In graph theory, the shortest path problem is the problem of finding a path between two vertices (or nodes) in a graph such that the sum of the weights of its constituent edges is minimized.
The problem of finding the shortest path between two intersections on a road map (the graph's vertices correspond to intersections and the edges correspond to road segments, each weighted by the length of its road segment) may be modeled by a special case of the shortest path problem in graphs.
Definition
The shortest path problem can be defined for graphs whether undirected, directed, or mixed.
It is defined here for undirected graphs; for directed graphs the definition of path
requires that consecutive vertices be connected by an appropriate directed edge.
Two vertices are adjacent when they are both incident to a common edge.
A path in an undirected graph is a sequence of vertices
such that is adjacent to for .
Such a path is called a path of length
from to .
(The are variables; their numbering here relates to their position in the sequence and needs not to relate to any canonical labeling of the vertices.)
The algorithm exists in many variants; Dijkstra's original variant found the shortest path between two nodes, but a more common variant fixes a single node as the "source" node and finds shortest paths from the source to all other nodes in the graph, producing a shortest-path tree.
For a given source node in the graph, the algorithm finds the shortest path between that node and every other. It can also be used for finding the shortest paths from a single node to a single destination node by stopping the algorithm once the shortest path to the destination node has been determined. For example, if the nodes of the graph represent cities and edge path costs represent driving distances between pairs of cities connected by a direct road, Dijkstra's algorithm can be used to find the shortest route between one city and all other cities. As a result, the shortest path algorithm is widely used in network routing protocols, most notably IS-IS and Open Shortest Path First (OSPF). It is also employed as a subroutine in other algorithms such as Johnson's.
A graph data structure consists of a finite (and possibly mutable) set of vertices or nodes or points, together with a set of unordered pairs of these vertices for an undirected graph or a set of ordered pairs for a directed graph. These pairs are known as edges, arcs, or lines for an undirected graph and as arrows, directed edges, directed arcs, or directed lines for a directed graph. The vertices may be part of the graph structure, or may be external entities represented by integer indices or references.
A graph data structure may also associate to each edge some edge value, such as a symbolic label or a numeric attribute (cost, capacity, length, etc.).
Operations
The basic operations provided by a graph data structure G usually include:
adjacent(G, x, y): tests whether there is an edge from the vertices x to y;
neighbors(G, x): lists all vertices y such that there is an edge from the vertices x to y;
Step by step instructions showing how to run Dijkstra's algorithm on a graph.
Code: https://github.com/msambol/dsa/blob/master/shortest_path/dijkstras.py (different than video, I added this retroactively)
Source: Algorithms by S. Dasgupta, C. H. Papadimitriou, and U. V. Vazirani [https://www.amazon.com/Algorithms-Sanjoy-Dasgupta/dp/0073523402]
LinkedIn: https://www.linkedin.com/in/michael-sambol
published: 16 Sep 2014
Graph Data Structure 4. Dijkstra’s Shortest Path Algorithm
This is the fourth in a series of computer science videos about the graph data structure. This is an explanation of Dijkstra’s algorithm for finding the shortest path between one vertex in a graph and another. Indeed, this explains how Dijkstra’s shortest path algorithm generates a set of information that includes the shortest paths from a starting vertex and every other vertex in the graph. It also mentions why Dijkstra’s shortest path algorithm is an example of a greedy algorithm.
Dijkstra Algorithm for Single Source Shortest Path
Procedure
Examples
Time Complexity
Drawbacks
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Courses on Udemy
================
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Data Structures using C and C++
https://www.udemy.com/course/datastructurescncpp/?referralCode=BD2EF8E61A98AB5E011D
C++ Programming
https://www.udemy.com/course/cpp-deep-dive/?referralCode=E4246A516919D7E84225
published: 09 Feb 2018
Dijkstra's Algorithm - Computerphile
Dijkstra's Algorithm finds the shortest path between two points. Dr Mike Pound explains how it works.
How Sat Nav Works: https://youtu.be/EUrU1y5is3Y
Slow Loris Attack: https://youtu.be/XiFkyR35v2Y
http://www.facebook.com/computerphile
https://twitter.com/computer_phile
This video was filmed and edited by Sean Riley.
Computer Science at the University of Nottingham: http://bit.ly/nottscomputer
Computerphile is a sister project to Brady Haran's Numberphile. More at http://www.bradyharan.com
published: 04 Jan 2017
Implement Dijkstra's Algorithm
Implement Dijkstra's shortest path algorithm yourself here: https://neetcode.io/problems/dijkstra
🚀 https://neetcode.io/ - A better way to prepare for Coding Interviews
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#neetcode #leetcode #python
published: 21 Sep 2023
How Dijkstra's Algorithm Works
Dijkstra's Algorithm allows us to find the shortest path between two vertices in a graph. Here, we explore the intuition behind the algorithm — what information we need to keep track of, in what order we need to explore vertices, and what the limitations of the algorithm are.
***
Spanning Tree is an educational video series about computer science and mathematics. See more at https://spanningtree.me
To be notified when a new video is released, sign up for the Spanning Tree mailing list at https://spanningtree.substack.com/
Spanning Tree is created by Brian Yu. https://brianyu.me/
Email me at [email protected] to suggest a future topic.
published: 15 Aug 2020
6.13 Dijkstra Algorithm | Single Source Shortest Path| Greedy Method
In this video I have explained Dijkstra's Algorithm with some Examples. It is Single Source Shortest Path Algorithm and use Greedy Method.
DSA Full Course: https: https://www.youtube.com/playlist?list=PLdo5W4Nhv31bbKJzrsKfMpo_grxuLl8LU
******************************************
More Playlists:
C Programming Course: https://www.youtube.com/playlist?list=PLdo5W4Nhv31a8UcMN9-35ghv8qyFWD9_S
C++ Programming: https://www.youtube.com/playlist?list=PLdo5W4Nhv31YU5Wx1dopka58teWP9aCee
Python Full Course: https://www.youtube.com/playlist?list=PLdo5W4Nhv31bZSiqiOL5ta39vSnBxpOPT
Printing Pattern in C: https://www.youtube.com/playlist?list=PLdo5W4Nhv31Yu1igxTE2x0aeShbKtVcCy
DAA Course: https://www.youtube.com/playlist?list=PLdo5W4Nhv31ZTn2P9vF02bkb3SC8uiUUn
Placement Series: https://www.youtube.c...
published: 12 Feb 2019
Dijkstras Shortest Path Algorithm Explained | With Example | Graph Theory
I explain Dijkstra's Shortest Path Algorithm with the help of an example.
This algorithm can be used to calculate the shortest distance between one node and every other node in a weighted graph.
If you want to learn more about algorithms, check out my Basic Algorithms in Python Playlist and subscribe to this channel.
published: 26 Sep 2020
Dijkstra Algorithm Example
Dijkstra's Algorithm is for finding minimum-weight (shortest) paths between two specified vertices in a graph.
published: 10 Dec 2013
Dijkstra's Shortest Path Algorithm | Graph Theory
Explanation of Dijkstra's shortest path algorithm
Dijkstra source code on Algorithms repository:
https://github.com/williamfiset/algorithms#graph-theory
Video slides:
https://github.com/williamfiset/Algorithms/tree/master/slides
Indexed Priority Queue Video:
https://youtu.be/jND_WJ8r7FE
0:00 Intro
0:28 What is Dijkstra's algorithm?
1:13 Algorithm prerequisites
1:55 Video outline
2:28 Dijkstra's algorithm overview
3:50 Lazy Dijkstra's animation
8:10 Lazy Dijkstra's code
11:33 Ignoring stale node optimization
12:11 Finding the shortest path
14:01 Stopping early optimization
15:11 Eager Dijkstra's with an indexed priority queue
16:27 Eager Dijkstra's animation
19:28 Eager Dijkstra's code
20:31 D-ary heap optimization
23:06 The current state of the art for heaps
==========================...
Step by step instructions showing how to run Dijkstra's algorithm on a graph.
Code: https://github.com/msambol/dsa/blob/master/shortest_path/dijkstras.py (diff...
Step by step instructions showing how to run Dijkstra's algorithm on a graph.
Code: https://github.com/msambol/dsa/blob/master/shortest_path/dijkstras.py (different than video, I added this retroactively)
Source: Algorithms by S. Dasgupta, C. H. Papadimitriou, and U. V. Vazirani [https://www.amazon.com/Algorithms-Sanjoy-Dasgupta/dp/0073523402]
LinkedIn: https://www.linkedin.com/in/michael-sambol
Step by step instructions showing how to run Dijkstra's algorithm on a graph.
Code: https://github.com/msambol/dsa/blob/master/shortest_path/dijkstras.py (different than video, I added this retroactively)
Source: Algorithms by S. Dasgupta, C. H. Papadimitriou, and U. V. Vazirani [https://www.amazon.com/Algorithms-Sanjoy-Dasgupta/dp/0073523402]
LinkedIn: https://www.linkedin.com/in/michael-sambol
This is the fourth in a series of computer science videos about the graph data structure. This is an explanation of Dijkstra’s algorithm for finding the shorte...
This is the fourth in a series of computer science videos about the graph data structure. This is an explanation of Dijkstra’s algorithm for finding the shortest path between one vertex in a graph and another. Indeed, this explains how Dijkstra’s shortest path algorithm generates a set of information that includes the shortest paths from a starting vertex and every other vertex in the graph. It also mentions why Dijkstra’s shortest path algorithm is an example of a greedy algorithm.
This is the fourth in a series of computer science videos about the graph data structure. This is an explanation of Dijkstra’s algorithm for finding the shortest path between one vertex in a graph and another. Indeed, this explains how Dijkstra’s shortest path algorithm generates a set of information that includes the shortest paths from a starting vertex and every other vertex in the graph. It also mentions why Dijkstra’s shortest path algorithm is an example of a greedy algorithm.
Dijkstra Algorithm for Single Source Shortest Path
Procedure
Examples
Time Complexity
Drawbacks
PATREON : https://www.patreon.com/bePatron?u=20475192
Courses ...
Dijkstra Algorithm for Single Source Shortest Path
Procedure
Examples
Time Complexity
Drawbacks
PATREON : https://www.patreon.com/bePatron?u=20475192
Courses on Udemy
================
Java Programming
https://www.udemy.com/course/java-se-programming/?referralCode=C71BADEAA4E7332D62B6
Data Structures using C and C++
https://www.udemy.com/course/datastructurescncpp/?referralCode=BD2EF8E61A98AB5E011D
C++ Programming
https://www.udemy.com/course/cpp-deep-dive/?referralCode=E4246A516919D7E84225
Dijkstra Algorithm for Single Source Shortest Path
Procedure
Examples
Time Complexity
Drawbacks
PATREON : https://www.patreon.com/bePatron?u=20475192
Courses on Udemy
================
Java Programming
https://www.udemy.com/course/java-se-programming/?referralCode=C71BADEAA4E7332D62B6
Data Structures using C and C++
https://www.udemy.com/course/datastructurescncpp/?referralCode=BD2EF8E61A98AB5E011D
C++ Programming
https://www.udemy.com/course/cpp-deep-dive/?referralCode=E4246A516919D7E84225
Dijkstra's Algorithm finds the shortest path between two points. Dr Mike Pound explains how it works.
How Sat Nav Works: https://youtu.be/EUrU1y5is3Y
Slow Lor...
Dijkstra's Algorithm finds the shortest path between two points. Dr Mike Pound explains how it works.
How Sat Nav Works: https://youtu.be/EUrU1y5is3Y
Slow Loris Attack: https://youtu.be/XiFkyR35v2Y
http://www.facebook.com/computerphile
https://twitter.com/computer_phile
This video was filmed and edited by Sean Riley.
Computer Science at the University of Nottingham: http://bit.ly/nottscomputer
Computerphile is a sister project to Brady Haran's Numberphile. More at http://www.bradyharan.com
Dijkstra's Algorithm finds the shortest path between two points. Dr Mike Pound explains how it works.
How Sat Nav Works: https://youtu.be/EUrU1y5is3Y
Slow Loris Attack: https://youtu.be/XiFkyR35v2Y
http://www.facebook.com/computerphile
https://twitter.com/computer_phile
This video was filmed and edited by Sean Riley.
Computer Science at the University of Nottingham: http://bit.ly/nottscomputer
Computerphile is a sister project to Brady Haran's Numberphile. More at http://www.bradyharan.com
Implement Dijkstra's shortest path algorithm yourself here: https://neetcode.io/problems/dijkstra
🚀 https://neetcode.io/ - A better way to prepare for Coding I...
Implement Dijkstra's shortest path algorithm yourself here: https://neetcode.io/problems/dijkstra
🚀 https://neetcode.io/ - A better way to prepare for Coding Interviews
🥷 Discord: https://discord.gg/ddjKRXPqtk
🐦 Twitter: https://twitter.com/neetcode1
🐮 Support the channel: https://www.patreon.com/NEETcode
⭐ BLIND-75 PLAYLIST: https://www.youtube.com/watch?v=KLlXCFG5TnA&list=PLot-Xpze53ldVwtstag2TL4HQhAnC8ATf
💡 DYNAMIC PROGRAMMING PLAYLIST: https://www.youtube.com/watch?v=73r3KWiEvyk&list=PLot-Xpze53lcvx_tjrr_m2lgD2NsRHlNO&index=1
#neetcode #leetcode #python
Dijkstra's Algorithm allows us to find the shortest path between two vertices in a graph. Here, we explore the intuition behind the algorithm — what information...
Dijkstra's Algorithm allows us to find the shortest path between two vertices in a graph. Here, we explore the intuition behind the algorithm — what information we need to keep track of, in what order we need to explore vertices, and what the limitations of the algorithm are.
***
Spanning Tree is an educational video series about computer science and mathematics. See more at https://spanningtree.me
To be notified when a new video is released, sign up for the Spanning Tree mailing list at https://spanningtree.substack.com/
Spanning Tree is created by Brian Yu. https://brianyu.me/
Email me at [email protected] to suggest a future topic.
Dijkstra's Algorithm allows us to find the shortest path between two vertices in a graph. Here, we explore the intuition behind the algorithm — what information we need to keep track of, in what order we need to explore vertices, and what the limitations of the algorithm are.
***
Spanning Tree is an educational video series about computer science and mathematics. See more at https://spanningtree.me
To be notified when a new video is released, sign up for the Spanning Tree mailing list at https://spanningtree.substack.com/
Spanning Tree is created by Brian Yu. https://brianyu.me/
Email me at [email protected] to suggest a future topic.
In this video I have explained Dijkstra's Algorithm with some Examples. It is Single Source Shortest Path Algorithm and use Greedy Method.
DSA Full Course: htt...
In this video I have explained Dijkstra's Algorithm with some Examples. It is Single Source Shortest Path Algorithm and use Greedy Method.
DSA Full Course: https: https://www.youtube.com/playlist?list=PLdo5W4Nhv31bbKJzrsKfMpo_grxuLl8LU
******************************************
More Playlists:
C Programming Course: https://www.youtube.com/playlist?list=PLdo5W4Nhv31a8UcMN9-35ghv8qyFWD9_S
C++ Programming: https://www.youtube.com/playlist?list=PLdo5W4Nhv31YU5Wx1dopka58teWP9aCee
Python Full Course: https://www.youtube.com/playlist?list=PLdo5W4Nhv31bZSiqiOL5ta39vSnBxpOPT
Printing Pattern in C: https://www.youtube.com/playlist?list=PLdo5W4Nhv31Yu1igxTE2x0aeShbKtVcCy
DAA Course: https://www.youtube.com/playlist?list=PLdo5W4Nhv31ZTn2P9vF02bkb3SC8uiUUn
Placement Series: https://www.youtube.com/playlist?list=PLdo5W4Nhv31YvlDpJhvOYbM9Ap8UypgEy
Dynamic Programming: https://www.youtube.com/playlist?list=PLdo5W4Nhv31aBrJE1WS4MR9LRfbmZrAQu
Operating Systems: //www.youtube.com/playlist?list=PLdo5W4Nhv31a5ucW_S1K3-x6ztBRD-PNa
DBMS: https://www.youtube.com/playlist?list=PLdo5W4Nhv31b33kF46f9aFjoJPOkdlsRc
********************************************
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#dijkstra #jennyslectures #datastructures
In this video I have explained Dijkstra's Algorithm with some Examples. It is Single Source Shortest Path Algorithm and use Greedy Method.
DSA Full Course: https: https://www.youtube.com/playlist?list=PLdo5W4Nhv31bbKJzrsKfMpo_grxuLl8LU
******************************************
More Playlists:
C Programming Course: https://www.youtube.com/playlist?list=PLdo5W4Nhv31a8UcMN9-35ghv8qyFWD9_S
C++ Programming: https://www.youtube.com/playlist?list=PLdo5W4Nhv31YU5Wx1dopka58teWP9aCee
Python Full Course: https://www.youtube.com/playlist?list=PLdo5W4Nhv31bZSiqiOL5ta39vSnBxpOPT
Printing Pattern in C: https://www.youtube.com/playlist?list=PLdo5W4Nhv31Yu1igxTE2x0aeShbKtVcCy
DAA Course: https://www.youtube.com/playlist?list=PLdo5W4Nhv31ZTn2P9vF02bkb3SC8uiUUn
Placement Series: https://www.youtube.com/playlist?list=PLdo5W4Nhv31YvlDpJhvOYbM9Ap8UypgEy
Dynamic Programming: https://www.youtube.com/playlist?list=PLdo5W4Nhv31aBrJE1WS4MR9LRfbmZrAQu
Operating Systems: //www.youtube.com/playlist?list=PLdo5W4Nhv31a5ucW_S1K3-x6ztBRD-PNa
DBMS: https://www.youtube.com/playlist?list=PLdo5W4Nhv31b33kF46f9aFjoJPOkdlsRc
********************************************
Connect & Contact Me:
Facebook: https://www.facebook.com/Jennys-Lectures-CSIT-Netjrf-316814368950701/
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#dijkstra #jennyslectures #datastructures
I explain Dijkstra's Shortest Path Algorithm with the help of an example.
This algorithm can be used to calculate the shortest distance between one node and eve...
I explain Dijkstra's Shortest Path Algorithm with the help of an example.
This algorithm can be used to calculate the shortest distance between one node and every other node in a weighted graph.
If you want to learn more about algorithms, check out my Basic Algorithms in Python Playlist and subscribe to this channel.
I explain Dijkstra's Shortest Path Algorithm with the help of an example.
This algorithm can be used to calculate the shortest distance between one node and every other node in a weighted graph.
If you want to learn more about algorithms, check out my Basic Algorithms in Python Playlist and subscribe to this channel.
Explanation of Dijkstra's shortest path algorithm
Dijkstra source code on Algorithms repository:
https://github.com/williamfiset/algorithms#graph-theory
Video...
Explanation of Dijkstra's shortest path algorithm
Dijkstra source code on Algorithms repository:
https://github.com/williamfiset/algorithms#graph-theory
Video slides:
https://github.com/williamfiset/Algorithms/tree/master/slides
Indexed Priority Queue Video:
https://youtu.be/jND_WJ8r7FE
0:00 Intro
0:28 What is Dijkstra's algorithm?
1:13 Algorithm prerequisites
1:55 Video outline
2:28 Dijkstra's algorithm overview
3:50 Lazy Dijkstra's animation
8:10 Lazy Dijkstra's code
11:33 Ignoring stale node optimization
12:11 Finding the shortest path
14:01 Stopping early optimization
15:11 Eager Dijkstra's with an indexed priority queue
16:27 Eager Dijkstra's animation
19:28 Eager Dijkstra's code
20:31 D-ary heap optimization
23:06 The current state of the art for heaps
===================================
Practicing for interviews? I have used, and recommend `Cracking the Coding Interview` which got me a job at Google. Link on Amazon: https://amzn.to/3cvMof5
A lot of the content on this channel is inspired by the book `Competitive Programming` by Steven Halim which I frequently use as a resource and reference. Link on Amazon: https://amzn.to/3wC2nix
Explanation of Dijkstra's shortest path algorithm
Dijkstra source code on Algorithms repository:
https://github.com/williamfiset/algorithms#graph-theory
Video slides:
https://github.com/williamfiset/Algorithms/tree/master/slides
Indexed Priority Queue Video:
https://youtu.be/jND_WJ8r7FE
0:00 Intro
0:28 What is Dijkstra's algorithm?
1:13 Algorithm prerequisites
1:55 Video outline
2:28 Dijkstra's algorithm overview
3:50 Lazy Dijkstra's animation
8:10 Lazy Dijkstra's code
11:33 Ignoring stale node optimization
12:11 Finding the shortest path
14:01 Stopping early optimization
15:11 Eager Dijkstra's with an indexed priority queue
16:27 Eager Dijkstra's animation
19:28 Eager Dijkstra's code
20:31 D-ary heap optimization
23:06 The current state of the art for heaps
===================================
Practicing for interviews? I have used, and recommend `Cracking the Coding Interview` which got me a job at Google. Link on Amazon: https://amzn.to/3cvMof5
A lot of the content on this channel is inspired by the book `Competitive Programming` by Steven Halim which I frequently use as a resource and reference. Link on Amazon: https://amzn.to/3wC2nix
Step by step instructions showing how to run Dijkstra's algorithm on a graph.
Code: https://github.com/msambol/dsa/blob/master/shortest_path/dijkstras.py (different than video, I added this retroactively)
Source: Algorithms by S. Dasgupta, C. H. Papadimitriou, and U. V. Vazirani [https://www.amazon.com/Algorithms-Sanjoy-Dasgupta/dp/0073523402]
LinkedIn: https://www.linkedin.com/in/michael-sambol
This is the fourth in a series of computer science videos about the graph data structure. This is an explanation of Dijkstra’s algorithm for finding the shortest path between one vertex in a graph and another. Indeed, this explains how Dijkstra’s shortest path algorithm generates a set of information that includes the shortest paths from a starting vertex and every other vertex in the graph. It also mentions why Dijkstra’s shortest path algorithm is an example of a greedy algorithm.
Dijkstra Algorithm for Single Source Shortest Path
Procedure
Examples
Time Complexity
Drawbacks
PATREON : https://www.patreon.com/bePatron?u=20475192
Courses on Udemy
================
Java Programming
https://www.udemy.com/course/java-se-programming/?referralCode=C71BADEAA4E7332D62B6
Data Structures using C and C++
https://www.udemy.com/course/datastructurescncpp/?referralCode=BD2EF8E61A98AB5E011D
C++ Programming
https://www.udemy.com/course/cpp-deep-dive/?referralCode=E4246A516919D7E84225
Dijkstra's Algorithm finds the shortest path between two points. Dr Mike Pound explains how it works.
How Sat Nav Works: https://youtu.be/EUrU1y5is3Y
Slow Loris Attack: https://youtu.be/XiFkyR35v2Y
http://www.facebook.com/computerphile
https://twitter.com/computer_phile
This video was filmed and edited by Sean Riley.
Computer Science at the University of Nottingham: http://bit.ly/nottscomputer
Computerphile is a sister project to Brady Haran's Numberphile. More at http://www.bradyharan.com
Dijkstra's Algorithm allows us to find the shortest path between two vertices in a graph. Here, we explore the intuition behind the algorithm — what information we need to keep track of, in what order we need to explore vertices, and what the limitations of the algorithm are.
***
Spanning Tree is an educational video series about computer science and mathematics. See more at https://spanningtree.me
To be notified when a new video is released, sign up for the Spanning Tree mailing list at https://spanningtree.substack.com/
Spanning Tree is created by Brian Yu. https://brianyu.me/
Email me at [email protected] to suggest a future topic.
In this video I have explained Dijkstra's Algorithm with some Examples. It is Single Source Shortest Path Algorithm and use Greedy Method.
DSA Full Course: https: https://www.youtube.com/playlist?list=PLdo5W4Nhv31bbKJzrsKfMpo_grxuLl8LU
******************************************
More Playlists:
C Programming Course: https://www.youtube.com/playlist?list=PLdo5W4Nhv31a8UcMN9-35ghv8qyFWD9_S
C++ Programming: https://www.youtube.com/playlist?list=PLdo5W4Nhv31YU5Wx1dopka58teWP9aCee
Python Full Course: https://www.youtube.com/playlist?list=PLdo5W4Nhv31bZSiqiOL5ta39vSnBxpOPT
Printing Pattern in C: https://www.youtube.com/playlist?list=PLdo5W4Nhv31Yu1igxTE2x0aeShbKtVcCy
DAA Course: https://www.youtube.com/playlist?list=PLdo5W4Nhv31ZTn2P9vF02bkb3SC8uiUUn
Placement Series: https://www.youtube.com/playlist?list=PLdo5W4Nhv31YvlDpJhvOYbM9Ap8UypgEy
Dynamic Programming: https://www.youtube.com/playlist?list=PLdo5W4Nhv31aBrJE1WS4MR9LRfbmZrAQu
Operating Systems: //www.youtube.com/playlist?list=PLdo5W4Nhv31a5ucW_S1K3-x6ztBRD-PNa
DBMS: https://www.youtube.com/playlist?list=PLdo5W4Nhv31b33kF46f9aFjoJPOkdlsRc
********************************************
Connect & Contact Me:
Facebook: https://www.facebook.com/Jennys-Lectures-CSIT-Netjrf-316814368950701/
Quora: https://www.quora.com/profile/Jayanti-Khatri-Lamba
Instagram: https://www.instagram.com/jayantikhatrilamba/
#dijkstra #jennyslectures #datastructures
I explain Dijkstra's Shortest Path Algorithm with the help of an example.
This algorithm can be used to calculate the shortest distance between one node and every other node in a weighted graph.
If you want to learn more about algorithms, check out my Basic Algorithms in Python Playlist and subscribe to this channel.
Explanation of Dijkstra's shortest path algorithm
Dijkstra source code on Algorithms repository:
https://github.com/williamfiset/algorithms#graph-theory
Video slides:
https://github.com/williamfiset/Algorithms/tree/master/slides
Indexed Priority Queue Video:
https://youtu.be/jND_WJ8r7FE
0:00 Intro
0:28 What is Dijkstra's algorithm?
1:13 Algorithm prerequisites
1:55 Video outline
2:28 Dijkstra's algorithm overview
3:50 Lazy Dijkstra's animation
8:10 Lazy Dijkstra's code
11:33 Ignoring stale node optimization
12:11 Finding the shortest path
14:01 Stopping early optimization
15:11 Eager Dijkstra's with an indexed priority queue
16:27 Eager Dijkstra's animation
19:28 Eager Dijkstra's code
20:31 D-ary heap optimization
23:06 The current state of the art for heaps
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In graph theory, the shortest path problem is the problem of finding a path between two vertices (or nodes) in a graph such that the sum of the weights of its constituent edges is minimized.
The problem of finding the shortest path between two intersections on a road map (the graph's vertices correspond to intersections and the edges correspond to road segments, each weighted by the length of its road segment) may be modeled by a special case of the shortest path problem in graphs.
Definition
The shortest path problem can be defined for graphs whether undirected, directed, or mixed.
It is defined here for undirected graphs; for directed graphs the definition of path
requires that consecutive vertices be connected by an appropriate directed edge.
Two vertices are adjacent when they are both incident to a common edge.
A path in an undirected graph is a sequence of vertices
such that is adjacent to for .
Such a path is called a path of length
from to .
(The are variables; their numbering here relates to their position in the sequence and needs not to relate to any canonical labeling of the vertices.)