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maximum sum of 3 non overlapping subarrays leetcode 689

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the problem "maximum sum of 3 non-overlapping subarrays" (leetcode 689) is an interesting challenge that requires a strategic approach to efficiently find the maximum sum of three non-overlapping subarrays of a given size. here’s a detailed tutorial on how to approach this problem.
problem statement
you are given an integer array `nums` and three integers `len1`, `len2`, and `len3`. the task is to find three non-overlapping subarrays of lengths `len1`, `len2`, and `len3`, respectively, such that their combined sum is maximized.
constraints
- the lengths of the subarrays (`len1`, `len2`, `len3`) must be non-negative integers.
- the sum of `len1`, `len2`, and `len3` must be less than or equal to the length of `nums`.
approach
1. **prefix sum array**:
- compute a prefix sum array to facilitate quick calculation of the sum of any subarray. the prefix sum at index `i` will contain the sum of all elements from the start of the array up to index `i`.
2. **calculate subarray sums**:
- precompute the sums of all possible subarrays of lengths `len1`, `len2`, and `len3` using the prefix sum array.
3. **dynamic programming**:
- use dynamic programming to keep track of the maximum sums of the first two non-overlapping subarrays up to each index.
4. **iterate and compute maximum**:
- for the third subarray, iterate through the possible starting positions, and for each position, check the maximum combined sum of the first two subarrays.
code example
here's a python implementation of the above approach:
explanation of the code
1. **prefix sum calculation**: the `prefix_sum` array is computed to allow o(1) time complexity for calculating any subarray sum.
2. **max sum arrays**:
- `max_sum1` keeps track of the maximum sum of the first subarray that can end at each index.
- `max_sum2` maintains the maximum sum of the first two non-overlapping subarrays.
3. **final calculation**: finally, we iterate over possible starting positions for the third s ...
#LeetCode #DynamicProgramming #windows
maximum sum
3 non overlapping subarrays
leetcode
sliding window
prefix sum
array partitioning
optimization
dynamic programming
greedy approach
contiguous subarrays
maximum sum problem
interval selection
constraints
algorithm design
problem solving
problem statement
you are given an integer array `nums` and three integers `len1`, `len2`, and `len3`. the task is to find three non-overlapping subarrays of lengths `len1`, `len2`, and `len3`, respectively, such that their combined sum is maximized.
constraints
- the lengths of the subarrays (`len1`, `len2`, `len3`) must be non-negative integers.
- the sum of `len1`, `len2`, and `len3` must be less than or equal to the length of `nums`.
approach
1. **prefix sum array**:
- compute a prefix sum array to facilitate quick calculation of the sum of any subarray. the prefix sum at index `i` will contain the sum of all elements from the start of the array up to index `i`.
2. **calculate subarray sums**:
- precompute the sums of all possible subarrays of lengths `len1`, `len2`, and `len3` using the prefix sum array.
3. **dynamic programming**:
- use dynamic programming to keep track of the maximum sums of the first two non-overlapping subarrays up to each index.
4. **iterate and compute maximum**:
- for the third subarray, iterate through the possible starting positions, and for each position, check the maximum combined sum of the first two subarrays.
code example
here's a python implementation of the above approach:
explanation of the code
1. **prefix sum calculation**: the `prefix_sum` array is computed to allow o(1) time complexity for calculating any subarray sum.
2. **max sum arrays**:
- `max_sum1` keeps track of the maximum sum of the first subarray that can end at each index.
- `max_sum2` maintains the maximum sum of the first two non-overlapping subarrays.
3. **final calculation**: finally, we iterate over possible starting positions for the third s ...
#LeetCode #DynamicProgramming #windows
maximum sum
3 non overlapping subarrays
leetcode
sliding window
prefix sum
array partitioning
optimization
dynamic programming
greedy approach
contiguous subarrays
maximum sum problem
interval selection
constraints
algorithm design
problem solving