the need for the restriction to simple paths is readily apparent: Otherwise, the concept of a shortest path is meaningless if there is a cycle in the network that has negative weight. For example, suppose that the edge in the network in Figure By using our site, you acknowledge that you have read and understand our Cookie PolicyPrivacy Policyand our Terms of Service.

The dark mode beta is finally here. Change your preferences any time. Stack Overflow for Teams is a private, secure spot for you and your coworkers to find and share information. Can somebody tell me why Dijkstra's algorithm for single source shortest path assumes that the edges must be non-negative.

Recall that in Dijkstra's algorithm, once a vertex is marked as "closed" and out of the open set - the algorithm found the shortest path to itand will never have to develop this node again - it assumes the path developed to this path is the shortest. Note that this is important, because in each relaxation step, the algorithm assumes the "cost" to the "closed" nodes is indeed minimal, and thus the node that will next be selected is also minimal.

The idea of it is: If we have a vertex in open such that its cost is minimal - by adding any positive number to any vertex - the minimality will never change.

Without the constraint on positive numbers - the above assumption is not true. Since we do "know" each vertex which was "closed" is minimal - we can safely do the relaxation step - without "looking back".

If we do need to "look back" - Bellman-Ford offers a recursive-like DP solution of doing so. So starting out the values the distance from the source to the vertex initially assigned to each vertex are.

Note A has a directed edge to B and C, also both of them are in Q, therefore we update both of those values. Note that C is connected to nothing, so line16 loop doesn't run. Finally we extract B, after which. Note B has a directed edge to C but C isn't present in Q therefore we again don't enter the for loop in line16. So for this graph Dijkstra's Algorithm wrongly computes the distance from A to C. This happens because Dijkstra's Algorithm does not try to find a shorter path to vertices which are already extracted from Q.

What the line16 loop is doing is taking the vertex u and saying "hey looks like we can go to v from source via uis that alt or alternative distance any better than the current dist[v] we got?

If so lets update dist[v] ". Note that in line16 they check all neighbors v i. In line14 they remove visited notes from Q. So if x is a visited neighbour of uthe path is not even considered as a possible shorter way from source to v. In our example above, C was a visited neighbour of B, thus the path was not considered, leaving the current shortest path unchanged. This is actually useful if the edge weights are all positive numbersbecause then we wouldn't waste our time considering paths that can't be shorter.

So I say that when running this algorithm if x is extracted from Q before ythen its not possible to find a path - which is shorter. Let me explain this with an example. And as we already assumed that the edge weights are positive, i.

So the alternative distance alt via y is always sure to be greater, i. So the value of dist[x] would not have been updated even if y was considered as a path to xthus we conclude that it makes sense to only consider neighbors of y which are still in Q note comment in line So any dist[x] calculation we make will be incorrect if x is removed before all vertices v - such that x is a neighbour of v with negative edge connecting them - is removed. So in the example I gave above, the mistake was because C was removed before B was removed.

While that C was a neighbour of B with a negative edge! Just to clarify, B and C are A's neighbours. B has a single neighbour C and C has no neighbours. Dijkstra's algorithm assumes paths can only become 'heavier', so that if you have a path from A to B with a weight of 3, and a path from A to C with a weight of 3, there's no way you can add an edge and get from A to B through C with a weight of less than 3. This assumption makes the algorithm faster than algorithms that have to take negative weights into account.

We have 2 sets of vertices at any step of the algorithm. Set A consists of the vertices to which we have computed the shortest paths.This course covers the essential information that every serious programmer needs to know about algorithms and data structures, with emphasis on applications and scientific performance analysis of Java implementations.

### Shortest Path in a weighted Graph where weight of an edge is 1 or 2

Part I covers elementary data structures, sorting, and searching algorithms. Part II focuses on graph- and string-processing algorithms. All the features of this course are available for free. It does not offer a certificate upon completion. Excellent course that provides a good introduction to more advanced algorithms that build on those presented in part 1 of the course.

Thank you Professor Sedgewick and Kevin Wayne. This class is amazing. If you follow this course schedule strictly, and finish the homework and interview questions, you are gonna to learn a lot of algorithm thinking methods.

In this lecture we study shortest-paths problems.

We begin by analyzing some basic properties of shortest paths and a generic algorithm for the problem. We introduce and analyze Dijkstra's algorithm for shortest-paths problems with nonnegative weights. Next, we consider an even faster algorithm for DAGs, which works even if the weights are negative.

We also consider applications ranging from content-aware fill to arbitrage. Loupe Copy. Algorithms, Part II. Enroll for Free. From the lesson. Shortest Paths APIs Shortest Path Properties Dijkstra's Algorithm Edge-Weighted DAGs Negative Weights Taught By.

Mathematics Stack Exchange is a question and answer site for people studying math at any level and professionals in related fields. It only takes a minute to sign up. H edges. B-C is 1 and B-F is 1. How do I decide which point to select? In the image, we can see the obvious shortest path is a-b-f-e-h total weight being 9. If we go via C, the path would be a-b-c-f-e-h amounting to It doesn't matter which one you select, the algorithm will always find a shortest path.

The only difference is that it might find a different path of equal length if you make a different choice here. So the step by step solution in this case is: Initially, the distance to A is 0 and all other distances are infinite. The old path to F was shorter. Now we pick F, since this is the unique node that is the minimum distance univisited node. Sign up to join this community. The best answers are voted up and rise to the top. Home Questions Tags Users Unanswered. What to do if 2 weights are equal in Dijkstra Shortest path algorithm? Ask Question. Asked 3 years, 2 months ago. Active 1 year, 9 months ago. Viewed 8k times. Edit: In the image, we can see the obvious shortest path is a-b-f-e-h total weight being 9. Saksham Chawla Saksham Chawla 13 1 1 silver badge 4 4 bronze badges.

Suppose I want to run Dijkstra's algorithm on a graph whose edge weights are integers in the range 0, Easy: instead of a queue of vertices, we will have a queue of buckets. Each bucket contains a set of vertices that all have the same distance estimate, and the priority of a bucket is that distance estimate. The queue contains vertices that are one edge away from one of those finalized vertices.

## Weighted Graphs and Dijkstra's Algorithm

To make this work, we'll have to make one other change: instead of implementing the priority queue as a heap, we'll implement it as a balanced binary search tree, using the distance as the key. Dijkstra's algorithm for edge weights in range 0, …, W Ask Question. Asked 6 years, 3 months ago. Active 6 years, 3 months ago. Viewed 8k times. Claribelle Claribelle 31 1 1 silver badge 2 2 bronze badges.

It would be very useful as a starting point. Active Oldest Votes. This algorithm is almost easier to implement than to describe You have to find the bucket for a given distance, which seems more search tree than priority like? I've edited my answer accordingly. With that modification, I think it now works. Thank you for catching that. The queue contains all the vertices that have not yet been visited.

Shortest Path using Dijkstra's Algorithm

## Shortest Paths Graph Algorithms

It only takes a minute to sign up. I'm doing this only for educationnal purposes, so when i was studying at college i was computer science student we've studied Dijkstra's algorithm for shortest path and our teacher said to us that : "Dijkstra's algorithm doesn't work with graphs which contains negative weight, and explained it to us", i really don't remeber the explication, and i forget a little about the algorithm, but now i think for making the Dijkstra's algorithm working for graphes with negative weights.

The trick is easy, Dijkstra algorithm doesn't work for negative weights, so we will force every weight to be in positive, and that by adding to each edge, the inverse of min negative weight, by that we have forced the graph to contains only positive weights, then we proceced with Dijkstra's algorithm, at the end we substract the value which we added it.

It seems to me as it works because we have just did added to every edge the same value, then at the end we substract it. So now, there's a negative wheight, we will take the min weight from the graph and add the inverse of it to all edges. I think this trick should still produce the shortest path for negative weights, because we just added the same value to each edge, the as equations, when we add a value to an equation, it stills valid :.

It doesn't because as steve-kass said, the shortest path may have more edges than other paths, thus when adding weight too each edge, there will be more total weight added to the shortest path than to other paths which use fewer edges. You could keep track of the depth of each node, then subtract v. Sign up to join this community. The best answers are voted up and rise to the top.

Home Questions Tags Users Unanswered. Tricking Dijkstra's Algorithm for negative weights Ask Question. Asked 4 years ago.

Active 1 year, 5 months ago. Viewed 3k times. MJD Would your method work? Active Oldest Votes. Marcus Chiu Marcus Chiu 1 2 2 bronze badges. Dijkstra's algorithm works in the usual case because we never need to wait for more options to get to a node to appear, in case they're better.

Here, that's not true; maybe there's a long indirect path to get to some node, which we haven't explored yet, but which will be much better because we get to subtract a much larger v. I'll keep that in mind to ask a new question in situations like this. Anyways, with you previous comment, that is true. But in lines each weight is increased by min-weight. All the problems with that algorithm still exist here. Sign up or log in Sign up using Google. Sign up using Facebook. Sign up using Email and Password.

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