LeetCode #1994 — HARD

The Number of Good Subsets

Break down a hard problem into reliable checkpoints, edge-case handling, and complexity trade-offs.

Solve on LeetCode
The Problem

Problem Statement

You are given an integer array nums. We call a subset of nums good if its product can be represented as a product of one or more distinct prime numbers.

  • For example, if nums = [1, 2, 3, 4]:
    • [2, 3], [1, 2, 3], and [1, 3] are good subsets with products 6 = 2*3, 6 = 2*3, and 3 = 3 respectively.
    • [1, 4] and [4] are not good subsets with products 4 = 2*2 and 4 = 2*2 respectively.

Return the number of different good subsets in nums modulo 109 + 7.

A subset of nums is any array that can be obtained by deleting some (possibly none or all) elements from nums. Two subsets are different if and only if the chosen indices to delete are different.

Example 1:

Input: nums = [1,2,3,4]
Output: 6
Explanation: The good subsets are:
- [1,2]: product is 2, which is the product of distinct prime 2.
- [1,2,3]: product is 6, which is the product of distinct primes 2 and 3.
- [1,3]: product is 3, which is the product of distinct prime 3.
- [2]: product is 2, which is the product of distinct prime 2.
- [2,3]: product is 6, which is the product of distinct primes 2 and 3.
- [3]: product is 3, which is the product of distinct prime 3.

Example 2:

Input: nums = [4,2,3,15]
Output: 5
Explanation: The good subsets are:
- [2]: product is 2, which is the product of distinct prime 2.
- [2,3]: product is 6, which is the product of distinct primes 2 and 3.
- [2,15]: product is 30, which is the product of distinct primes 2, 3, and 5.
- [3]: product is 3, which is the product of distinct prime 3.
- [15]: product is 15, which is the product of distinct primes 3 and 5.

Constraints:

  • 1 <= nums.length <= 105
  • 1 <= nums[i] <= 30
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 are given an integer array nums. We call a subset of nums good if its product can be represented as a product of one or more distinct prime numbers. For example, if nums = [1, 2, 3, 4]: [2, 3], [1, 2, 3], and [1, 3] are good subsets with products 6 = 2*3, 6 = 2*3, and 3 = 3 respectively. [1, 4] and [4] are not good subsets with products 4 = 2*2 and 4 = 2*2 respectively. Return the number of different good subsets in nums modulo 109 + 7. A subset of nums is any array that can be obtained by deleting some (possibly none or all) elements from nums. Two subsets are different if and only if the chosen indices to delete are different.

Baseline thinking

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

Pattern signal: Array · Hash Map · Math · Dynamic Programming · Bit Manipulation

Example 1

[1,2,3,4]

Example 2

[4,2,3,15]

Related Problems

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  • Fair Distribution of Cookies (fair-distribution-of-cookies)
  • Number of Ways to Wear Different Hats to Each Other (number-of-ways-to-wear-different-hats-to-each-other)
Step 02

Core Insight

What unlocks the optimal approach

  • Consider only the numbers which have a good prime factorization.
  • Use brute force to find all possible good subsets and then calculate its frequency in nums.
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
Largest constraint values
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 #1994: The Number of Good Subsets
class Solution {
    public int numberOfGoodSubsets(int[] nums) {
        int[] primes = {2, 3, 5, 7, 11, 13, 17, 19, 23, 29};
        int[] cnt = new int[31];
        for (int x : nums) {
            ++cnt[x];
        }
        final int mod = (int) 1e9 + 7;
        int n = primes.length;
        long[] f = new long[1 << n];
        f[0] = 1;
        for (int i = 0; i < cnt[1]; ++i) {
            f[0] = (f[0] * 2) % mod;
        }
        for (int x = 2; x < 31; ++x) {
            if (cnt[x] == 0 || x % 4 == 0 || x % 9 == 0 || x % 25 == 0) {
                continue;
            }
            int mask = 0;
            for (int i = 0; i < n; ++i) {
                if (x % primes[i] == 0) {
                    mask |= 1 << i;
                }
            }
            for (int state = (1 << n) - 1; state > 0; --state) {
                if ((state & mask) == mask) {
                    f[state] = (f[state] + cnt[x] * f[state ^ mask]) % mod;
                }
            }
        }
        long ans = 0;
        for (int i = 1; i < 1 << n; ++i) {
            ans = (ans + f[i]) % mod;
        }
        return (int) 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(n × m)
Space
O(n × m)

Approach Breakdown

RECURSIVE
O(2ⁿ) time
O(n) space

Pure recursion explores every possible choice at each step. With two choices per state (take or skip), the decision tree has 2ⁿ leaves. The recursion stack uses O(n) space. Many subproblems are recomputed exponentially many times.

DYNAMIC PROGRAMMING
O(n × m) time
O(n × m) space

Each cell in the DP table is computed exactly once from previously solved subproblems. The table dimensions determine both time and space. Look for the state variables — each unique combination of state values is one cell. Often a rolling array can reduce space by one dimension.

Shortcut: Count your DP state dimensions → that’s your time. Can you drop one? That’s your space optimization.
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.

Overflow in intermediate arithmetic

Wrong move: Temporary multiplications exceed integer bounds.

Usually fails on: Large inputs wrap around unexpectedly.

Fix: Use wider types, modular arithmetic, or rearranged operations.

State misses one required dimension

Wrong move: An incomplete state merges distinct subproblems and caches incorrect answers.

Usually fails on: Correctness breaks on cases that differ only in hidden state.

Fix: Define state so each unique subproblem maps to one DP cell.