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MathWorks·Software Engineer·Technical Phone Screen·Intermediate

Intermediate
May 2026

Summary

MathWorks SET interview had at least one solid algorithms problem that I didn't feel fully prepared for going in. The problem looked deceptively clean on the surface but the optimal approach took me a minute to land on.

Questions Asked (1)

Q1

Given an array of integers, you repeatedly pick any two numbers, replace them with their sum, and pay a cost equal to that sum. Keep going until one number is left. What strategy minimizes the total cost?

Algorithms & Data Structures
Author's notes

I knew there was a greedy angle here but took me a bit to convince myself why merging the two smallest first actually works.

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AI HintsAI Generated

Suggested Approach

Recognize that the problem is equivalent to building a Huffman tree, where the total cost is the sum of all internal node weights. The optimal strategy is to always combine the two smallest numbers first, which minimizes the total cost. Explain this using a greedy algorithm and justify why it works.

Pro tip: Mention that this is exactly the Huffman coding algorithm and that using a min-heap gives an efficient O(n log n) solution. Also, briefly explain why greedy works here (exchange argument) to show depth.

1. Understand the problem

Restate the problem: we need to merge numbers pairwise, paying the sum each time, and minimize the total cost. Recognize that the order of merges affects the total cost.

2. Identify the optimal strategy

Propose the greedy strategy: always pick the two smallest numbers to merge. Explain that this minimizes the immediate cost and leads to the global minimum.

3. Connect to Huffman coding

Explain that this is exactly the Huffman coding problem, where the total cost is the weighted external path length. The greedy algorithm builds the optimal Huffman tree.

4. Prove correctness

Provide a brief justification: by exchange argument, any optimal solution must merge the two smallest first; otherwise swapping would reduce cost. This ensures the greedy choice is safe.

5. Discuss implementation and complexity

Describe using a min-heap (priority queue) to efficiently extract the two smallest and insert their sum. Time complexity is O(n log n), space O(n).

Key Points to Mention

  • Greedy algorithm: always merge the two smallest numbers.
  • Equivalence to Huffman coding and optimal merge pattern.
  • Proof of optimality via exchange argument or induction.
  • Use of a min-heap for efficient implementation.
  • Time complexity O(n log n) and space complexity O(n).
  • Example: for [1,2,3,4], merging 1+2=3 (cost 3), then 3+3=6 (cost 6), then 4+6=10 (cost 10), total 19; alternative strategies yield higher cost.

AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.