What is the best approach for Maximum Number of Non-Overlapping Subarrays With Sum Equals Target?

The most efficient approach uses prefix sum with a hash map. Track the cumulative sum while iterating and check if current_sum minus target has appeared before. When found, increment the count and reset the prefix tracking to avoid overlap. This method runs in O(n) time with O(n) space.

Is Maximum Number of Non-Overlapping Subarrays With Sum Equals Target asked at Google/Amazon/Meta?

Problems involving prefix sums and non-overlapping subarrays frequently appear in interviews at companies like Amazon, Google, and Meta. Variations of this problem test understanding of prefix sums, greedy segmentation, and hash-based lookups.

What data structure is used in Maximum Number of Non-Overlapping Subarrays With Sum Equals Target?

The key data structures are a hash map or hash set to store prefix sums encountered during traversal. These structures allow constant-time checks for whether a previous prefix creates a subarray equal to the target.

What is the time complexity of Maximum Number of Non-Overlapping Subarrays With Sum Equals Target?

The optimal solutions run in O(n) time because the array is scanned once while maintaining prefix sums in a hash map or set. Each lookup and insertion is O(1) on average. Space complexity is O(n) due to storing prefix sums.

Description Hints 2 Solution Video 9:52

1546. Maximum Number of Non-Overlapping Subarrays With Sum Equals Target

Q: Is Maximum Number of Non-Overlapping Subarrays With Sum Equals Target easy or hard?

The problem is rated Medium on LeetCode with an acceptance rate around 48%. The challenge lies in recognizing the prefix sum pattern and applying a greedy reset to prevent overlapping subarrays.

Q: Maximum Number of Non-Overlapping Subarrays With Sum Equals Target Python/Java solution

Python and JavaScript implementations typically use a hash map or set with prefix sums to detect valid subarrays in O(n) time. C++ solutions often follow the same logic using unordered_set or unordered_map while resetting state whenever a valid subarray is found.

Q: How to solve Maximum Number of Non-Overlapping Subarrays With Sum Equals Target in O(n)?

Maintain a running prefix sum and store previously seen sums in a hash map or set. At each element, check whether current_sum minus target exists. When a match appears, increment the answer and reset the prefix tracking so the next subarray starts after the current one. This guarantees non-overlapping segments while keeping the algorithm linear.

Medium49.4% acceptance1 company

Given an array nums and an integer target, return the maximum number of non-empty non-overlapping subarrays such that the sum of values in each subarray is equal to target.

Example 1:

Input: nums = [1,1,1,1,1], target = 2
Output: 2
Explanation: There are 2 non-overlapping subarrays [1,1,1,1,1] with sum equals to target(2).

Example 2:

Input: nums = [-1,3,5,1,4,2,-9], target = 6
Output: 2
Explanation: There are 3 subarrays with sum equal to 6.
([5,1], [4,2], [3,5,1,4,2,-9]) but only the first 2 are non-overlapping.

Constraints:

1 <= nums.length <= 10⁵
-10⁴ <= nums[i] <= 10⁴
0 <= target <= 10⁶

Asked at 1 company

Google

Topics

Hints open one at a time

Hint 1

Keep track of prefix sums to quickly look up what subarray that sums "target" can be formed at each step of scanning the input array.

Hint 2

It can be proved that greedily forming valid subarrays as soon as one is found is optimal.

9:52

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1class Solution:
2    def maxNonOverlapping(self, nums: List[int], target: int) -> int:
3        

Starter codePython 3Spaces: 4

Examples

Case 1Case 2

Input

[1,1,1,1,1]
2

Input

[-1,3,5,1,4,2,-9]
6

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