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Compute optimal matrix-chain multiplication order

Last updated: Mar 29, 2026

Quick Overview

This question evaluates dynamic programming, algorithmic analysis, and sensitivity reasoning for matrix-chain multiplication, and it belongs to the Coding & Algorithms domain; it is commonly asked because it tests understanding of optimal substructure, trade-offs in parenthesization, and correctness versus greedy heuristics.

  • hard
  • Apple
  • Coding & Algorithms
  • Data Scientist

Compute optimal matrix-chain multiplication order

Company: Apple

Role: Data Scientist

Category: Coding & Algorithms

Difficulty: hard

Interview Round: Onsite

Matrix Chain Multiplication optimal parenthesization. Given matrices with dimensions: A1 (10×30), A2 (30×5), A3 (5×60), A4 (60×2), A5 (2×100). (a) Use dynamic programming to compute the minimum scalar multiplications and the optimal parenthesization; show your DP tables m[i,j] and s[i,j] and the final order. (b) Prove optimal substructure and explain why a greedy choice on local smallest dimension fails. (c) Analyze time and space complexity; discuss when Strassen‑like algorithms could reduce cost in practice given these skinny/fat shapes. (d) If A3 is 5×k and A4 is k×2 with k unknown at design time, derive the threshold on k where the optimal parenthesization changes.

Quick Answer: This question evaluates dynamic programming, algorithmic analysis, and sensitivity reasoning for matrix-chain multiplication, and it belongs to the Coding & Algorithms domain; it is commonly asked because it tests understanding of optimal substructure, trade-offs in parenthesization, and correctness versus greedy heuristics.

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Apple
Oct 13, 2025, 9:49 PM
Data Scientist
Onsite
Coding & Algorithms
4
0

Matrix Chain Multiplication: Optimal Parenthesization and Analysis

You are given five matrices to multiply: A1 (10×30), A2 (30×5), A3 (5×60), A4 (60×2), A5 (2×100). Assume the standard cost model: multiplying a (p×q) matrix by a (q×r) matrix costs p·q·r scalar multiplications. Matrix multiplication is associative but not commutative.

Tasks:

(a) Use dynamic programming to compute the minimum number of scalar multiplications and the optimal parenthesization. Show the DP tables m[i,j] (minimum cost) and s[i,j] (split point) and give the final order.

(b) Prove optimal substructure and explain why a greedy choice based on the locally smallest multiplication cost (or smallest local dimension) can fail; provide a concrete counterexample.

(c) Analyze time and space complexity. Discuss when Strassen-like algorithms could reduce cost in practice for skinny/fat matrices like these.

(d) Now let A3 be 5×k and A4 be k×2, where k is unknown at design time. Derive the threshold on k (integer) at which the optimal parenthesization changes, and state the optimal order on each side of the threshold.

Solution

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