How do you solve Find the Highest Altitude on LeetCode?

Find the Highest Altitude (LeetCode #1732) 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 highest altitude is determined by cumulative gains, which can be efficiently calculated using a running sum.

What is the time complexity of Find the Highest Altitude?

The optimal solution for Find the Highest Altitude runs in O(n) time and uses O(1) space. This complexity is linear because we only traverse the array once, updating our altitude and maximum altitude in constant time.

What is the brute force solution for Find the Highest Altitude?

The brute force approach for Find the Highest Altitude is Brute Force, with O(n²) time complexity and O(1) space complexity. In this approach, we calculate the altitude at each point by summing up all previous gains. This is straightforward but inefficient since we repeatedly calculate sums for each point. The optimal Optimal Solution approach improves this to O(n).

What algorithm or data structure is used to solve Find the Highest Altitude?

Find the Highest Altitude uses the following concepts: Array, Prefix Sum. The recommended patterns to study are: Prefix Sum, Array.

What are the interview tips for Find the Highest Altitude?

Always consider edge cases, such as all negative gains. Think about how you can reduce the number of calculations needed, especially in array problems.

What are common mistakes when solving Find the Highest Altitude?

Not initializing variables correctly, leading to incorrect results. Overcomplicating the problem by trying to calculate all altitudes instead of focusing on the maximum.

#1732

Find the Highest Altitude

Easy

ArrayPrefix SumPrefix SumArray

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)

We can compute the highest altitude in a single pass by maintaining a running sum of the gains. This is more efficient as we avoid repeated calculations.

⚙️

Algorithm

4 steps

1Step 1: Initialize `current_altitude` to 0 and `max_altitude` to 0.
2Step 2: Iterate through each gain in the array, updating `current_altitude` by adding the gain.
3Step 3: After updating, check if `current_altitude` is greater than `max_altitude` and update it if necessary.
4Step 4: Return `max_altitude` after processing all gains.

solution.py9 lines

1# Full working Python code
2
3def highestAltitude(gain):
4    current_altitude = 0
5    max_altitude = 0
6    for g in gain:
7        current_altitude += g
8        max_altitude = max(max_altitude, current_altitude)
9    return max_altitude

ℹ

Complexity note: This complexity is linear because we only traverse the array once, updating our altitude and maximum altitude in constant time.

1The highest altitude is determined by cumulative gains, which can be efficiently calculated using a running sum.
2Understanding prefix sums can simplify many problems involving cumulative data.

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