675. Cut Off Trees for Golf Event
Problem Description
In this problem, we are given a matrix representing a forest where each cell can either be empty, blocked, or contain a tree with a certain height. The value '0' indicates a cell that cannot be walked through, the value '1' represents an empty cell that can be walked through, and any value above '1' is a tree with its height represented by that number. We can walk through the cells with trees, and upon doing so, we can choose to cut those trees down.
The primary objective is to cut all the trees in ascending order of their height and track the number of steps it would take to do so. We start from the top-left corner of the matrix (coordinates (0, 0)) and can move in the four cardinal directions: north, east, south, or west. Whenever a tree is cut down, its cell turns into an empty cell ('1'). If it is impossible to cut down all the trees, we have to return '-1'. Our goal is to determine the minimum number of steps required to achieve this task, given that we can only walk through non-zero cells and must cut down trees in order from the shortest to the tallest.
Flowchart Walkthrough
Let's deduce the appropriate algorithm using the Flowchart for LeetCode problem 675, "Cut Off Trees for Golf Event". Here's a step-by-step analysis:
Is it a graph?
- Yes: The problem presents a 2D grid where each cell can be considered a node, and movements between cells are edges.
Is it a tree?
- No: The grid is not a tree as it can have multiple routes and connections between nodes and does not have a hierarchical structure.
Is the problem related to directed acyclic graphs (DAGs)?
- No: This scenario does not involve any directed acyclic graph structures since the problem deals with undirected movements across the grid.
Is the problem related to shortest paths?
- Yes: The core challenge involves finding the shortest way to cut all trees in increasing order of their height, which is essentially finding the shortest path multiple times in a grid.
Is the graph weighted?
- Yes: Although not explicitly stated with different numerical weights, the problem's nature implies a weighted treatment since certain paths (cells) cannot be directly moved through (e.g., cells with tree heights at zero).
Using the algorithm suggested by the flowchart:
- Upon recognizing a shortest path problem in a weighted graph, Dijkstra's algorithm would generally be suggested. However, given that each movement in the grid can be thought of as having the same 'weight' or cost (1 move per step), we effectively treat the graph as having uniform weights, which can simplify to using Breadth-First Search (BFS) for practicality. BFS is perfect for finding shortest paths in an unweighted grid, treating each step from one cell to an adjacent one as a single move.
Conclusion: According to the BFS suitable situation highlighted in the flowchart, the problem adjusts best with Breadth-First Search for efficiently finding the least number of moves to reach each successive tree. The BFS's use in repeated scenarios for each tree (considered as a target from the current starting position) optimizes the path discovery to cut all the trees in the needed order.
Intuition
To solve this problem, we need to find a way to navigate through the forest, cutting down trees in the specified order, and accumulating the number of steps taken to move from one tree to the next. The steps to approach the solution are as follows:
-
Identify and Sort Trees by Height: We need to traverse all the cells in the matrix and identify the tree cells (values greater than '1'). We then sort these tree cells based on their height since we need to cut them in ascending order of their height.
-
Calculate Minimum Distance Using BFS: We can use Breadth-First Search (BFS) to find the shortest path from one tree to the next. This algorithm allows us to explore the nearest neighbors of a cell first before moving on to cells that are further away, and thus we can find the shortest possible path step-by-step.
-
Optimizing BFS with a Priority Queue (Heuristic): To optimize our BFS algorithm, we can use a priority queue (in Python, we use a min-heap). We add a heuristic function
f(i, j, x, y)
which is the Manhattan distance between the current cell(i, j)
and the target cell(x, y)
. Along with the number of stepsdist
, this will decide the order of cells to be explored. The heap keeps the potential steps ordered by the estimated distance, so that the nearest cells are explored first. -
Perform BFS For Each Tree: Starting from the initial point
(0, 0)
, we perform BFS for each tree in sorted order to get the minimum steps required to reach that tree. If at any point, a tree cannot be reached, we return '-1' as the answer. -
Summing Steps and Cutting Trees: As we move from one tree to the next, we keep adding the steps returned by BFS to our total step count. When a tree is cut, we must update the cell value to '1' to reflect that the cell is now empty and can be walked through for future paths.
-
Return Total Steps: After all trees have been cut, we return the total number of steps taken as the solution.
By implementing the above approach, the cutOffTree
function is designed to provide the minimum steps required to cut off all trees in the correct order, which is crucial for solving this LeetCode problem efficiently.
Learn more about Breadth-First Search and Heap (Priority Queue) patterns.
Solution Approach
The solution implements a combination of sorting, breadth-first search (BFS), and the use of a priority queue to find the minimum number of steps required to cut all trees in ascending order of their height.
Here's a breakdown of the implementation details for cutOffTree
, which includes algorithms, data structures, or patterns:
-
Initialize Variables: The forest dimensions
m
andn
are determined by the length of the rows and columns of the input matrix. The starting point for the process is set to(0, 0)
. -
Tree Identification and Sorting: A list of tuples
trees
is created where each tuple contains the height of the tree and its coordinates(height, x, y)
. This list is populated by iterating over all cells in the forest matrix, and added to the list are cells with a value greater than1
(tree cells). After collecting all tree cells, the list is sorted by the tree height to ensure that trees are cut in the correct order. -
Breadth-First Search (BFS):
- The BFS function named
bfs
takes the current position(i, j)
and the target position(x, y)
as parameters. - A priority queue
q
is created, initially containing a tuple with the heuristic distance, the current position's coordinates, and the starting step count,0
. - A dictionary
dist
is used to track the shortest distance from the starting point to any cell(i, j)
. - While the queue is not empty, the cell with the smallest heuristic distance (estimated distance to the target) is popped, ensuring that cells closer to the target are visited first.
- For every adjacent cell
(a, b)
, we check if we can move into that cell (it's inside the grid and not a '0'). If it's a valid move and either not visited or offers a shorter path, updatedist
with the new distance and push the updated distance along with the heuristic into the queue.
- The BFS function named
-
Iterating Over Trees:
- Start from the initial position
(i, j) = (0, 0)
and iterate over all trees in the sortedtrees
list. - For each tree, call the
bfs
function to find the shortest path from the current position to the tree's position(x, y)
. The function returns the number of steps to reach the tree or-1
if the tree isn't reachable. - If a tree is unreachable (
bfs
returns-1
), the function returns-1
immediately as it's impossible to cut all trees. - The steps returned from
bfs
for each successful tree cutting are accumulated in the variableans
. Upon each successful cut, the new current position is set to the tree's coordinates.
- Start from the initial position
-
Returning the Result: Once all trees are visited and cut, the total number of minimum steps accumulated in
ans
is returned.
By implementing these steps, we use BFS to calculate the minimum steps needed between each consecutive pair of trees and keep a cumulative count. The use of a priority queue with the Manhattan distance heuristic helps in traversing the forest efficiently. This gives us an optimized means to handle the problem's constraints and find the solution using Python's structures and algorithms.
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Start EvaluatorExample Walkthrough
Let's consider a small 3x3 forest matrix forest
as our example:
1[ 2 [1, 3, 0], 3 [0, 2, 1], 4 [1, 1, 4] 5]
Here, (1,1)
is an empty cell, (0,0)
is a blocked cell, 3
, 2
, and 4
are trees with heights mentioned.
Initialization
m
= 3 (rows)n
= 3 (columns)- Start at
(0, 0)
Tree Identification and Sorting
- We identify the cells with trees:
3
at(0, 1)
2
at(1, 1)
4
at(2, 2)
- The tree list, unordered, is:
[(3, 0, 1), (2, 1, 1), (4, 2, 2)]
- After sorting, it becomes:
[(2, 1, 1), (3, 0, 1), (4, 2, 2)]
Breadth-First Search (BFS)
- Start BFS from
(0, 0)
to reach the first tree(2, 1, 1)
. - Since
(0, 0)
is blocked, move to an adjacent cell(0, 1)
where tree3
is located. However, we need to cut2
first. - Because the direct path to
(2, 1, 1)
is blocked,bfs
calculates a detour:(0, 1) -> (0, 2) -> (1, 2) -> (1, 1)
with 3 steps.
Optimizing BFS and Moving to Next Trees
- After cutting down tree
2
, our current position is(1, 1)
. - Next, we calculate the path to tree
3
at(0, 1)
. - BFS reveals a single step is needed:
(1, 1) -> (0, 1)
. - Cut tree
3
, and the current position is(0, 1)
. - Finally, we find the shortest path to tree
4
at(2, 2)
, which takes 3 steps:(0, 1) -> (0, 2) -> (1, 2) -> (2, 2)
.
Summing Steps and Returning the Result
- Total steps taken:
3 (to tree 2) + 1 (to tree 3) + 3 (to tree 4)
=7
. - Since we were able to reach all trees, the result is
7
.
By following these steps and utilizing the BFS with the priority queue and heuristic to find the shortest path through the forest, we can ensure that we cut all the trees in the required order using the minimum number of steps possible. If at any point a tree is unreachable, our implementation would return -1
, representing an impossible task. In this case, all trees were reachable, and the minimum steps required to cut all trees were successfully calculated.
Solution Implementation
1from typing import List
2from heapq import heappop, heappush
3
4class Solution:
5 def cutOffTree(self, forest: List[List[int]]) -> int:
6 # Helper function to calculate Manhattan distance
7 def get_distance(i, j, x, y):
8 return abs(i - x) + abs(j - y)
9
10 # Perform a Breadth-First Search to find the shortest path
11 def bfs(start_i, start_j, dest_i, dest_j):
12 # Initialize the priority queue with the starting position
13 # and with initial distance as the heuristic
14 queue = [(get_distance(start_i, start_j, dest_i, dest_j), start_i, start_j)]
15 # Distance dictionary keeps track of steps from the start
16 distance = {start_i * column + start_j: 0}
17 while queue:
18 # Pop the position with lowest estimated cost
19 _, curr_i, curr_j = heappop(queue)
20 current_step = distance[curr_i * column + curr_j]
21 if (curr_i, curr_j) == (dest_i, dest_j):
22 return current_step
23 # Check all four adjacent neighbors
24 for delta_i, delta_j in [[0, -1], [0, 1], [-1, 0], [1, 0]]:
25 next_i, next_j = curr_i + delta_i, curr_j + delta_j
26 # Validate next position and check if it's a tree
27 if 0 <= next_i < row and 0 <= next_j < column and forest[next_i][next_j] > 0:
28 next_position_key = next_i * column + next_j
29 if next_position_key not in distance or distance[next_position_key] > current_step + 1:
30 distance[next_position_key] = current_step + 1
31 heappush(queue, (distance[next_position_key] + get_distance(next_i, next_j, dest_i, dest_j), next_i, next_j))
32 return -1
33
34 row, column = len(forest), len(forest[0])
35 # Extract the trees and their positions and sort them based on height
36 trees = [(forest[i][j], i, j) for i in range(row) for j in range(column) if forest[i][j] > 1]
37 trees.sort()
38 current_i = current_j = total_steps = 0
39
40 # Iterate through all trees in sorted order
41 for _, target_i, target_j in trees:
42 # Get the distance to the next tree
43 steps_to_next_tree = bfs(current_i, current_j, target_i, target_j)
44 # If a tree can't be reached, return -1
45 if steps_to_next_tree == -1:
46 return -1
47 # Add the steps to the total steps and update current position
48 total_steps += steps_to_next_tree
49 current_i, current_j = target_i, target_j
50 return total_steps
51
1import java.util.*;
2
3class Solution {
4 private int[] distances = new int[3600]; // Pre-allocating space for distances.
5 private List<List<Integer>> forestGrid; // The forest as a grid of trees.
6 private int rows; // Number of rows in the forest grid.
7 private int cols; // Number of columns in the forest grid.
8
9 public int cutOffTree(List<List<Integer>> forest) {
10 this.forestGrid = forest;
11 rows = forest.size();
12 cols = forest.get(0).size();
13 List<int[]> trees = new ArrayList<>();
14
15 // Collecting trees with height > 1 to cut them down in order of height.
16 for (int i = 0; i < rows; ++i) {
17 for (int j = 0; j < cols; ++j) {
18 int treeHeight = forest.get(i).get(j);
19 if (treeHeight > 1) {
20 trees.add(new int[] {treeHeight, i * cols + j});
21 }
22 }
23 }
24
25 // Sorting trees by their heights because we need to cut trees in increasing order of height.
26 trees.sort(Comparator.comparingInt(a -> a[0]));
27
28 int steps = 0; // The minimum number of steps to cut all trees.
29 int start = 0; // Tracking the starting position of the worker (row * columns + column).
30
31 // Looping through the sorted trees to cut.
32 for (int[] tree : trees) {
33 int end = tree[1]; // Tree's position.
34 int stepToTree = bfs(start, end); // Using BFS to find the shortest path to the tree.
35 if (stepToTree == -1) {
36 return -1; // If tree is unreachable return -1.
37 }
38 steps += stepToTree; // Adding the steps to the final result.
39 start = end; // Update start position for the next tree.
40 }
41
42 return steps; // Returning the total number of steps needed to cut all trees.
43 }
44
45 private int bfs(int start, int end) {
46 PriorityQueue<int[]> queue = new PriorityQueue<>(Comparator.comparingInt(a -> a[0])); // Priority Queue for selecting the node with minimum heuristic distance.
47 queue.offer(new int[] {calculateHeuristic(start, end), start});
48 Arrays.fill(distances, Integer.MAX_VALUE);
49 distances[start] = 0; // Starting point's distance is 0.
50
51 int[] directions = {-1, 0, 1, 0, -1}; // Directions: up, right, down, left.
52
53 // Process nodes in the queue.
54 while (!queue.isEmpty()) {
55 int[] node = queue.poll();
56 int currentState = node[1];
57 if (currentState == end) { // If end is reached.
58 return distances[currentState];
59 }
60
61 // Exploring adjacent squares in the forest.
62 for (int k = 0; k < 4; ++k) {
63 int newX = currentState / cols + directions[k];
64 int newY = currentState % cols + directions[k + 1];
65 if (newX >= 0 && newX < rows && newY >= 0 && newY < cols && forestGrid.get(newX).get(newY) > 0) {
66 if (distances[newX * cols + newY] > distances[currentState] + 1) {
67 distances[newX * cols + newY] = distances[currentState] + 1; // Update distance.
68 queue.offer(new int[] {distances[newX * cols + newY] + calculateHeuristic(newX * cols + newY, end), newX * cols + newY});
69 }
70 }
71 }
72 }
73
74 return -1; // If no path is found to the end, return -1;
75 }
76
77 // Heuristic function for A* search: computes the Manhattan distance between two points.
78 private int calculateHeuristic(int start, int end) {
79 int startX = start / cols;
80 int startY = start % cols;
81 int endX = end / cols;
82 int endY = end % cols;
83 return Math.abs(startX - endX) + Math.abs(startY - endY);
84 }
85}
86
1class Solution {
2public:
3 int rows; // "m" is renamed to "rows" to be more descriptive
4 int cols; // "n" is renamed to "cols" to be more descriptive
5 vector<int> distances; // "dist" is renamed to "distances"
6
7 int cutOffTree(vector<vector<int>>& forest) {
8 rows = forest.size();
9 cols = forest[0].size();
10 distances.resize(rows * cols); // Using rows * cols instead of a hardcoded value
11 vector<pair<int, int>> trees;
12
13 // Collect all the trees with a height greater than 1 and their locations
14 for (int i = 0; i < rows; ++i) {
15 for (int j = 0; j < cols; ++j) {
16 if (forest[i][j] > 1) {
17 trees.emplace_back(forest[i][j], i * cols + j);
18 }
19 }
20 }
21
22 // Sort trees based on their height
23 sort(trees.begin(), trees.end());
24 int totalSteps = 0;
25 int currentPos = 0;
26
27 // Attempt to cut each tree in ascending order of height
28 for (auto& tree : trees) {
29 int targetPos = tree.second;
30 int stepsToTree = bfs(currentPos, targetPos, forest);
31 if (stepsToTree == -1) return -1; // If a tree is unreachable, return -1
32 totalSteps += stepsToTree;
33 currentPos = targetPos;
34 }
35
36 return totalSteps;
37 }
38
39 // Breadth-first search to find the shortest path to a tree
40 int bfs(int startPos, int endPos, vector<vector<int>>& forest) {
41 // PriorityQueue to get the node with the minimum distance from queue
42 priority_queue<pair<int, int>, vector<pair<int, int>>, greater<pair<int, int>>> minHeap;
43
44 // Start from the initial position
45 minHeap.push({distanceHeuristic(startPos, endPos), startPos});
46
47 // Initially, set all distances to infinity
48 fill(distances.begin(), distances.end(), INT_MAX);
49 distances[startPos] = 0;
50
51 // Possible moves (up, right, down, left)
52 vector<int> directions = {-1, 0, 1, 0, -1};
53
54 while (!minHeap.empty()) {
55 int currentPos = minHeap.top().second;
56 minHeap.pop();
57 if (currentPos == endPos) return distances[currentPos]; // Reached target
58
59 // Explore all adjacent cells
60 for (int k = 0; k < 4; ++k) {
61 int newX = currentPos / cols + directions[k];
62 int newY = currentPos % cols + directions[k + 1];
63
64 // If the cell is within bounds and not blocked
65 if (newX >= 0 && newX < rows && newY >= 0 && newY < cols && forest[newX][newY] > 0) {
66 int nextPos = newX * cols + newY;
67 // If a shorter distance is found, update it
68 if (distances[nextPos] > distances[currentPos] + 1) {
69 distances[nextPos] = distances[currentPos] + 1;
70 minHeap.push({distances[nextPos] + distanceHeuristic(nextPos, endPos), nextPos});
71 }
72 }
73 }
74 }
75
76 // If the path is not found, return -1
77 return -1;
78 }
79
80 // A heuristic function for estimating distance for priority queue (Manhattan distance)
81 int distanceHeuristic(int startPos, int endPos) {
82 int startX = startPos / cols;
83 int startY = startPos % cols;
84 int endX = endPos / cols;
85 int endY = endPos % cols;
86 return abs(startX - endX) + abs(startY - endY);
87 }
88};
89
1// Assuming that 'number' is a valid substitute for 'int' in the original C++ code
2// and we're using arrays and tuples to represent the vectors and pairs.
3
4// Define the number of rows and columns
5let rows: number;
6let cols: number;
7// Define the distances array
8let distances: number[];
9
10// Function to calculate the minimum steps to cut off all trees in a forest
11function cutOffTree(forest: number[][]): number {
12 rows = forest.length;
13 cols = forest[0].length;
14 // Initialize the distances with a size of rows * cols
15 distances = new Array(rows * cols);
16 // Collect trees with height greater than 1
17 const trees: [number, number][] = [];
18
19 // Collect all trees with a height greater than 1 and their locations
20 for (let i = 0; i < rows; ++i) {
21 for (let j = 0; j < cols; ++j) {
22 if (forest[i][j] > 1) {
23 trees.push([forest[i][j], i * cols + j]);
24 }
25 }
26 }
27
28 // Sort trees based on their height
29 trees.sort((a, b) => a[0] - b[0]);
30 let totalSteps = 0;
31 let currentPos = 0;
32
33 // Attempt to cut each tree in ascending order of height
34 for (const tree of trees) {
35 const targetPos = tree[1];
36 const stepsToTree = bfs(currentPos, targetPos, forest);
37 if (stepsToTree === -1) return -1; // If a tree is unreachable, return -1
38 totalSteps += stepsToTree;
39 currentPos = targetPos;
40 }
41
42 return totalSteps;
43}
44
45// Breadth-first search to find the shortest path to a tree
46function bfs(startPos: number, endPos: number, forest: number[][]): number {
47 // PriorityQueue is replaced with an array since JavaScript doesn't have a built-in PriorityQueue
48 type QueueElement = [number, number]; // Format: [heuristic+cost, position]
49 const minHeap: QueueElement[] = [];
50
51 // Start from the initial position
52 minHeap.push([distanceHeuristic(startPos, endPos), startPos]);
53
54 // Initially, set all distances to Infinity
55 distances.fill(Number.MAX_SAFE_INTEGER);
56 distances[startPos] = 0;
57
58 // Possible moves (up, right, down, left)
59 const directions = [-1, 0, 1, 0, -1];
60
61 while (minHeap.length > 0) {
62 // Since JavaScript lacks a built-in PriorityQueue, we manually sort and pop
63 minHeap.sort((a, b) => a[0] - b[0]);
64 const current = minHeap.shift();
65 if (!current) break;
66 const currentPos = current[1];
67
68 if (currentPos === endPos) return distances[currentPos]; // Reached target
69
70 // Explore all adjacent cells
71 for (let k = 0; k < 4; ++k) {
72 const newX = Math.floor(currentPos / cols) + directions[k];
73 const newY = currentPos % cols + directions[k + 1];
74
75 // If the cell is within bounds and not blocked
76 if (newX >= 0 && newX < rows && newY >= 0 && newY < cols && forest[newX][newY] > 0) {
77 const nextPos = newX * cols + newY;
78
79 // If a shorter distance is found, update it
80 if (distances[nextPos] > distances[currentPos] + 1) {
81 distances[nextPos] = distances[currentPos] + 1;
82 minHeap.push([distances[nextPos] + distanceHeuristic(nextPos, endPos), nextPos]);
83 }
84 }
85 }
86 }
87
88 // If the path is not found, return -1
89 return -1;
90}
91
92// A heuristic function for estimating distance for priority queue (Manhattan distance)
93function distanceHeuristic(startPos: number, endPos: number): number {
94 const startX = Math.floor(startPos / cols);
95 const startY = startPos % cols;
96 const endX = Math.floor(endPos / cols);
97 const endY = endPos % cols;
98 return Math.abs(startX - endX) + Math.abs(startY - endY);
99}
100
Time and Space Complexity
The given Python code performs a series of Breadth-First Searches (BFS) over a 2D grid to find the shortest path to cut trees in increasing order of their heights.
Time Complexity:
- Sorting the trees: There are at most
m*n
trees, and sorting takesO(T log T)
time whereT
is the number of trees. In the worst case, this isO(m*n log(m*n))
. - BFS function (
bfs
): For each tree, we run a BFS. In the worst case, a BFS will visit every cell in the grid once. This isO(m*n)
in the worst-case scenario for each tree. - Total BFS cost: Since BFS is executed for every tree, the worst-case time complexity for BFS across all trees is
O(T * m * n)
whereT
is the number of trees.
Overall, the total time complexity of the algorithm is O(m*n log(m*n) + T * m * n)
, which in practice is dominated by the BFS searches, so it can be approximated by O(T * m * n)
.
Space Complexity:
- Queue/Heap in BFS: The heap can grow up to
O(m*n)
in space because, in the worst case, it might contain all the cells on the grid. - Visited dictionary (
dist
): The visited dictionary also stores information for each cell at most once, resulting in a space complexity ofO(m*n)
. - Trees list: The list of trees sorted by height takes
O(T)
space.
Therefore, the total space complexity of the algorithm is the larger of O(m*n)
(due to the heap and visited dictionary) and O(T)
(due to the list of trees), which is O(m*n)
since m*n
is the upper bound on T
.
Learn more about how to find time and space complexity quickly using problem constraints.
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