Minimum Total Space Wasted With K Resizing Operations
The problem asks to minimize the total space wasted with at most k
resizing operations. We can use a dynamic programming approach to efficiently solve this problem.
Python solution:
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 class Solution:
def minSpaceWastedKResizing(self, nums: List[int], k: int) > int:
n = len(nums)
prefix_sum = [0] * (n + 1)
for i in range(n):
prefix_sum[i + 1] = prefix_sum[i] + nums[i]
# dp[i][j] stores the minimum waste with i resizes for first j numbers
dp = [[float('inf')] * (k + 1) for _ in range(n + 1)]
dp[0][0] = 0
for j in range(1, n + 1):
for r in range(k + 1):
max_num = 0
total = 0
for i in range(j, 0, 1):
max_num = max(max_num, nums[i  1])
total = max_num * (j  i + 1)  (prefix_sum[j]  prefix_sum[i  1])
if r > 0:
dp[j][r] = min(dp[j][r], dp[i  1][r  1] + total)
else:
dp[j][r] = dp[i  1][r] + total
return dp[n][k]

This code works by considering the minimum wasted space for each subset of nums
for each possible number of resizes. It utilizes dynamic programming to compute these values efficiently and returns the minimum wasted space for the whole array with k
resizes.