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This approach involves using a BFS algorithm to find the shortest path between the starting point and each tree in increasing order of their heights. We first list all trees with their positions and heights, sort them, and for each sorted tree, compute the shortest path using BFS. If any tree is unreachable, we return -1.
The time complexity of this approach is O(T * m * n) where T is the number of trees. This is because we perform BFS from the starting position to each tree. The space complexity is O(m * n) due to the storage of the queue and visited matrix in BFS.
1var cutOffTree = function(forest) {
2 const directions = [[1, 0], [-1, 0], [0, 1], [0, -1]];
3 const bfs = (sr, sc, tr, tc) => {
4 if (sr === tr && sc === tc) return 0;
5 const queue = [[sr, sc, 0]];
6 const visited = new Set([`${sr},${sc}`]);
7 while (queue.length) {
8 const [r, c, steps] = queue.shift();
9 for (const [dr, dc] of directions) {
10 const nr = r + dr, nc = c + dc;
11 if (nr >= 0 && nr < forest.length && nc >= 0 && nc < forest[0].length && !visited.has(`${nr},${nc}`) && forest[nr][nc] > 0) {
12 if (nr === tr && nc === tc) return steps + 1;
13 queue.push([nr, nc, steps + 1]);
14 visited.add(`${nr},${nc}`);
15 }
16 }
17 }
18 return -1;
19 };
20
21 const trees = [];
22 for (let r = 0; r < forest.length; r++) {
23 for (let c = 0; c < forest[0].length; c++) {
24 if (forest[r][c] > 1) trees.push([forest[r][c], r, c]);
25 }
26 }
27 trees.sort((a, b) => a[0] - b[0]);
28
29 let totalSteps = 0;
30 let sr = 0, sc = 0;
31 for (const [height, tr, tc] of trees) {
32 const steps = bfs(sr, sc, tr, tc);
33 if (steps === -1) return -1;
34 totalSteps += steps;
35 sr = tr;
36 sc = tc;
37 }
38 return totalSteps;
39};The JavaScript solution sorts trees by their height and iteratively applies BFS from one tree to the next in order. It maintains current reachable positions using a queue, extending it with viable moves until the goal is reached or determined unreachable.
Using Bidirectional BFS can help in optimizing the pathfinding step by searching from both the start and the goal concurrently. The aim is to decrease the search space substantially as the two searches meet in the middle. This method is an extension to the regular BFS approach, potentially optimizing the traversal cost considerably under specific configurations.
The potential benefits of bidirectional search in space complexity can be realized when effectively reducing the number of explored nodes, as time complexity ideally shortens from O(b^d) to roughly O(b^(d/2)), where b is the branching factor and d the solution depth.
1Implementing a complete bidirectional BFS in C is complex and often involves intricate management of forward and backward search queues and visited states. This might require substantial utility function support A detailed explanation of handling bidirectional BFS in C would involve structuring two queues for the forward and backward search, ensuring efficient state management overlaps and transitions by interfacing node positions from both directions dynamically, essentially shortening the BFS travel path.