Leetcode #573: Squirrel Simulation
In this guide, we solve Leetcode #573 Squirrel Simulation 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 two integers height and width representing a garden of size height x width. You are also given: an array tree where tree = [treer, treec] is the position of the tree in the garden, an array squirrel where squirrel = [squirrelr, squirrelc] is the position of the squirrel in the garden, and an array nuts where nuts[i] = [nutir, nutic] is the position of the ith nut in the garden.
Quick Facts
- Difficulty: Medium
- Premium: Yes
- Tags: Array, Math
Intuition
There is a mathematical invariant or formula that directly leads to the result.
Using math avoids unnecessary loops and reduces complexity.
Approach
Derive the formula or update rule, then compute the answer directly.
Handle edge cases like overflow or zero carefully.
Steps:
- Identify the math relationship.
- Compute the result with a loop or formula.
- Handle edge cases.
Example
Input: height = 5, width = 7, tree = [2,2], squirrel = [4,4], nuts = [[3,0], [2,5]]
Output: 12
Explanation: The squirrel should go to the nut at [2, 5] first to achieve a minimal distance.
Python Solution
class Solution:
def minDistance(
self,
height: int,
width: int,
tree: List[int],
squirrel: List[int],
nuts: List[List[int]],
) -> int:
tr, tc = tree
sr, sc = squirrel
s = sum(abs(r - tr) + abs(c - tc) for r, c in nuts) * 2
ans = inf
for r, c in nuts:
a = abs(r - tr) + abs(c - tc)
b = abs(r - sr) + abs(c - sc)
ans = min(ans, s - a + b)
return ans
Complexity
The time complexity is , where is the number of nuts. The space complexity is .
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.