#2048

Medium

auto_awesomeBacktracking search with pruning

LeetCode Problem Workspace

Next Greater Numerically Balanced Number

Find the smallest numerically balanced number greater than a given integer using backtracking search and pruning.

hash table math backtracking counting enumeration

Problem Statement

A numerically balanced number is one where, for each digit d, the number contains exactly d occurrences of d. For example, 22 is balanced because the digit 2 appears exactly twice.

Given an integer n, your task is to return the smallest numerically balanced number strictly greater than n. You must ensure that the digits of the resulting number satisfy the balance condition and that it is the smallest number greater than n.

Examples

Example 1

Input: n = 1

Output: 22

22 is numerically balanced since:

The digit 2 occurs 2 times. It is also the smallest numerically balanced number strictly greater than 1.

Example 2

Input: n = 1000

Output: 1333

1333 is numerically balanced since:

The digit 1 occurs 1 time.
The digit 3 occurs 3 times. It is also the smallest numerically balanced number strictly greater than 1000. Note that 1022 cannot be the answer because 0 appeared more than 0 times.

Example 3

Input: n = 3000

Output: 3133

3133 is numerically balanced since:

The digit 1 occurs 1 time.
The digit 3 occurs 3 times. It is also the smallest numerically balanced number strictly greater than 3000.

Constraints

0 <= n <= 106

Solution Approach

Backtracking with Pruning

The most efficient way to solve this problem is by applying backtracking, which explores all possible combinations of digits. Pruning helps discard combinations that can't form a valid numerically balanced number, saving computation time.

Enumeration and Counting Digits

By counting the occurrences of each digit, we can systematically check which numbers are numerically balanced. Starting from the smallest number greater than n, we check if the digits match the required frequency before confirming the result.

Hash Table for Efficient Counting

Using a hash table to store the frequency of each digit helps streamline the process of validation. It allows for quick checks of digit counts as the algorithm narrows down potential candidates for the next numerically balanced number.

Complexity Analysis

Metric	Value
Time	Depends on the final approach
Space	Depends on the final approach

The time and space complexity of this problem depend on the chosen backtracking approach and how efficiently the search space is pruned. If the pruning step is effective, the time complexity can be reduced considerably, making the approach scalable within the given constraints.

What Interviewers Usually Probe

Ability to optimize a backtracking solution using pruning.
Understanding of how digit counts relate to number structure.
Proficiency in using hash tables for counting occurrences.

Common Pitfalls or Variants

Common pitfalls

Failing to prune the search space effectively, leading to inefficient exploration.
Incorrectly assuming that a number is balanced without verifying digit counts.
Overcomplicating the counting of digits, missing simple checks that could speed up the solution.

Follow-up variants

Implementing the solution without using backtracking.
Optimizing the counting process by using other data structures besides a hash table.
Considering a more brute force approach without pruning.

FAQ

What is a numerically balanced number?

A numerically balanced number is one in which, for each digit d, the number contains exactly d occurrences of d. For example, 22 is balanced because 2 appears twice.

How does backtracking help solve this problem?

Backtracking allows you to explore all possible combinations of digits to find the smallest valid numerically balanced number greater than n. The search can be pruned to eliminate invalid candidates early, making the approach more efficient.

What are the main challenges of the Next Greater Numerically Balanced Number problem?

The primary challenge is finding a numerically balanced number efficiently, which requires understanding how digits and their counts relate, and implementing an efficient backtracking solution with pruning to avoid unnecessary calculations.

Can the solution be optimized without backtracking?

While backtracking is the most natural approach, the problem can be optimized using other methods like enumeration and digit counting, though backtracking remains efficient with pruning.

What is the role of hash tables in this problem?

Hash tables help efficiently count the occurrences of each digit, allowing for fast validation of potential numerically balanced numbers, reducing the time spent checking candidates.

terminal

Solution

Solution 1: Enumeration

We note that the range of $n$ in the problem is $[0, 10^6]$, and one of the balanced numbers greater than $10^6$ is $1224444$. Therefore, we directly enumerate $x \in [n + 1, ..]$ and then judge whether $x$ is a balanced number. The enumerated $x$ will definitely not exceed $1224444$.

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class Solution:
    def nextBeautifulNumber(self, n: int) -> int:
        for x in count(n + 1):
            y = x
            cnt = [0] * 10
            while y:
                y, v = divmod(y, 10)
                cnt[v] += 1
            if all(v == 0 or i == v for i, v in enumerate(cnt)):
                return x

Continue Practicing

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