Leetcode #2812: Find the Safest Path in a Grid
In this guide, we solve Leetcode #2812 Find the Safest Path in a Grid in Python and focus on the core idea that makes the solution efficient.
You will see the intuition, the step-by-step method, and a clean Python implementation you can use in interviews.
Problem Statement
You are given a 0-indexed 2D matrix grid of size n x n, where (r, c) represents: A cell containing a thief if grid[r][c] = 1 An empty cell if grid[r][c] = 0 You are initially positioned at cell (0, 0). In one move, you can move to any adjacent cell in the grid, including cells containing thieves.
Quick Facts
- Difficulty: Medium
- Premium: No
- Tags: Breadth-First Search, Union Find, Array, Binary Search, Matrix, Heap (Priority Queue)
Intuition
The problem structure suggests a monotonic decision, which makes binary search a natural fit.
By halving the search space each step, we reach the answer efficiently.
Approach
Search either directly on a sorted array or on the answer space using a check function.
Each check is fast, and the logarithmic search keeps the overall runtime low.
Steps:
- Define the search bounds.
- Check the mid point condition.
- Narrow the bounds until convergence.
Example
Input: grid = [[1,0,0],[0,0,0],[0,0,1]]
Output: 0
Explanation: All paths from (0, 0) to (n - 1, n - 1) go through the thieves in cells (0, 0) and (n - 1, n - 1).
Python Solution
class UnionFind:
def __init__(self, n):
self.p = list(range(n))
self.size = [1] * n
def find(self, x):
if self.p[x] != x:
self.p[x] = self.find(self.p[x])
return self.p[x]
def union(self, a, b):
pa, pb = self.find(a), self.find(b)
if pa == pb:
return False
if self.size[pa] > self.size[pb]:
self.p[pb] = pa
self.size[pa] += self.size[pb]
else:
self.p[pa] = pb
self.size[pb] += self.size[pa]
return True
class Solution:
def maximumSafenessFactor(self, grid: List[List[int]]) -> int:
n = len(grid)
if grid[0][0] or grid[n - 1][n - 1]:
return 0
q = deque()
dist = [[inf] * n for _ in range(n)]
for i in range(n):
for j in range(n):
if grid[i][j]:
q.append((i, j))
dist[i][j] = 0
dirs = (-1, 0, 1, 0, -1)
while q:
i, j = q.popleft()
for a, b in pairwise(dirs):
x, y = i + a, j + b
if 0 <= x < n and 0 <= y < n and dist[x][y] == inf:
dist[x][y] = dist[i][j] + 1
q.append((x, y))
q = ((dist[i][j], i, j) for i in range(n) for j in range(n))
q = sorted(q, reverse=True)
uf = UnionFind(n * n)
for d, i, j in q:
for a, b in pairwise(dirs):
x, y = i + a, j + b
if 0 <= x < n and 0 <= y < n and dist[x][y] >= d:
uf.union(i * n + j, x * n + y)
if uf.find(0) == uf.find(n * n - 1):
return int(d)
return 0
Complexity
The time complexity is , and the space complexity . The space complexity is O(1).
Edge Cases and Pitfalls
Watch for boundary values, empty inputs, and duplicate values where applicable. If the problem involves ordering or constraints, confirm the invariant is preserved at every step.
Summary
This Python solution focuses on the essential structure of the problem and keeps the implementation interview-friendly while meeting the constraints.