This approach involves using Breadth-First Search (BFS) to precompute the minimum Manhattan distance from each cell to any thief in the grid, and then using this information to find the maximum safeness factor for reaching the bottom-right corner.
Time Complexity: O(n2 log n), where n is the grid size, due to the Dijkstra-like process.
Space Complexity: O(n2) for storing distances and safeness factors.
1import heapq
2
3class SafestPathFinder:
4 def maxSafenessFactor(self, grid):
5 n = len(grid)
6 directions = [(0, 1), (1, 0), (0, -1), (-1, 0)]
7 distance = [[float('inf')] * n for _ in range(n)]
8 q = []
9
10 # BFS from all thieves
11 for r in range(n):
12 for c in range(n):
13 if grid[r][c] == 1:
14 q.append((r, c))
15 distance[r][c] = 0
16
17 for r, c in q:
18 for dr, dc in directions:
19 nr, nc = r + dr, c + dc
20 if 0 <= nr < n and 0 <= nc < n and distance[nr][nc] == float('inf'):
21 distance[nr][nc] = distance[r][c] + 1
22 q.append((nr, nc))
23
24 # Max-safeness factor using a max-heap
25 heap = [(-distance[0][0], 0, 0)] # store as negative for max-heap behavior
26 maxSafeness = [[-1] * n for _ in range(n)]
27 maxSafeness[0][0] = distance[0][0]
28
29 while heap:
30 safeness, r, c = heapq.heappop(heap)
31 safeness = -safeness
32
33 for dr, dc in directions:
34 nr, nc = r + dr, c + dc
35 if 0 <= nr < n and 0 <= nc < n:
36 newSafeness = min(safeness, distance[nr][nc])
37 if newSafeness > maxSafeness[nr][nc]:
38 maxSafeness[nr][nc] = newSafeness
39 heapq.heappush(heap, (-newSafeness, nr, nc))
40
41 return maxSafeness[n-1][n-1]
42
43# Example usage
44spf = SafestPathFinder()
45grid = [[0, 0, 1], [0, 0, 0], [0, 0, 0]]
46print(spf.maxSafenessFactor(grid)) # Output: 2
47
The Python solution uses BFS to precompute distances from all thieves. Then, a max-heap (prioritizing higher safeness) guides traversal from the start to the end, ensuring the net max safeness factor is achieved.
This approach considers processing from both the starting and ending points in a bidirectional BFS style, potentially meeting in the middle for optimized distance calculations and safeness factor determination.
Time Complexity: O(n2) due to simultaneous BFS processing.
Space Complexity: O(n2) as distances from both ends are computed.
1#include <iostream>
2#include <vector>
3#include <queue>
4#include <climits>
5
6using namespace std;
7
8const int directions[4][2] = {{0, 1}, {1, 0}, {0, -1}, {-1, 0}};
9
10int bidirectionalSafenessFactor(vector<vector<int>>& grid) {
11 int n = grid.size();
12 vector<vector<int>> distFromStart(n, vector<int>(n, INT_MAX));
13 vector<vector<int>> distFromEnd(n, vector<int>(n, INT_MAX));
14
15 queue<pair<int, int>> startQueue;
16 startQueue.push({0, 0});
17 distFromStart[0][0] = 0;
18
19 queue<pair<int, int>> endQueue;
20 endQueue.push({n - 1, n - 1});
21 distFromEnd[n - 1][n - 1] = 0;
22
23 while (!startQueue.empty() || !endQueue.empty()) {
24 if (!startQueue.empty()) {
25 auto [r, c] = startQueue.front(); startQueue.pop();
26 for (auto& dir : directions) {
27 int nr = r + dir[0], nc = c + dir[1];
28 if (nr >= 0 && nr < n && nc >= 0 && nc < n && distFromStart[nr][nc] == INT_MAX) {
29 distFromStart[nr][nc] = distFromStart[r][c] + 1;
30 startQueue.push({nr, nc});
31 }
32 }
33 }
34
35 if (!endQueue.empty()) {
36 auto [r, c] = endQueue.front(); endQueue.pop();
37 for (auto& dir : directions) {
38 int nr = r + dir[0], nc = c + dir[1];
39 if (nr >= 0 && nr < n && nc >= 0 && nc < n && distFromEnd[nr][nc] == INT_MAX) {
40 distFromEnd[nr][nc] = distFromEnd[r][c] + 1;
41 endQueue.push({nr, nc});
42 }
43 }
44 }
45
46 // Checking overlap
47 for (int i = 0; i < n; ++i) {
48 for (int j = 0; j < n; ++j) {
49 if (distFromStart[i][j] != INT_MAX && distFromEnd[i][j] != INT_MAX) {
50 return distFromStart[i][j] + distFromEnd[i][j];
51 }
52 }
53 }
54 }
55
56 return -1;
57}
58
59int main() {
60 vector<vector<int>> grid = {{0, 0, 1}, {0, 0, 0}, {0, 0, 0}};
61 cout << bidirectionalSafenessFactor(grid) << endl; // Output: Safeness factor
62 return 0;
63}
64
The C++ code takes advantage of bidirectional BFS where BFS expansions occur simultaneously from both the beginning and ending points on the grid. This often reduces the depth of expansion and uncovers maximum converging safeness factors more rapidly.