LeetCode 2398. Maximum Number of Robots Within Budget Solution in Java, C++, Python & More | Explanation + Code

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2398. Maximum Number of Robots Within Budget

Description

You have n robots. You are given two 0-indexed integer arrays, chargeTimes and runningCosts, both of length n. The ith robot costs chargeTimes[i] units to charge and costs runningCosts[i] units to run. You are also given an integer budget.

The total cost of running k chosen robots is equal to max(chargeTimes) + k * sum(runningCosts), where max(chargeTimes) is the largest charge cost among the k robots and sum(runningCosts) is the sum of running costs among the k robots.

Return the maximum number of consecutive robots you can run such that the total cost does not exceed budget.

 

Example 1:

Input: chargeTimes = [3,6,1,3,4], runningCosts = [2,1,3,4,5], budget = 25
Output: 3
Explanation: 
It is possible to run all individual and consecutive pairs of robots within budget.
To obtain answer 3, consider the first 3 robots. The total cost will be max(3,6,1) + 3 * sum(2,1,3) = 6 + 3 * 6 = 24 which is less than 25.
It can be shown that it is not possible to run more than 3 consecutive robots within budget, so we return 3.

Example 2:

Input: chargeTimes = [11,12,19], runningCosts = [10,8,7], budget = 19
Output: 0
Explanation: No robot can be run that does not exceed the budget, so we return 0.

 

Constraints:

  • chargeTimes.length == runningCosts.length == n
  • 1 <= n <= 5 * 104
  • 1 <= chargeTimes[i], runningCosts[i] <= 105
  • 1 <= budget <= 1015

Solutions

Solution 1: Two Pointers + Monotonic Queue

The problem is essentially finding the maximum value within a sliding window, which can be solved using a monotonic queue.

We only need to use binary search to enumerate the size of the window k, and find the largest k that satisfies the problem requirements.

In the implementation process, we don't actually need to perform binary search enumeration. We just need to change the fixed window to a non-fixed window with double pointers.

The time complexity is O(n), and the space complexity is O(n), where n is the number of robots in the problem.

PythonJavaC++GoTypeScript
class Solution: def maximumRobots( self, chargeTimes: List[int], runningCosts: List[int], budget: int ) -> int: q = deque() ans = s = l = 0 for r, (t, c) in enumerate(zip(chargeTimes, runningCosts)): s += c while q and chargeTimes[q[-1]] <= t: q.pop() q.append(r) while q and (r - l + 1) * s + chargeTimes[q[0]] > budget: if q[0] == l: q.popleft() s -= runningCosts[l] l += 1 ans = max(ans, r - l + 1) return ans(code-box)

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