The **bellman-Ford algorithm** helps us locate the briefest way from a vertex to any remaining vertices of a weighted graph.

In this article, you will learn-

- 1 What is the Bellman-Ford algorithm?
- 2 Why would one ever have edges with negative weights in real life?
- 3 Why do we need to be careful with negative weights?
- 4 How Bellman Ford’s algorithm works
- 5 Bellman-Ford Pseudocode
- 6 Bellman Ford versus Dijkstra
- 7 Python, Java, and C/C++ Examples
- 8 Bellman Ford’s Complexity
- 9 Bellman Ford’s Algorithm Applications

## What is the Bellman-Ford algorithm?

The **Bellman-Ford algorithm** is an extension of Dijkstra’s algorithm which calculates the briefest separation from the source highlight the entirety of the vertices. While Dijkstra’s algorithm simply works for edges with positive distances, Bellman Ford’s algorithm works for negative distances also.

It is like Dijkstra’s algorithm yet it can work with graphs in which edges can have negative weights.

## Why would one ever have edges with negative weights in real life?

Negative weight edges may appear to be futile from the start yet they can clarify a ton of phenomena like cashflow, the warmth released/absorbed in a chemical reaction, and so forth

For example, if there are various approaches to reach starting with one chemical A then onto the next chemical B, every strategy will have sub-reactions including both warmth dissipation and absorption.

On the off chance that we need to locate the arrangement of responses where the least energy is required, at that point we should have the option to factor in the warmth absorption as negative weights and warmth dissipation as positive weights.

## Why do we need to be careful with negative weights?

Negative weight edges can create negative weight cycles for example a cycle that will diminish the complete way distance by returning to a similar point.

Most brief way algorithms like Dijkstra’s Algorithm that can’t recognize such a cycle can give a mistaken outcome since they can experience a negative weight cycle and reduce the way length.

## How Bellman Ford’s algorithm works

Bellman Ford’s algorithm works by overestimating the length of the way from the beginning vertex to any remaining vertices. At that point, it iteratively relaxes those estimates by finding new ways that are shorter than the previously overestimated ways.

By doing this more than once for all vertices, we can ensure that the outcome is optimized.

Negative weight cycles can give an incorrect outcome when attempting to discover the briefest path

Step-2 for Bellman-Ford algorithm

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Step-4 for Bellman-Ford algorithm

Step-5 for Bellman-Ford algorithm

Step-6 for Bellman-Ford algorithm

## Bellman-Ford Pseudocode

We need to keep up the way distance of each vertex. We can store that in an array of size v, where v is the number of vertices.

We likewise need to have the option to get the briefest way, not just know the length of the shortest way. For this, we map every vertex to the vertex that last updated its way length.

When the algorithm is finished, we can backtrack from the destination vertex to the source vertex to discover the way.

function bellmanFord(G, S) for each vertex V in G distance[V] <- infinite previous[V] <- NULL distance[S] <- 0 for each vertex V in G for each edge (U,V) in G tempDistance <- distance[U] + edge_weight(U, V) if tempDistance < distance[V] distance[V] <- tempDistance previous[V] <- U for each edge (U,V) in G If distance[U] + edge_weight(U, V) < distance[V} Error: Negative Cycle Exists return distance[], previous[]

## Bellman Ford versus Dijkstra

Bellman-Ford algorithm and Dijkstra’s algorithm are fundamentally the same as in structure. While Dijkstra looks just to the immediate neighbors of a vertex, Bellman experiences each edge in each iteration.

Dijkstra’s vs Bellman-Ford Algorithm

## Python, Java, and C/C++ Examples

**Python**

# Bellman Ford Algorithm in Python class Graph: def __init__(self, vertices): self.V = vertices # Total number of vertices in the graph self.graph = [] # Array of edges # Add edges def add_edge(self, s, d, w): self.graph.append([s, d, w]) # Print the solution def print_solution(self, dist): print("Vertex Distance from Source") for i in range(self.V): print("{0}\t\t{1}".format(i, dist[i])) def bellman_ford(self, src): # Step 1: fill the distance array and predecessor array dist = [float("Inf")] * self.V # Mark the source vertex dist[src] = 0 # Step 2: relax edges |V| - 1 times for _ in range(self.V - 1): for s, d, w in self.graph: if dist[s] != float("Inf") and dist[s] + w < dist[d]: dist[d] = dist[s] + w # Step 3: detect negative cycle # if value changes then we have a negative cycle in the graph # and we cannot find the shortest distances for s, d, w in self.graph: if dist[s] != float("Inf") and dist[s] + w < dist[d]: print("Graph contains negative weight cycle") return # No negative weight cycle found! # Print the distance and predecessor array self.print_solution(dist) g = Graph(5) g.add_edge(0, 1, 5) g.add_edge(0, 2, 4) g.add_edge(1, 3, 3) g.add_edge(2, 1, 6) g.add_edge(3, 2, 2) g.bellman_ford(0)

**Java**

// Bellman Ford Algorithm in Java class CreateGraph { // CreateGraph - it consists of edges class CreateEdge { int s, d, w; CreateEdge() { s = d = w = 0; } }; int V, E; CreateEdge edge[]; // Creates a graph with V vertices and E edges CreateGraph(int v, int e) { V = v; E = e; edge = new CreateEdge[e]; for (int i = 0; i < e; ++i) edge[i] = new CreateEdge(); } void BellmanFord(CreateGraph graph, int s) { int V = graph.V, E = graph.E; int dist[] = new int[V]; // Step 1: fill the distance array and predecessor array for (int i = 0; i < V; ++i) dist[i] = Integer.MAX_VALUE; // Mark the source vertex dist[s] = 0; // Step 2: relax edges |V| - 1 times for (int i = 1; i < V; ++i) { for (int j = 0; j < E; ++j) { // Get the edge data int u = graph.edge[j].s; int v = graph.edge[j].d; int w = graph.edge[j].w; if (dist[u] != Integer.MAX_VALUE && dist[u] + w < dist[v]) dist[v] = dist[u] + w; } } // Step 3: detect negative cycle // if value changes then we have a negative cycle in the graph // and we cannot find the shortest distances for (int j = 0; j < E; ++j) { int u = graph.edge[j].s; int v = graph.edge[j].d; int w = graph.edge[j].w; if (dist[u] != Integer.MAX_VALUE && dist[u] + w < dist[v]) { System.out.println("CreateGraph contains negative w cycle"); return; } } // No negative w cycle found! // Print the distance and predecessor array printSolution(dist, V); } // Print the solution void printSolution(int dist[], int V) { System.out.println("Vertex Distance from Source"); for (int i = 0; i < V; ++i) System.out.println(i + "\t\t" + dist[i]); } public static void main(String[] args) { int V = 5; // Total vertices int E = 8; // Total Edges CreateGraph graph = new CreateGraph(V, E); // edge 0 --> 1 graph.edge[0].s = 0; graph.edge[0].d = 1; graph.edge[0].w = 5; // edge 0 --> 2 graph.edge[1].s = 0; graph.edge[1].d = 2; graph.edge[1].w = 4; // edge 1 --> 3 graph.edge[2].s = 1; graph.edge[2].d = 3; graph.edge[2].w = 3; // edge 2 --> 1 graph.edge[3].s = 2; graph.edge[3].d = 1; graph.edge[3].w = 6; // edge 3 --> 2 graph.edge[4].s = 3; graph.edge[4].d = 2; graph.edge[4].w = 2; graph.BellmanFord(graph, 0); // 0 is the source vertex } }

**C**

// Bellman Ford Algorithm in C #include <stdio.h> #include <stdlib.h> #define INFINITY 99999 //struct for the edges of the graph struct Edge { int u; //start vertex of the edge int v; //end vertex of the edge int w; //weight of the edge (u,v) }; //Graph - it consists of edges struct Graph { int V; //total number of vertices in the graph int E; //total number of edges in the graph struct Edge *edge; //array of edges }; void bellmanford(struct Graph *g, int source); void display(int arr[], int size); int main(void) { //create graph struct Graph *g = (struct Graph *)malloc(sizeof(struct Graph)); g->V = 4; //total vertices g->E = 5; //total edges //array of edges for graph g->edge = (struct Edge *)malloc(g->E * sizeof(struct Edge)); //------- adding the edges of the graph /* edge(u, v) where u = start vertex of the edge (u,v) v = end vertex of the edge (u,v) w is the weight of the edge (u,v) */ //edge 0 --> 1 g->edge[0].u = 0; g->edge[0].v = 1; g->edge[0].w = 5; //edge 0 --> 2 g->edge[1].u = 0; g->edge[1].v = 2; g->edge[1].w = 4; //edge 1 --> 3 g->edge[2].u = 1; g->edge[2].v = 3; g->edge[2].w = 3; //edge 2 --> 1 g->edge[3].u = 2; g->edge[3].v = 1; g->edge[3].w = 6; //edge 3 --> 2 g->edge[4].u = 3; g->edge[4].v = 2; g->edge[4].w = 2; bellmanford(g, 0); //0 is the source vertex return 0; } void bellmanford(struct Graph *g, int source) { //variables int i, j, u, v, w; //total vertex in the graph g int tV = g->V; //total edge in the graph g int tE = g->E; //distance array //size equal to the number of vertices of the graph g int d[tV]; //predecessor array //size equal to the number of vertices of the graph g int p[tV]; //step 1: fill the distance array and predecessor array for (i = 0; i < tV; i++) { d[i] = INFINITY; p[i] = 0; } //mark the source vertex d[source] = 0; //step 2: relax edges |V| - 1 times for (i = 1; i <= tV - 1; i++) { for (j = 0; j < tE; j++) { //get the edge data u = g->edge[j].u; v = g->edge[j].v; w = g->edge[j].w; if (d[u] != INFINITY && d[v] > d[u] + w) { d[v] = d[u] + w; p[v] = u; } } } //step 3: detect negative cycle //if value changes then we have a negative cycle in the graph //and we cannot find the shortest distances for (i = 0; i < tE; i++) { u = g->edge[i].u; v = g->edge[i].v; w = g->edge[i].w; if (d[u] != INFINITY && d[v] > d[u] + w) { printf("Negative weight cycle detected!\n"); return; } } //No negative weight cycle found! //print the distance and predecessor array printf("Distance array: "); display(d, tV); printf("Predecessor array: "); display(p, tV); } void display(int arr[], int size) { int i; for (i = 0; i < size; i++) { printf("%d ", arr[i]); } printf("\n"); }

**C++**

// Bellman Ford Algorithm in C++ #include <bits/stdc++.h> // Struct for the edges of the graph struct Edge { int u; //start vertex of the edge int v; //end vertex of the edge int w; //w of the edge (u,v) }; // Graph - it consists of edges struct Graph { int V; // Total number of vertices in the graph int E; // Total number of edges in the graph struct Edge* edge; // Array of edges }; // Creates a graph with V vertices and E edges struct Graph* createGraph(int V, int E) { struct Graph* graph = new Graph; graph->V = V; // Total Vertices graph->E = E; // Total edges // Array of edges for graph graph->edge = new Edge[E]; return graph; } // Printing the solution void printArr(int arr[], int size) { int i; for (i = 0; i < size; i++) { printf("%d ", arr[i]); } printf("\n"); } void BellmanFord(struct Graph* graph, int u) { int V = graph->V; int E = graph->E; int dist[V]; // Step 1: fill the distance array and predecessor array for (int i = 0; i < V; i++) dist[i] = INT_MAX; // Mark the source vertex dist[u] = 0; // Step 2: relax edges |V| - 1 times for (int i = 1; i <= V - 1; i++) { for (int j = 0; j < E; j++) { // Get the edge data int u = graph->edge[j].u; int v = graph->edge[j].v; int w = graph->edge[j].w; if (dist[u] != INT_MAX && dist[u] + w < dist[v]) dist[v] = dist[u] + w; } } // Step 3: detect negative cycle // if value changes then we have a negative cycle in the graph // and we cannot find the shortest distances for (int i = 0; i < E; i++) { int u = graph->edge[i].u; int v = graph->edge[i].v; int w = graph->edge[i].w; if (dist[u] != INT_MAX && dist[u] + w < dist[v]) { printf("Graph contains negative w cycle"); return; } } // No negative weight cycle found! // Print the distance and predecessor array printArr(dist, V); return; } int main() { // Create a graph int V = 5; // Total vertices int E = 8; // Total edges // Array of edges for graph struct Graph* graph = createGraph(V, E); //------- adding the edges of the graph /* edge(u, v) where u = start vertex of the edge (u,v) v = end vertex of the edge (u,v) w is the weight of the edge (u,v) */ //edge 0 --> 1 graph->edge[0].u = 0; graph->edge[0].v = 1; graph->edge[0].w = 5; //edge 0 --> 2 graph->edge[1].u = 0; graph->edge[1].v = 2; graph->edge[1].w = 4; //edge 1 --> 3 graph->edge[2].u = 1; graph->edge[2].v = 3; graph->edge[2].w = 3; //edge 2 --> 1 graph->edge[3].u = 2; graph->edge[3].v = 1; graph->edge[3].w = 6; //edge 3 --> 2 graph->edge[4].u = 3; graph->edge[4].v = 2; graph->edge[4].w = 2; BellmanFord(graph, 0); //0 is the source vertex return 0; }

## Bellman Ford’s Complexity

### Time Complexity

Best Case Complexity | O(E) |

Average Case Complexity | O(VE) |

Worst Case Complexity | O(VE) |

### Space Complexity

And, the space complexity is O(V).

## Bellman Ford’s Algorithm Applications

- For calculating briefest paths in routing algorithms
- For finding the shortest path

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