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

Distance of Nearest Cell
Having 1 in a Binary Grid



Problem Statement

Given a binary grid of size N x M, where each cell contains either a 0 or a 1, compute a matrix where each cell contains the distance to the nearest cell that has a value of 1.

Distance is measured in terms of the number of steps required to move from one cell to another in the four cardinal directions (up, down, left, right).

Examples

Input Grid Output Grid Description
[[0,0,0],[0,1,0],[0,0,0]] [[2,1,2],[1,0,1],[2,1,2]] Center 1 propagates distance outwards
[[1,0,0],[0,0,0],[0,0,0]] [[0,1,2],[1,2,3],[2,3,4]] Only top-left is 1, all others compute from it
[[0,0],[0,0]] [[Infinity,Infinity],[Infinity,Infinity]] No 1 present; can't compute distance
[[1,1],[1,1]] [[0,0],[0,0]] All cells are already 1
[[0]] [[Infinity]] Single cell 0, no 1 exists
[[1]] [[0]] Single cell 1, distance is 0

Solution

Understanding the Problem

You are given a binary grid (a 2D matrix of 0s and 1s). For each cell, you must find the distance to the nearest cell that has the value 1. The distance is calculated in terms of steps to adjacent (top, bottom, left, or right) cells — diagonals are not allowed.

Case 1: All Cells are 1

If all the cells in the grid contain 1, then the distance for every cell is 0 — it is already a 1, so the nearest 1 is itself.

Case 2: No Cell Contains 1

If there are no 1s in the grid, it's impossible to find a distance to a 1. In practice, such a case might be handled by returning -1 or leaving the distance matrix filled with a sentinel value (like Infinity).

Case 3: Mixed 0s and 1s

This is the most typical scenario. To find the minimum distance efficiently for each cell, we perform a multi-source BFS starting from all the cells that already contain a 1. Initially, all 1s are added to a queue with a distance of 0. As BFS proceeds, we update the distance of each 0-cell as it is reached for the first time by expanding from neighboring 1s.

Why Breadth-First Search?

BFS ensures that we reach each cell using the shortest path from the nearest 1. By exploring all directions level by level, the first time we visit a cell will be through the shortest possible path.

Result

The final result is a grid of the same size, where each cell contains the shortest number of steps to reach the nearest cell with a 1.

Algorithm Steps

  1. Initialize a distance matrix with large values.
  2. Create a queue and enqueue all cells with 1 (distance = 0).
  3. While the queue is not empty:
    1. Dequeue the front cell (i, j).
    2. For each neighbor in 4 directions:
      1. If neighbor is within bounds and has not been visited (or a shorter path is found),
      2. Update its distance as distance[i][j] + 1 and enqueue it.
  4. Return the final distance matrix.

Code

JavaScript
function nearest1Distance(grid) {
  const rows = grid.length, cols = grid[0].length;
  const distance = Array.from({ length: rows }, () => Array(cols).fill(Infinity));
  const queue = [];
  const directions = [[1,0], [-1,0], [0,1], [0,-1]];

  for (let i = 0; i < rows; i++) {
    for (let j = 0; j < cols; j++) {
      if (grid[i][j] === 1) {
        distance[i][j] = 0;
        queue.push([i, j]);
      }
    }
  }

  while (queue.length > 0) {
    const [x, y] = queue.shift();
    for (const [dx, dy] of directions) {
      const nx = x + dx, ny = y + dy;
      if (nx >= 0 && ny >= 0 && nx < rows && ny < cols && distance[nx][ny] > distance[x][y] + 1) {
        distance[nx][ny] = distance[x][y] + 1;
        queue.push([nx, ny]);
      }
    }
  }

  return distance;
}

const grid = [
  [0, 0, 0],
  [0, 1, 0],
  [0, 0, 0]
];

console.log("Distance of nearest 1 for each cell:", nearest1Distance(grid));

Time Complexity

CaseTime ComplexityExplanation
Best CaseO(m × n)Even in the best case where all cells are 1, we still traverse all cells to build the output matrix.
Average CaseO(m × n)Each cell is visited at most once during the BFS, and every cell is processed to compute its minimum distance.
Worst CaseO(m × n)In the worst case, we must traverse every cell and explore its neighbors, which is linear with respect to the number of cells in the grid.

Space Complexity

O(m × n)

Explanation: We use a queue for BFS and a distance matrix of the same size as the grid, leading to linear space usage.



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