Start by collecting all trees and sorting them by height using a priority queue. Use breadth-first search to calculate the shortest path between consecutive trees, ensuring all cells are reachable. Return the total steps or -1 if any tree is inaccessible, emphasizing BFS over a grid and handling blocked paths carefully for efficiency.
Problem Statement
You are given a forest represented as an m x n matrix where each cell can be 0 (obstacle), 1 (empty land), or a positive integer representing a tree height. You need to cut off all trees in increasing order of their height, starting from the shortest tree. You can move one step at a time in four directions: north, south, east, and west, and cannot cross obstacles.
After cutting a tree, its cell becomes empty land (1). Determine the minimum number of steps required to cut all trees in order. If it is impossible to reach any tree, return -1. Constraints: forest dimensions up to 50x50, all tree heights distinct, and you may start at (0,0) if it contains a tree.
Examples
Example 1
Input: forest = [[1,2,3],[0,0,4],[7,6,5]]
Output: 6
Following the path above allows you to cut off the trees from shortest to tallest in 6 steps.
Example 2
Input: forest = [[1,2,3],[0,0,0],[7,6,5]]
Output: -1
The trees in the bottom row cannot be accessed as the middle row is blocked.
Example 3
Input: forest = [[2,3,4],[0,0,5],[8,7,6]]
Output: 6
You can follow the same path as Example 1 to cut off all the trees. Note that you can cut off the first tree at (0, 0) before making any steps.
Constraints
- m == forest.length
- n == forest[i].length
- 1 <= m, n <= 50
- 0 <= forest[i][j] <= 109
- Heights of all trees are distinct.
Solution Approach
Sort Trees by Height
Traverse the forest and record positions and heights of all trees. Use a min-heap (priority queue) to ensure trees are cut in increasing order. Sorting guarantees BFS always targets the next shortest tree.
Breadth-First Search Between Trees
For each consecutive tree, perform BFS from the current position to the target tree to find the minimum steps. Track visited cells to avoid revisiting and handle obstacles (0 cells). Return -1 immediately if the tree is unreachable.
Accumulate Steps and Update Forest
After reaching a tree, add the BFS distance to the total steps. Mark the cell as 1 to simulate cutting. Repeat until all trees are cut. This ensures accurate step counting while maintaining BFS traversal efficiency.
Complexity Analysis
| Metric | Value |
|---|---|
| Time | O((RC)^2) |
| Space | O(R*C) |
Time complexity is O((RC)^2) because BFS is performed for each of up to RC trees on an RC grid. Space complexity is O(RC) for BFS queue and visited matrix.
What Interviewers Usually Probe
- Candidate uses BFS for shortest path between trees.
- Candidate correctly handles blocked paths and returns -1 when unreachable.
- Candidate sorts trees and uses priority queue to ensure correct cutting order.
Common Pitfalls or Variants
Common pitfalls
- Failing to cut trees strictly by increasing height.
- Not checking if a tree is unreachable, leading to wrong total steps.
- Inefficient BFS or repeated calculations for the same paths.
Follow-up variants
- Forest with multiple trees of same height requiring tie-breaking by position.
- Allow diagonal movement between cells for cutting trees.
- Large sparse forest where BFS optimization with pruning is necessary.
How GhostInterview Helps
- GhostInterview identifies the next tree in order and calculates BFS paths efficiently.
- It highlights inaccessible trees early to prevent wasted computation.
- Provides step-by-step accumulation of moves ensuring correct total step count and heap order.
Topic Pages
Related GhostInterview Pages
- LeetCode Interview Copilot - Use GhostInterview as a live solver when you want direct help with LeetCode-style coding questions.
- Coding Interview Assistant - See how GhostInterview supports array, string, linked list, graph, and tree interview workflows.
- How GhostInterview Works - Review the screenshot, reasoning, and answer flow before using the solver in a live interview.
FAQ
What is the main algorithmic pattern for Cut Off Trees for Golf Event?
The problem primarily uses Array plus Breadth-First Search with a priority queue to manage tree cutting order.
Why use a heap in this problem?
A min-heap ensures trees are cut in increasing height order, which is required for the golf event.
How do I handle unreachable trees?
During BFS, if the target tree cannot be reached due to obstacles, immediately return -1 to indicate impossibility.
Can BFS be optimized for large forests?
Yes, you can prune visited paths and terminate BFS early when reaching the target tree to save computation.
Do I need to update the forest after cutting a tree?
Yes, mark the cell as 1 to simulate the tree being cut, which affects subsequent BFS traversals.
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