Is Maximize Win From Two Segments easy or hard?

Maximize Win From Two Segments is rated Medium difficulty on LeetCode. The challenge comes from combining two optimal segments without overlap. Recognizing that sliding window plus prefix optimization reduces the problem to a linear scan is the key insight.

Maximize Win From Two Segments Python/Java solution

Python solutions usually implement the sliding window technique because it provides a clean O(n) implementation with two pointers and a prefix array. Java and C++ solutions often follow the same logic or use binary search to compute segment ranges before combining them.

How to solve Maximize Win From Two Segments in O(n)?

Use a sliding window to compute the number of prizes covered by each valid segment while scanning the array once. Maintain a prefix array storing the best segment size seen so far. For each window ending at index r, combine its size with the best segment that ends before its starting point. This guarantees the maximum total prizes in linear time.

What is the best approach for Maximize Win From Two Segments?

The sliding window approach is the most efficient and commonly expected solution. It scans the sorted prizePositions array with two pointers to maintain a valid segment of length k. A prefix array tracks the best segment seen so far so the current window can be combined with a previous one. This produces an optimal O(n) time solution.

Is Maximize Win From Two Segments asked at Google/Amazon/Meta?

Problems combining sliding window with prefix optimization are common in interviews at companies like Amazon, Google, and Meta. Variants involving maximum coverage, interval selection, or two non-overlapping windows appear frequently in coding interviews.

What data structure is used in Maximize Win From Two Segments?

The core solution relies on arrays and the two-pointer technique. A prefix maximum array stores the best segment length found so far, while the sliding window controls the current valid range. Some implementations also use binary search to locate the end of each segment.

What is the time complexity of Maximize Win From Two Segments?

The optimal sliding window solution runs in O(n) time with O(n) extra space for prefix maximum tracking. Each pointer moves at most n times, so the array is processed in a single pass. A binary search based approach takes O(n log n) time.

Description Hints 2 Solution Video 16:08

2555. Maximize Win From Two Segments

Medium38.2% acceptance1 company

There are some prizes on the X-axis. You are given an integer array prizePositions that is sorted in non-decreasing order, where prizePositions[i] is the position of the i^th prize. There could be different prizes at the same position on the line. You are also given an integer k.

You are allowed to select two segments with integer endpoints. The length of each segment must be k. You will collect all prizes whose position falls within at least one of the two selected segments (including the endpoints of the segments). The two selected segments may intersect.

For example if k = 2, you can choose segments [1, 3] and [2, 4], and you will win any prize i that satisfies 1 <= prizePositions[i] <= 3 or 2 <= prizePositions[i] <= 4.

Return the maximum number of prizes you can win if you choose the two segments optimally.

Example 1:

Input: prizePositions = [1,1,2,2,3,3,5], k = 2
Output: 7
Explanation: In this example, you can win all 7 prizes by selecting two segments [1, 3] and [3, 5].

Example 2:

Input: prizePositions = [1,2,3,4], k = 0
Output: 2
Explanation: For this example, one choice for the segments is [3, 3] and [4, 4], and you will be able to get 2 prizes.

Constraints:

1 <= prizePositions.length <= 10⁵
1 <= prizePositions[i] <= 10⁹
0 <= k <= 10⁹
prizePositions is sorted in non-decreasing order.

Asked at 1 company

Uber

Topics

Hints open one at a time

Hint 1

Try solving the problem for one interval.

Hint 2

Using the solution with one interval, how can you combine that with a second interval?

16:08

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