How do you solve Minimum Hours of Training to Win a Competition on LeetCode?

Minimum Hours of Training to Win a Competition (LeetCode #2383) 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: Training can be done in a focused manner, addressing only the deficits in energy and experience.

What is the time complexity of Minimum Hours of Training to Win a Competition?

The optimal solution for Minimum Hours of Training to Win a Competition runs in O(n) time and uses O(1) space. The complexity is O(n) because we only loop through the opponents once, calculating the necessary training in constant time.

What is the brute force solution for Minimum Hours of Training to Win a Competition?

The brute force approach for Minimum Hours of Training to Win a Competition is Brute Force, with O(n²) time complexity and O(1) space complexity. The brute force approach involves simulating the competition by training until you can defeat each opponent. This means checking how much training is needed for both energy and experience for each opponent sequentially. The optimal Optimal Solution approach improves this to O(n).

What algorithm or data structure is used to solve Minimum Hours of Training to Win a Competition?

Minimum Hours of Training to Win a Competition uses the following concepts: Array, Greedy. The recommended patterns to study are: Greedy, Array.

What are the interview tips for Minimum Hours of Training to Win a Competition?

Always clarify the problem requirements and constraints before jumping into coding. Think about edge cases, such as what happens if your initial values are already sufficient. Explain your thought process clearly while coding; it helps the interviewer understand your approach.

What are common mistakes when solving Minimum Hours of Training to Win a Competition?

Failing to account for the need to train for both energy and experience simultaneously. Not updating the current energy and experience after defeating an opponent.

#2383

Minimum Hours of Training to Win a Competition

Easy

ArrayGreedyGreedyArray

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 calculating the minimum training required for both energy and experience separately, and summing them up. This avoids unnecessary training and focuses on the minimum needed to defeat each opponent.

⚙️

Algorithm

4 steps

1Step 1: Initialize total training hours to 0.
2Step 2: For each opponent, check if current energy and experience are sufficient.
3Step 3: If not, calculate the difference needed for both energy and experience.
4Step 4: Update the total training hours and the current energy and experience after defeating the opponent.

solution.py12 lines

1def minTrainingHours(initialEnergy, initialExperience, energy, experience):
2    total_hours = 0
3    for i in range(len(energy)):
4        if initialEnergy <= energy[i]:
5            total_hours += energy[i] - initialEnergy + 1
6            initialEnergy = energy[i] + 1
7        if initialExperience <= experience[i]:
8            total_hours += experience[i] - initialExperience + 1
9            initialExperience = experience[i] + 1
10        initialEnergy -= energy[i]
11        initialExperience += experience[i]
12    return total_hours

ℹ

Complexity note: The complexity is O(n) because we only loop through the opponents once, calculating the necessary training in constant time.

1Training can be done in a focused manner, addressing only the deficits in energy and experience.
2The order of opponents matters; always address the current opponent's requirements before moving on.

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