Yandex

Binary TreesBinary Trees36

  1. 1Preorder Traversal of a Binary Tree using Recursion
  2. 2Preorder Traversal of a Binary Tree using Iteration
  3. 3Postorder Traversal of a Binary Tree Using Recursion
  4. 4Postorder Traversal of a Binary Tree using Iteration
  5. 5Level Order Traversal of a Binary Tree using Recursion
  6. 6Level Order Traversal of a Binary Tree using Iteration
  7. 7Reverse Level Order Traversal of a Binary Tree using Iteration
  8. 8Reverse Level Order Traversal of a Binary Tree using Recursion
  9. 9Find Height of a Binary Tree
  10. 10Find Diameter of a Binary Tree
  11. 11Find Mirror of a Binary Tree - Todo
  12. 12Inorder Traversal of a Binary Tree using Recursion
  13. 13Inorder Traversal of a Binary Tree using Iteration
  14. 14Left View of a Binary Tree
  15. 15Right View of a Binary Tree
  16. 16Top View of a Binary Tree
  17. 17Bottom View of a Binary Tree
  18. 18Zigzag Traversal of a Binary Tree
  19. 19Check if a Binary Tree is Balanced
  20. 20Diagonal Traversal of a Binary Tree
  21. 21Boundary Traversal of a Binary Tree
  22. 22Construct a Binary Tree from a String with Bracket Representation
  23. 23Convert a Binary Tree into a Doubly Linked List
  24. 24Convert a Binary Tree into a Sum Tree
  25. 25Find Minimum Swaps Required to Convert a Binary Tree into a BST
  26. 26Check if a Binary Tree is a Sum Tree
  27. 27Check if All Leaf Nodes are at the Same Level in a Binary Tree
  28. 28Lowest Common Ancestor (LCA) in a Binary Tree
  29. 29Solve the Tree Isomorphism Problem
  30. 30Check if a Binary Tree Contains Duplicate Subtrees of Size 2 or More
  31. 31Check if Two Binary Trees are Mirror Images
  32. 32Calculate the Sum of Nodes on the Longest Path from Root to Leaf in a Binary Tree
  33. 33Print All Paths in a Binary Tree with a Given Sum
  34. 34Find the Distance Between Two Nodes in a Binary Tree
  35. 35Find the kth Ancestor of a Node in a Binary Tree
  36. 36Find All Duplicate Subtrees in a Binary Tree

GraphsGraphs46

  1. 1Breadth-First Search in Graphs
  2. 2Depth-First Search in Graphs
  3. 3Number of Provinces in an Undirected Graph
  4. 4Connected Components in a Matrix
  5. 5Rotten Oranges Problem - BFS in Matrix
  6. 6Flood Fill Algorithm - Graph Based
  7. 7Detect Cycle in an Undirected Graph using DFS
  8. 8Detect Cycle in an Undirected Graph using BFS
  9. 9Distance of Nearest Cell Having 1 - Grid BFS
  10. 10Surrounded Regions in Matrix using Graph Traversal
  11. 11Number of Enclaves in Grid
  12. 12Word Ladder - Shortest Transformation using Graph
  13. 13Word Ladder II - All Shortest Transformation Sequences
  14. 14Number of Distinct Islands using DFS
  15. 15Check if a Graph is Bipartite using DFS
  16. 16Topological Sort Using DFS
  17. 17Topological Sort using Kahn's Algorithm
  18. 18Cycle Detection in Directed Graph using BFS
  19. 19Course Schedule - Task Ordering with Prerequisites
  20. 20Course Schedule 2 - Task Ordering Using Topological Sort
  21. 21Find Eventual Safe States in a Directed Graph
  22. 22Alien Dictionary Character Order
  23. 23Shortest Path in Undirected Graph with Unit Distance
  24. 24Shortest Path in DAG using Topological Sort
  25. 25Dijkstra's Algorithm Using Set - Shortest Path in Graph
  26. 26Dijkstra’s Algorithm Using Priority Queue
  27. 27Shortest Distance in a Binary Maze using BFS
  28. 28Path With Minimum Effort in Grid using Graphs
  29. 29Cheapest Flights Within K Stops - Graph Problem
  30. 30Number of Ways to Reach Destination in Shortest Time - Graph Problem
  31. 31Minimum Multiplications to Reach End - Graph BFS
  32. 32Bellman-Ford Algorithm for Shortest Paths
  33. 33Floyd Warshall Algorithm for All-Pairs Shortest Path
  34. 34Find the City With the Fewest Reachable Neighbours
  35. 35Minimum Spanning Tree in Graphs
  36. 36Prim's Algorithm for Minimum Spanning Tree
  37. 37Disjoint Set (Union-Find) with Union by Rank and Path Compression
  38. 38Kruskal's Algorithm - Minimum Spanning Tree
  39. 39Minimum Operations to Make Network Connected
  40. 40Most Stones Removed with Same Row or Column
  41. 41Accounts Merge Problem using Disjoint Set Union
  42. 42Number of Islands II - Online Queries using DSU
  43. 43Making a Large Island Using DSU
  44. 44Bridges in Graph using Tarjan's Algorithm
  45. 45Articulation Points in Graphs
  46. 46Strongly Connected Components using Kosaraju's Algorithm

TriesTries1

Dijkstra’s Algorithm
Using Set in Graphs



Problem Statement

You are given a weighted, undirected, and connected graph with V vertices. The graph is represented as an adjacency list adj where adj[i] is a list of lists. Each list contains two integers: the first is a connected vertex j and the second is the weight of the edge from i to j.

Given a source vertex S, the task is to find the shortest distance from the source to every other vertex. The output should be a list of integers denoting these shortest distances.

Examples

Vertices (V) Adjacency List Source (S) Output Description
3 [[[1,1],[2,4]], [[0,1],[2,2]], [[0,4],[1,2]]] 0 [0,1,3] Shortest paths from vertex 0 to 1 and 2
4 [[[1,5],[2,1]], [[0,5],[3,1]], [[0,1],[3,2]], [[1,1],[2,2]]] 0 [0,4,1,3] Path from 0→2→3 is cheaper than 0→1→3
1 [[]] 0 [0] Single node, distance to itself is 0
2 [[[1,10]], [[0,10]]] 1 [10,0] Bidirectional edge with weight 10

Solution

Dijkstra's Algorithm is used to find the shortest path from a source vertex to all other vertices in a weighted graph with non-negative weights. When implemented with a set, we can efficiently keep track of the minimum weight edge at each step.

The idea is to use a set to store pairs of (distance, vertex). At each step, we extract the node with the smallest tentative distance, then update the distances of its neighbors.

  • Initialize a distance array with infinity for all vertices, except the source which is 0.
  • Insert (0, source) into the set.
  • While the set is not empty:
    • Extract the vertex with the smallest distance.
    • For each neighbor, calculate the potential new distance.
    • If this distance is smaller, update the distance and insert into the set.

This ensures that the shortest paths are computed efficiently, without revisiting already processed vertices unnecessarily.

Algorithm Steps

  1. Create a distance array dist of size V, initialized to infinity. Set dist[S] = 0.
  2. Create a set st and insert the pair (0, S).
  3. While st is not empty:
    1. Extract the pair (distance, node) with the smallest distance.
    2. For each neighbor of node in adj[node]:
      1. If dist[node] + edge_weight < dist[neighbor], update dist[neighbor] and insert (dist[neighbor], neighbor) into the set.
  4. Return the dist array.

Code

JavaScript
function dijkstra(V, adj, S) {
  const dist = new Array(V).fill(Infinity);
  dist[S] = 0;

  const set = new Set();
  set.add([0, S]);

  const compare = (a, b) => a[0] - b[0];

  while (set.size > 0) {
    let [d, node] = [...set].sort(compare)[0];
    set.delete([d, node]);

    for (const [neighbor, weight] of adj[node]) {
      if (d + weight < dist[neighbor]) {
        set.add([dist[neighbor], neighbor]); // Remove old
        dist[neighbor] = d + weight;
        set.add([dist[neighbor], neighbor]);
      }
    }
  }

  return dist;
}

const adj = [
  [[1, 1], [2, 4]],
  [[0, 1], [2, 2]],
  [[0, 4], [1, 2]]
];

console.log("Shortest distances:", dijkstra(3, adj, 0)); // Output: [0, 1, 3]


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