Find the City With the Smallest Number of Neighbors at a Threshold Distance | 3 Ways | Leetcode 1334
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This is the 53rd Video of our Playlist "Graphs : Popular Interview Problems" by codestorywithMIK
Dijkstra's - • Dijkstra's Algorithm | PART-1 | (Micr...
Bellman-Ford - • Bellman-Ford Algorithm | Full Detail ...
Floyd Warshall - • Floyd Warshall Algorithm | Full Detai...
In this video we will try to solve a very good Graph Problem : Find the City With the Smallest Number of Neighbors at a Threshold Distance | 3 Approaches | Leetcode 1334 | codestorywithMIK
I will explain the intuition so easily that you will never forget and start seeing this as cakewalk EASYYY.
We will do live coding after explanation and see if we are able to pass all the test cases.
Also, please note that my Github solution link below contains both C++ as well as JAVA code.
Problem Name : Find the City With the Smallest Number of Neighbors at a Threshold Distance | 3 Approaches | Leetcode 1334 | codestorywithMIK
Company Tags : Amazon, Microsoft
My solutions on Github(C++ & JAVA) -
Dijkstra's - https://github.com/MAZHARMIK/Intervie...
Bellman-Ford - https://github.com/MAZHARMIK/Intervie...
Floyd Warshall - https://github.com/MAZHARMIK/Intervie...
Leetcode Link : https://leetcode.com/problems/find-th...
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My Graph Concepts Playlist : • Graph Concepts & Qns - 1 : Graph will...
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My GitHub Repo for interview preparation : https://github.com/MAZHARMIK/Intervie...
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Summary :
Dijkstra's Algorithm
Purpose: To find the shortest paths from a source city to all other cities.
Implementation:
Uses a priority queue to process nodes in order of increasing distance.
Initializes the distances to all cities as infinity, except for the source city (distance 0).
Iteratively updates the shortest paths by exploring adjacent nodes.
Usage in the Solution: Computes shortest paths from each city to every other city, fills the shortest path matrix, and determines the city with the fewest reachable cities within a given distance threshold.
Bellman-Ford Algorithm
Purpose: To find the shortest paths from a source city to all other cities, particularly useful for graphs with negative weights (though not needed here).
Implementation:
Initializes distances to all cities as infinity, except for the source city (distance 0).
Relaxes edges repeatedly (n-1 times) to ensure shortest paths are found.
Ensures bi-directional edges are processed for undirected graphs.
Usage in the Solution: Computes shortest paths from each city to every other city, fills the shortest path matrix, and determines the city with the fewest reachable cities within a given distance threshold.
Floyd-Warshall Algorithm
Purpose: To find the shortest paths between all pairs of cities.
Implementation:
Initializes a distance matrix with direct edge weights and sets the distance to itself as zero.
Uses three nested loops to update the shortest paths, considering each node as an intermediate point.
Continuously updates the matrix to ensure it contains the shortest paths between all pairs of nodes.
Usage in the Solution: Computes shortest paths between all pairs of cities, fills the shortest path matrix, and determines the city with the fewest reachable cities within a given distance threshold.
Common Steps
✨ Timelines✨
00:00 - Introduction
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