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Dijkstra Shortest Path Algorithm | Greedy on Graphs
Greedy Algorithms using Python
course content

Conteúdo do Curso

Greedy Algorithms using Python

Greedy Algorithms using Python

1. Greedy Algorithms: Overview and Examples
2. Greedy on Arrays
3. Greedy on Graphs

bookDijkstra Shortest Path Algorithm

The Dijkstra algorithm is a very popular and useful algorithm, which is used for searching the shortest path between two vertices, or between the start vertex and all other vertices at all. This algorithm isn't perfect at all, but it returns the shortest path always for a weighted graph with positive weights (or paths). Yes, sometimes edges can have a negative value of 'weight'.

This is a step-by-step algorithm to visit all the nodes, and every time update the minimum path from start to the current node. So for each vertex, we have a dist[vertex] tag – minimum path length which is found now.

Initially, the start node has tag 0 and all the other nodes have tag inf.

The algorithm is next:

  1. Select the current vertex v. It should be the closest one (with minimum value of dist[v]) and not visited yet.
  2. If there is no such a vertex v or the distance to it is equal to inf, we should stop the algorithm. There is no way to access the other vertices.
  3. For each neighbor of current node v update tags: dist[neighbor] = min(dist[neighbor], dist[v] + g[v][neighbor]) - distance has the minimum value now.
  4. Stop if all nodes are visited.

On the gif, you can see the demo of how it works. After completing the task, the graph from a gif is created, and you can follow it step-by-step.

Tarefa

Complete the algorithm following the comments in the code.

Switch to desktopMude para o desktop para praticar no mundo realContinue de onde você está usando uma das opções abaixo
Tudo estava claro?

Como podemos melhorá-lo?

Obrigado pelo seu feedback!

Seção 3. Capítulo 2
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bookDijkstra Shortest Path Algorithm

The Dijkstra algorithm is a very popular and useful algorithm, which is used for searching the shortest path between two vertices, or between the start vertex and all other vertices at all. This algorithm isn't perfect at all, but it returns the shortest path always for a weighted graph with positive weights (or paths). Yes, sometimes edges can have a negative value of 'weight'.

This is a step-by-step algorithm to visit all the nodes, and every time update the minimum path from start to the current node. So for each vertex, we have a dist[vertex] tag – minimum path length which is found now.

Initially, the start node has tag 0 and all the other nodes have tag inf.

The algorithm is next:

  1. Select the current vertex v. It should be the closest one (with minimum value of dist[v]) and not visited yet.
  2. If there is no such a vertex v or the distance to it is equal to inf, we should stop the algorithm. There is no way to access the other vertices.
  3. For each neighbor of current node v update tags: dist[neighbor] = min(dist[neighbor], dist[v] + g[v][neighbor]) - distance has the minimum value now.
  4. Stop if all nodes are visited.

On the gif, you can see the demo of how it works. After completing the task, the graph from a gif is created, and you can follow it step-by-step.

Tarefa

Complete the algorithm following the comments in the code.

Switch to desktopMude para o desktop para praticar no mundo realContinue de onde você está usando uma das opções abaixo
Tudo estava claro?

Como podemos melhorá-lo?

Obrigado pelo seu feedback!

Seção 3. Capítulo 2
toggle bottom row

bookDijkstra Shortest Path Algorithm

The Dijkstra algorithm is a very popular and useful algorithm, which is used for searching the shortest path between two vertices, or between the start vertex and all other vertices at all. This algorithm isn't perfect at all, but it returns the shortest path always for a weighted graph with positive weights (or paths). Yes, sometimes edges can have a negative value of 'weight'.

This is a step-by-step algorithm to visit all the nodes, and every time update the minimum path from start to the current node. So for each vertex, we have a dist[vertex] tag – minimum path length which is found now.

Initially, the start node has tag 0 and all the other nodes have tag inf.

The algorithm is next:

  1. Select the current vertex v. It should be the closest one (with minimum value of dist[v]) and not visited yet.
  2. If there is no such a vertex v or the distance to it is equal to inf, we should stop the algorithm. There is no way to access the other vertices.
  3. For each neighbor of current node v update tags: dist[neighbor] = min(dist[neighbor], dist[v] + g[v][neighbor]) - distance has the minimum value now.
  4. Stop if all nodes are visited.

On the gif, you can see the demo of how it works. After completing the task, the graph from a gif is created, and you can follow it step-by-step.

Tarefa

Complete the algorithm following the comments in the code.

Switch to desktopMude para o desktop para praticar no mundo realContinue de onde você está usando uma das opções abaixo
Tudo estava claro?

Como podemos melhorá-lo?

Obrigado pelo seu feedback!

The Dijkstra algorithm is a very popular and useful algorithm, which is used for searching the shortest path between two vertices, or between the start vertex and all other vertices at all. This algorithm isn't perfect at all, but it returns the shortest path always for a weighted graph with positive weights (or paths). Yes, sometimes edges can have a negative value of 'weight'.

This is a step-by-step algorithm to visit all the nodes, and every time update the minimum path from start to the current node. So for each vertex, we have a dist[vertex] tag – minimum path length which is found now.

Initially, the start node has tag 0 and all the other nodes have tag inf.

The algorithm is next:

  1. Select the current vertex v. It should be the closest one (with minimum value of dist[v]) and not visited yet.
  2. If there is no such a vertex v or the distance to it is equal to inf, we should stop the algorithm. There is no way to access the other vertices.
  3. For each neighbor of current node v update tags: dist[neighbor] = min(dist[neighbor], dist[v] + g[v][neighbor]) - distance has the minimum value now.
  4. Stop if all nodes are visited.

On the gif, you can see the demo of how it works. After completing the task, the graph from a gif is created, and you can follow it step-by-step.

Tarefa

Complete the algorithm following the comments in the code.

Switch to desktopMude para o desktop para praticar no mundo realContinue de onde você está usando uma das opções abaixo
Seção 3. Capítulo 2
Switch to desktopMude para o desktop para praticar no mundo realContinue de onde você está usando uma das opções abaixo
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