<p>You are currently designing a dynamic array. You are given a <strong>0-indexed</strong> integer array <code>nums</code>, where <code>nums[i]</code> is the number of elements that will be in the array at time <code>i</code>. In addition, you are given an integer <code>k</code>, the <strong>maximum</strong> number of times you can <strong>resize</strong> the array (to<strong> any</strong> size).</p>

<p>The size of the array at time <code>t</code>, <code>size<sub>t</sub></code>, must be at least <code>nums[t]</code> because there needs to be enough space in the array to hold all the elements. The <strong>space wasted</strong> at&nbsp;time <code>t</code> is defined as <code>size<sub>t</sub> - nums[t]</code>, and the <strong>total</strong> space wasted is the <strong>sum</strong> of the space wasted across every time <code>t</code> where <code>0 &lt;= t &lt; nums.length</code>.</p>

<p>Return <em>the <strong>minimum</strong> <strong>total space wasted</strong> if you can resize the array at most</em> <code>k</code> <em>times</em>.</p>

<p><strong>Note:</strong> The array can have <strong>any size</strong> at the start and does<strong> not </strong>count towards the number of resizing operations.</p>

<p>&nbsp;</p>
<p><strong class="example">Example 1:</strong></p>

<pre>
<strong>Input:</strong> nums = [10,20], k = 0
<strong>Output:</strong> 10
<strong>Explanation:</strong> size = [20,20].
We can set the initial size to be 20.
The total wasted space is (20 - 10) + (20 - 20) = 10.
</pre>

<p><strong class="example">Example 2:</strong></p>

<pre>
<strong>Input:</strong> nums = [10,20,30], k = 1
<strong>Output:</strong> 10
<strong>Explanation:</strong> size = [20,20,30].
We can set the initial size to be 20 and resize to 30 at time 2. 
The total wasted space is (20 - 10) + (20 - 20) + (30 - 30) = 10.
</pre>

<p><strong class="example">Example 3:</strong></p>

<pre>
<strong>Input:</strong> nums = [10,20,15,30,20], k = 2
<strong>Output:</strong> 15
<strong>Explanation:</strong> size = [10,20,20,30,30].
We can set the initial size to 10, resize to 20 at time 1, and resize to 30 at time 3.
The total wasted space is (10 - 10) + (20 - 20) + (20 - 15) + (30 - 30) + (30 - 20) = 15.
</pre>

<p>&nbsp;</p>
<p><strong>Constraints:</strong></p>

<ul>
	<li><code>1 &lt;= nums.length &lt;= 200</code></li>
	<li><code>1 &lt;= nums[i] &lt;= 10<sup>6</sup></code></li>
	<li><code>0 &lt;= k &lt;= nums.length - 1</code></li>
</ul>

from functools import lru_cache

class Solution:
    def minSpaceWastedKResizing(self, nums, k):
        n = len(nums)
        
        @lru_cache(None)
        def dp(i, k):
            if i == n:
                return 0
            if k == 0:
                return max(nums[i:]) * (n - i) - sum(nums[i:])
            
            min_waste = float('inf')
            max_so_far = 0
            total_sum = 0
            
            for j in range(i, n):
                max_so_far = max(max_so_far, nums[j])
                total_sum += nums[j]
                waste = max_so_far * (j - i + 1) - total_sum
                min_waste = min(min_waste, waste + dp(j + 1, k - 1))
            
            return min_waste
        
        return dp(0, k)

# Example usage:
solution = Solution()
nums = [10, 20, 15, 30, 20]
k = 2
print(solution.minSpaceWastedKResizing(nums, k))  # Output: 15
28900