We are given an 0s and 1s. Each cell in the grid represents either land or water. A cell with a value of 1 represents land, while a cell with a value of 0 represents water. A group of adjacent cells with a value of 1 constitutes an island. Two cells are considered adjacent if one is above, below, to the left, or to the right of the other. Our task is to return the smallest number of 0s we must flip to connect the two islands.
Note: We may assume all four edges of the grid are surrounded by water.
Constraints:
n
grid.length grid[i].length.
grid[i][j] is either 0 or 1.
There are exactly two islands in the grid.
The solution for finding the shortest bridge between two islands in a grid involves a two-step process using Breadth-First Search (BFS) and queues. First, we locate and mark one of the islands by performing BFS. During this step, we use a queue to explore and mark all the cells of the first island to keep track of the entire island as the starting point for the next phase.
In the second phase, we expand outward from the marked island using another BFS. We initialize a new queue with the coordinates of the first island’s boundary cells. As we explore each cell, we mark it as visited to avoid revisiting. The BFS expands layer by layer, crossing the water (0s) and keeping track of the number of layers (or distance) crossed. The first time ...
We are given an 0s and 1s. Each cell in the grid represents either land or water. A cell with a value of 1 represents land, while a cell with a value of 0 represents water. A group of adjacent cells with a value of 1 constitutes an island. Two cells are considered adjacent if one is above, below, to the left, or to the right of the other. Our task is to return the smallest number of 0s we must flip to connect the two islands.
Note: We may assume all four edges of the grid are surrounded by water.
Constraints:
n
grid.length grid[i].length.
grid[i][j] is either 0 or 1.
There are exactly two islands in the grid.
The solution for finding the shortest bridge between two islands in a grid involves a two-step process using Breadth-First Search (BFS) and queues. First, we locate and mark one of the islands by performing BFS. During this step, we use a queue to explore and mark all the cells of the first island to keep track of the entire island as the starting point for the next phase.
In the second phase, we expand outward from the marked island using another BFS. We initialize a new queue with the coordinates of the first island’s boundary cells. As we explore each cell, we mark it as visited to avoid revisiting. The BFS expands layer by layer, crossing the water (0s) and keeping track of the number of layers (or distance) crossed. The first time ...