MathWorks·Software Engineer·Technical Phone Screen·Intermediate

Intermediate

Jun 2026

Summary

MathWorks software engineer round with one algorithmic problem. Pretty clean problem statement but the constraints are sneaky and I spent more time than I'd like to admit figuring out the right approach.

Questions Asked (1)

You have an integer array and an integer K. You must perform exactly K decrement operations (each operation reduces one element by 1). After all operations, maximize the minimum value in the array. Return that maximum possible minimum.

Algorithms & Data Structures

Author's notes

Binary search on the answer is the move here, but I didn't see it immediately.

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AI HintsAI Generated

Suggested Approach

First, recognize that the problem is to find the maximum possible minimum value after exactly K decrements. Use binary search on the answer: for a candidate minimum X, compute the total decrements needed to make all elements at least X (i.e., sum of max(0, X - a[i])). If this sum ≤ K, then X is feasible. The answer is the largest feasible X.

Pro tip: Clarify that after making all elements at least X, any remaining decrements can be applied to any element without reducing the minimum below X, as long as we don't drop below X. This ensures exactly K operations are used.

1. Understand the problem

Restate the goal: after exactly K decrements, maximize the minimum element. Note that decrements can only reduce values, so the minimum cannot increase.

2. Identify feasibility check

For a target minimum X, compute the total decrements needed to raise all elements to at least X: sum(max(0, X - a[i])). If this sum ≤ K, X is achievable.

3. Apply binary search

Binary search X between the current minimum and the maximum possible value (e.g., max(a) + K). Find the largest X for which the feasibility check holds.

4. Handle exactly K operations

After ensuring all elements ≥ X, if there are leftover decrements, apply them to any element(s) without dropping below X. This is always possible if X is feasible.

5. Analyze complexity

The binary search runs in O(n log(maxVal + K)) time, which is efficient for typical constraints.

Key Points to Mention

Binary search on the answer (the minimum value).
Feasibility function: total decrements needed to make all elements ≥ X.
Monotonicity: if X is feasible, any smaller value is also feasible.
Handling exactly K operations by distributing leftover decrements without violating the minimum.
Time complexity: O(n log(max(a) + K)).
Edge cases: K=0, all elements equal, large K.

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