Dijkstra in all languages
Dijkstra in Java
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Java™ is a compiled language used for many purposes, ranging from embedded systems, UIapplications to web servers.
See CodeDijkstra in C
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C is a compiled language used for many purposes, although it can be primarily found in systems where importance is important. This is because C offers a lot of lowlevel support for optimization, at the cost of not having some of the convenient abstractions that other languages offer. C is therefore primarily found in situations where available computation power is low such as embedded systems, or situations where required computation power is high, such as simulation or deep learning.
See CodeDijkstra in Javascript
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JavaScript JavaScript is an interpreted scripting language previously primarily used in web pages (executed in browsers) that has since…
See CodeDijkstra in Python
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Python Python™ is an interpreted language used for many purposes ranging from embedded programming to web development, with one of the largest use cases being data science.
See CodeDijkstra in R
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R R is an interpreted language first released in 1993 with a significant increase in popularity in recent years. It is primarily used for data mining and science as well as statistics, and is a popular language in noncomputer science disciplines ranging from Biology to Physics. R is dynamically typed, and has one of the widest variety of libraries for statistics, machine learning, data mining etc.
See CodeAbout the algorithm:
Dijkstra's Algorithm
The Dijkstra algorithm is an algorithm used to solve the shortest path problem in a graph. This means that given a number of nodes and the edges between them as well as the “length” of the edges (referred to as “weight”), the Dijkstra algorithm is finds the shortest path from the specified start node to all other nodes. Nodes are sometimes referred to as vertices (plural of vertex)  here, we’ll call them nodes.
Description of the Algorithm
The basic principle behind the Dijkstra algorithm is to iteratively look at the node with the currently smallest distance to the source and update all not yet visited neighbors if the path to it via the current node is shorter. In more detail, this leads to the following Steps:
 Initialize the distance to the starting node as 0 and the distances to all other nodes as infinite
 Set all nodes to “unvisited”

While we haven’t visited all nodes:
 Find the node with currently shortest distance from the source (for the first pass, this will be the source node itself)
 For all nodes next to it that we haven’t visited yet, check if the currently smallest distance to that neighbor is bigger than if we were to go via the current node
 If it is, update the smallest distance of that neighbor to be the distance from the source to the current node plus the distance from the current node to that neighbor
In the end, the array we used to keep track of the currently shortest distance from the source to all other nodes will contain the (final) shortest distances.
Example of the Algorithm
The steps the algorithm performs on this graph if given node 0 as a starting point, in order, are:
 Visiting node 0
 Updating distance of node 1 to 3
 Updating distance of node 2 to 1
 Visited nodes: 0
 Currently lowest distances: [0, 3, 1, Infinite, Infinite, Infinite]

Visiting node 1 with current distance 1
 Updating distance of node 3 to 5
 Visited nodes: 0, 2
 Currently lowest distances: [0, 3, 1, 5, Infinite, Infinite]

Visiting node 3 with current distance 3
 Updating distance of node 4 to 4
 Visited nodes: 0, 1, 2
 Currently lowest distances: [0, 3, 1, 5, 4, Infinite]

Visiting node 4 with current distance 4
 Updating distance of node 5 to 5
 Visited nodes: 0, 1, 2, 4
 Currently lowest distances: [0, 3, 1, 5, 4, 5]

Visiting node 5 with current distance 5
 No distances to update
 Visited nodes: 0, 1, 2, 3, 4
 Currently lowest distances: [0, 3, 1, 5, 4, 5]

Visiting node 5 with current distance 5
 No distances to update
 Visited nodes: 0, 1, 2, 3, 4, 5
All nodes visited Final lowest distances: [0, 3, 1, 5, 4, 5]
Runtime of the Algorithm
The runtime complexity of Dijkstra depends on how it is implemented. If a minheap is used to determine the next node to visit, and adjacency is implemented using adjacency lists, the runtime is O(E + VlogV) (V = number of Nodes, E = number of Edges). If a we simply search all distances to find the node with the lowest distance in each step, and use a matrix to look up whether two nodes are adjacent, the runtime complexity increases to O(V^2).
Space of the Algorithm
The space complexity of Dijkstra depends on how it is implemented as well and is equal to the runtime complexity.