Solve and optimize menu combo DP

Quick Overview

This question evaluates dynamic programming and combinatorial optimization skills, focusing on state definition, transitions, memoization, pruning dominated offers, and minimizing state space for menu-combo minimum-cost problems in the domain of Coding & Algorithms.

Solve and optimize menu combo DP

Company: Airbnb

Role: Software Engineer

Category: Coding & Algorithms

Difficulty: medium

Interview Round: Technical Screen

Given up to N different menu items, each with a unit price, and a list of combo offers where each offer specifies quantities for some items and a total combo price, write an algorithm that, given the required quantities for each item, returns the minimum cost to satisfy the order without buying extra items. Describe and implement a dynamic programming or memoized search solution, stating the state definition, transitions, and base cases. Analyze time and space complexity in terms of N (item types), Q (maximum needed quantity per item), and S (number of offers). Follow-up: When N = 3, propose and implement an optimization that leverages a fixed 3D state (i, j, k) to reduce overhead; explain any preprocessing such as pruning dominated or irrelevant offers and capping offer counts to needs; provide the resulting complexity. Discuss additional practical optimizations such as skipping offers that exceed current needs or reordering states to enable bottom-up computation.

Quick Answer: This question evaluates dynamic programming and combinatorial optimization skills, focusing on state definition, transitions, memoization, pruning dominated offers, and minimizing state space for menu-combo minimum-cost problems in the domain of Coding & Algorithms.

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Sep 6, 2025, 12:00 AM
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Given up to N different menu items, each with a unit price, and a list of combo offers where each offer specifies quantities for some items and a total combo price, write an algorithm that, given the required quantities for each item, returns the minimum cost to satisfy the order without buying extra items. Describe and implement a dynamic programming or memoized search solution, stating the state definition, transitions, and base cases. Analyze time and space complexity in terms of N (item types), Q (maximum needed quantity per item), and S (number of offers). Follow-up: When N = 3, propose and implement an optimization that leverages a fixed 3D state (i, j, k) to reduce overhead; explain any preprocessing such as pruning dominated or irrelevant offers and capping offer counts to needs; provide the resulting complexity. Discuss additional practical optimizations such as skipping offers that exceed current needs or reordering states to enable bottom-up computation.

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