LeetCode #2466 — MEDIUM

Count Ways To Build Good Strings

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

The Problem

Problem Statement

Given the integers zero, one, low, and high, we can construct a string by starting with an empty string, and then at each step perform either of the following:

Append the character '0' zero times.
Append the character '1' one times.

This can be performed any number of times.

A good string is a string constructed by the above process having a length between low and high (inclusive).

Return the number of different good strings that can be constructed satisfying these properties. Since the answer can be large, return it modulo 10⁹ + 7.

Example 1:

Input: low = 3, high = 3, zero = 1, one = 1
Output: 8
Explanation: 
One possible valid good string is "011". 
It can be constructed as follows: "" -> "0" -> "01" -> "011". 
All binary strings from "000" to "111" are good strings in this example.

Example 2:

Input: low = 2, high = 3, zero = 1, one = 2
Output: 5
Explanation: The good strings are "00", "11", "000", "110", and "011".

Constraints:

1 <= low <= high <= 10⁵
1 <= zero, one <= low

Step 01

Brute Force Baseline

Problem summary: Given the integers zero, one, low, and high, we can construct a string by starting with an empty string, and then at each step perform either of the following: Append the character '0' zero times. Append the character '1' one times. This can be performed any number of times. A good string is a string constructed by the above process having a length between low and high (inclusive). Return the number of different good strings that can be constructed satisfying these properties. Since the answer can be large, return it modulo 109 + 7.

Baseline thinking

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

Pattern signal: Dynamic Programming

Example 1

Example 2

Core Insight

What unlocks the optimal approach

Calculate the number of good strings with length less or equal to some constant x.
Apply dynamic programming using the group size of consecutive zeros and ones.

Interview move: turn each hint into an invariant you can check after every iteration/recursion step.

Step 03

Algorithm Walkthrough

Iteration Checklist

Define state (indices, window, stack, map, DP cell, or recursion frame).
Apply one transition step and update the invariant.
Record answer candidate when condition is met.
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 #2466: Count Ways To Build Good Strings
class Solution {
    private static final int MOD = (int) 1e9 + 7;
    private int[] f;
    private int lo;
    private int hi;
    private int zero;
    private int one;

    public int countGoodStrings(int low, int high, int zero, int one) {
        f = new int[high + 1];
        Arrays.fill(f, -1);
        lo = low;
        hi = high;
        this.zero = zero;
        this.one = one;
        return dfs(0);
    }

    private int dfs(int i) {
        if (i > hi) {
            return 0;
        }
        if (f[i] != -1) {
            return f[i];
        }
        long ans = 0;
        if (i >= lo && i <= hi) {
            ++ans;
        }
        ans += dfs(i + zero) + dfs(i + one);
        ans %= MOD;
        f[i] = (int) ans;
        return f[i];
    }
}

// Accepted solution for LeetCode #2466: Count Ways To Build Good Strings
func countGoodStrings(low int, high int, zero int, one int) int {
	f := make([]int, high+1)
	for i := range f {
		f[i] = -1
	}
	const mod int = 1e9 + 7
	var dfs func(i int) int
	dfs = func(i int) int {
		if i > high {
			return 0
		}
		if f[i] != -1 {
			return f[i]
		}
		ans := 0
		if i >= low && i <= high {
			ans++
		}
		ans += dfs(i+zero) + dfs(i+one)
		ans %= mod
		f[i] = ans
		return ans
	}
	return dfs(0)
}

# Accepted solution for LeetCode #2466: Count Ways To Build Good Strings
class Solution:
    def countGoodStrings(self, low: int, high: int, zero: int, one: int) -> int:
        @cache
        def dfs(i):
            if i > high:
                return 0
            ans = 0
            if low <= i <= high:
                ans += 1
            ans += dfs(i + zero) + dfs(i + one)
            return ans % mod

        mod = 10**9 + 7
        return dfs(0)

// Accepted solution for LeetCode #2466: Count Ways To Build Good Strings
// Rust example auto-generated from java reference.
// Replace the signature and local types with the exact LeetCode harness for this problem.
impl Solution {
    pub fn rust_example() {
        // Port the logic from the reference block below.
    }
}

// Reference (java):
// // Accepted solution for LeetCode #2466: Count Ways To Build Good Strings
// class Solution {
//     private static final int MOD = (int) 1e9 + 7;
//     private int[] f;
//     private int lo;
//     private int hi;
//     private int zero;
//     private int one;
// 
//     public int countGoodStrings(int low, int high, int zero, int one) {
//         f = new int[high + 1];
//         Arrays.fill(f, -1);
//         lo = low;
//         hi = high;
//         this.zero = zero;
//         this.one = one;
//         return dfs(0);
//     }
// 
//     private int dfs(int i) {
//         if (i > hi) {
//             return 0;
//         }
//         if (f[i] != -1) {
//             return f[i];
//         }
//         long ans = 0;
//         if (i >= lo && i <= hi) {
//             ++ans;
//         }
//         ans += dfs(i + zero) + dfs(i + one);
//         ans %= MOD;
//         f[i] = (int) ans;
//         return f[i];
//     }
// }

// Accepted solution for LeetCode #2466: Count Ways To Build Good Strings
function countGoodStrings(low: number, high: number, zero: number, one: number): number {
    const mod = 10 ** 9 + 7;
    const f: number[] = new Array(high + 1).fill(0);
    f[0] = 1;

    for (let i = 1; i <= high; i++) {
        if (i >= zero) f[i] += f[i - zero];
        if (i >= one) f[i] += f[i - one];
        f[i] %= mod;
    }

    const ans = f.slice(low, high + 1).reduce((acc, cur) => acc + cur, 0);

    return ans % mod;
}

Step 06

Interactive Study Demo

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

Custom checkpoints (one per line)

Press Step or Run All to begin.

Step 07

Complexity Analysis

Time

O(n)

Space

O(n)

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.

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.