How do you solve Ant on the Boundary on LeetCode?

Ant on the Boundary (LeetCode #3028) can be solved using Brute Force or Optimal Solution. The optimal approach is Optimal Solution with O(n) time complexity and O(1) space complexity. Key insight: The ant only counts a return to the boundary if it ends exactly at position 0 after a move.

What is the brute force solution for Ant on the Boundary?

The brute force approach for Ant on the Boundary is Brute Force, with O(n²) time complexity and O(1) space complexity. The brute force approach involves simulating each movement of the ant step by step and checking if it returns to the boundary after each move. This is straightforward but inefficient for larger inputs. The optimal Optimal Solution approach improves this to O(n).

What algorithm or data structure is used to solve Ant on the Boundary?

Ant on the Boundary uses the following concepts: Array, Simulation, Prefix Sum. The recommended patterns to study are: Array, Simulation.

What are the interview tips for Ant on the Boundary?

Always clarify the problem statement and edge cases before jumping into coding. Think about the time and space complexity of your solution and explain it during the interview. Practice simulating the problem with small inputs to build intuition.

What are common mistakes when solving Ant on the Boundary?

Not properly checking the position after each move. Confusing the movement direction based on the sign of the numbers.

#3028

Ant on the Boundary

Easy

ArraySimulationPrefix SumArraySimulation

LeetCode ↗

Approaches

Brute ForceOptimal

Complexity Comparison

	Brute Force	Optimal Solution★
Time	O(n²)	O(n)
Space	O(1)	O(1)

💡

Intuition

Time O(n)Space O(1)

The optimal approach involves keeping track of the ant's position and counting the returns to the boundary in a single pass through the nums array. This is more efficient and scales better with larger inputs.

⚙️

Algorithm

5 steps

1Step 1: Initialize a variable 'position' to 0 to track the ant's current position.
2Step 2: Initialize a counter 'returns' to 0 to count how many times the ant returns to the boundary.
3Step 3: Loop through each element in the nums array and update the position based on the value of the current element.
4Step 4: After moving, check if the position is exactly 0 (boundary). If yes, increment the 'returns' counter.
5Step 5: Return the 'returns' counter at the end.

solution.py10 lines

1# Full working Python code
2
3def ant_on_boundary(nums):
4    position = 0
5    returns = 0
6    for move in nums:
7        position += move
8        if position == 0:
9            returns += 1
10    return returns

ℹ

Complexity note: The time complexity is O(n) because we make a single pass through the nums array, updating the position and checking for returns in constant time.

1The ant only counts a return to the boundary if it ends exactly at position 0 after a move.
2The movement direction is determined by the sign of the nums array elements.

Solutions and explanations are original Tejav content. Problem titles © LeetCode — use the LeetCode button above for the full problem statement.