LeetCode #2999 — HARD

Count the Number of Powerful Integers

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 three integers start, finish, and limit. You are also given a 0-indexed string s representing a positive integer.

A positive integer x is called powerful if it ends with s (in other words, s is a suffix of x) and each digit in x is at most limit.

Return the total number of powerful integers in the range [start..finish].

A string x is a suffix of a string y if and only if x is a substring of y that starts from some index (including 0) in y and extends to the index y.length - 1. For example, 25 is a suffix of 5125 whereas 512 is not.

Example 1:

Input: start = 1, finish = 6000, limit = 4, s = "124"
Output: 5
Explanation: The powerful integers in the range [1..6000] are 124, 1124, 2124, 3124, and, 4124. All these integers have each digit <= 4, and "124" as a suffix. Note that 5124 is not a powerful integer because the first digit is 5 which is greater than 4.
It can be shown that there are only 5 powerful integers in this range.

Example 2:

Input: start = 15, finish = 215, limit = 6, s = "10"
Output: 2
Explanation: The powerful integers in the range [15..215] are 110 and 210. All these integers have each digit <= 6, and "10" as a suffix.
It can be shown that there are only 2 powerful integers in this range.

Example 3:

Input: start = 1000, finish = 2000, limit = 4, s = "3000"
Output: 0
Explanation: All integers in the range [1000..2000] are smaller than 3000, hence "3000" cannot be a suffix of any integer in this range.

Constraints:

  • 1 <= start <= finish <= 1015
  • 1 <= limit <= 9
  • 1 <= s.length <= floor(log10(finish)) + 1
  • s only consists of numeric digits which are at most limit.
  • s does not have leading zeros.
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 three integers start, finish, and limit. You are also given a 0-indexed string s representing a positive integer. A positive integer x is called powerful if it ends with s (in other words, s is a suffix of x) and each digit in x is at most limit. Return the total number of powerful integers in the range [start..finish]. A string x is a suffix of a string y if and only if x is a substring of y that starts from some index (including 0) in y and extends to the index y.length - 1. For example, 25 is a suffix of 5125 whereas 512 is not.

Baseline thinking

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

Pattern signal: Math · Dynamic Programming

Example 1

1
6000
4
"124"

Example 2

15
215
6
"10"

Example 3

1000
2000
4
"3000"

Related Problems

  • Powerful Integers (powerful-integers)
  • Numbers With Repeated Digits (numbers-with-repeated-digits)
Step 02

Core Insight

What unlocks the optimal approach

  • We can use digit DP to count powerful integers in the range <code>[1, x]</code>.
  • Let <code>dp[i][j]</code> be the number of integers that have <code>i</code> digits (with allowed leading 0s) and <code>j</code> refers to the comparison between the current number and the prefix of <code>x</code>, <code>j == 0</code> if the i-digit number formed currently is identical to the leftmost <code>i</code> digits of <code>x</code>, else if <code>j ==1</code> it means the i-digit number is smaller than the leftmost <code>i</code> digits of <code>x</code>.
  • The answer is <code>count[finish] - count[start - 1]</code>, where <code>count[i]</code> refers to the number of powerful integers in the range <code>[1..i]</code>.
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 #2999: Count the Number of Powerful Integers
class Solution {
    private String s;
    private String t;
    private Long[] f;
    private int limit;

    public long numberOfPowerfulInt(long start, long finish, int limit, String s) {
        this.s = s;
        this.limit = limit;
        t = String.valueOf(start - 1);
        f = new Long[20];
        long a = dfs(0, true);
        t = String.valueOf(finish);
        f = new Long[20];
        long b = dfs(0, true);
        return b - a;
    }

    private long dfs(int pos, boolean lim) {
        if (t.length() < s.length()) {
            return 0;
        }
        if (!lim && f[pos] != null) {
            return f[pos];
        }
        if (t.length() - pos == s.length()) {
            return lim ? (s.compareTo(t.substring(pos)) <= 0 ? 1 : 0) : 1;
        }
        int up = lim ? t.charAt(pos) - '0' : 9;
        up = Math.min(up, limit);
        long ans = 0;
        for (int i = 0; i <= up; ++i) {
            ans += dfs(pos + 1, lim && i == (t.charAt(pos) - '0'));
        }
        if (!lim) {
            f[pos] = ans;
        }
        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(log M × D)
Space
O(log 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.

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.