LeetCode #2115 — MEDIUM

Find All Possible Recipes from Given Supplies

Move from brute-force thinking to an efficient approach using array strategy.

Solve on LeetCode
The Problem

Problem Statement

You have information about n different recipes. You are given a string array recipes and a 2D string array ingredients. The ith recipe has the name recipes[i], and you can create it if you have all the needed ingredients from ingredients[i]. A recipe can also be an ingredient for other recipes, i.e., ingredients[i] may contain a string that is in recipes.

You are also given a string array supplies containing all the ingredients that you initially have, and you have an infinite supply of all of them.

Return a list of all the recipes that you can create. You may return the answer in any order.

Note that two recipes may contain each other in their ingredients.

Example 1:

Input: recipes = ["bread"], ingredients = [["yeast","flour"]], supplies = ["yeast","flour","corn"]
Output: ["bread"]
Explanation:
We can create "bread" since we have the ingredients "yeast" and "flour".

Example 2:

Input: recipes = ["bread","sandwich"], ingredients = [["yeast","flour"],["bread","meat"]], supplies = ["yeast","flour","meat"]
Output: ["bread","sandwich"]
Explanation:
We can create "bread" since we have the ingredients "yeast" and "flour".
We can create "sandwich" since we have the ingredient "meat" and can create the ingredient "bread".

Example 3:

Input: recipes = ["bread","sandwich","burger"], ingredients = [["yeast","flour"],["bread","meat"],["sandwich","meat","bread"]], supplies = ["yeast","flour","meat"]
Output: ["bread","sandwich","burger"]
Explanation:
We can create "bread" since we have the ingredients "yeast" and "flour".
We can create "sandwich" since we have the ingredient "meat" and can create the ingredient "bread".
We can create "burger" since we have the ingredient "meat" and can create the ingredients "bread" and "sandwich".

Constraints:

  • n == recipes.length == ingredients.length
  • 1 <= n <= 100
  • 1 <= ingredients[i].length, supplies.length <= 100
  • 1 <= recipes[i].length, ingredients[i][j].length, supplies[k].length <= 10
  • recipes[i], ingredients[i][j], and supplies[k] consist only of lowercase English letters.
  • All the values of recipes and supplies combined are unique.
  • Each ingredients[i] does not contain any duplicate values.
Patterns Used

Roadmap

  1. Brute Force Baseline
  2. Core Insight
  3. Algorithm Walkthrough
  4. Edge Cases
  5. Full Annotated Code
  6. Interactive Study Demo
  7. Complexity Analysis
Step 01

Brute Force Baseline

Problem summary: You have information about n different recipes. You are given a string array recipes and a 2D string array ingredients. The ith recipe has the name recipes[i], and you can create it if you have all the needed ingredients from ingredients[i]. A recipe can also be an ingredient for other recipes, i.e., ingredients[i] may contain a string that is in recipes. You are also given a string array supplies containing all the ingredients that you initially have, and you have an infinite supply of all of them. Return a list of all the recipes that you can create. You may return the answer in any order. Note that two recipes may contain each other in their ingredients.

Baseline thinking

Start with the most direct exhaustive search. That gives a correctness anchor before optimizing.

Pattern signal: Array · Hash Map · Topological Sort

Example 1

["bread"]
[["yeast","flour"]]
["yeast","flour","corn"]

Example 2

["bread","sandwich"]
[["yeast","flour"],["bread","meat"]]
["yeast","flour","meat"]

Example 3

["bread","sandwich","burger"]
[["yeast","flour"],["bread","meat"],["sandwich","meat","bread"]]
["yeast","flour","meat"]

Related Problems

  • Course Schedule II (course-schedule-ii)
  • Count Good Meals (count-good-meals)
Step 02

Core Insight

What unlocks the optimal approach

  • Can we use a data structure to quickly query whether we have a certain ingredient?
  • Once we verify that we can make a recipe, we can add it to our ingredient data structure. We can then check if we can make more recipes as a result of this.
Interview move: turn each hint into an invariant you can check after every iteration/recursion step.
Step 03

Algorithm Walkthrough

Iteration Checklist

  1. Define state (indices, window, stack, map, DP cell, or recursion frame).
  2. Apply one transition step and update the invariant.
  3. Record answer candidate when condition is met.
  4. Continue until all input is consumed.
Use the first example testcase as your mental trace to verify each transition.
Step 04

Edge Cases

Minimum Input
Single element / shortest valid input
Validate boundary behavior before entering the main loop or recursion.
Duplicates & Repeats
Repeated values / repeated states
Decide whether duplicates should be merged, skipped, or counted explicitly.
Extreme Constraints
Upper-end input sizes
Re-check complexity target against constraints to avoid time-limit issues.
Invalid / Corner Shape
Empty collections, zeros, or disconnected structures
Handle special-case structure before the core algorithm path.
Step 05

Full Annotated Code

Source-backed implementations are provided below for direct study and interview prep.

// Accepted solution for LeetCode #2115: Find All Possible Recipes from Given Supplies
class Solution {
    public List<String> findAllRecipes(
        String[] recipes, List<List<String>> ingredients, String[] supplies) {
        Map<String, List<String>> g = new HashMap<>();
        Map<String, Integer> indeg = new HashMap<>();
        for (int i = 0; i < recipes.length; ++i) {
            for (String v : ingredients.get(i)) {
                g.computeIfAbsent(v, k -> new ArrayList<>()).add(recipes[i]);
            }
            indeg.put(recipes[i], ingredients.get(i).size());
        }
        Deque<String> q = new ArrayDeque<>();
        for (String s : supplies) {
            q.offer(s);
        }
        List<String> ans = new ArrayList<>();
        while (!q.isEmpty()) {
            for (int n = q.size(); n > 0; --n) {
                String i = q.pollFirst();
                for (String j : g.getOrDefault(i, Collections.emptyList())) {
                    indeg.put(j, indeg.get(j) - 1);
                    if (indeg.get(j) == 0) {
                        ans.add(j);
                        q.offer(j);
                    }
                }
            }
        }
        return ans;
    }
}
Step 06

Interactive Study Demo

Use this to step through a reusable interview workflow for this problem.

Press Step or Run All to begin.
Step 07

Complexity Analysis

Time
O(V + E)
Space
O(V + E)

Approach Breakdown

REPEATED DFS
O(V × E) time
O(V) space

Repeatedly find a vertex with no incoming edges, remove it and its outgoing edges, and repeat. Finding the zero-in-degree vertex scans all V vertices, and we do this V times. Removing edges touches E edges total. Without an in-degree array, this gives O(V × E).

TOPOLOGICAL SORT
O(V + E) time
O(V + E) space

Build an adjacency list (O(V + E)), then either do Kahn's BFS (process each vertex once + each edge once) or DFS (visit each vertex once + each edge once). Both are O(V + E). Space includes the adjacency list (O(V + E)) plus the in-degree array or visited set (O(V)).

Shortcut: Process each vertex once + each edge once → O(V + E). Same as BFS/DFS on a graph.
Coach Notes

Common Mistakes

Review these before coding to avoid predictable interview regressions.

Off-by-one on range boundaries

Wrong move: Loop endpoints miss first/last candidate.

Usually fails on: Fails on minimal arrays and exact-boundary answers.

Fix: Re-derive loops from inclusive/exclusive ranges before coding.

Mutating counts without cleanup

Wrong move: Zero-count keys stay in map and break distinct/count constraints.

Usually fails on: Window/map size checks are consistently off by one.

Fix: Delete keys when count reaches zero.