LeetCode #2522 — MEDIUM

Partition String Into Substrings With Values at Most K

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

Solve on LeetCode
The Problem

Problem Statement

You are given a string s consisting of digits from 1 to 9 and an integer k.

A partition of a string s is called good if:

  • Each digit of s is part of exactly one substring.
  • The value of each substring is less than or equal to k.

Return the minimum number of substrings in a good partition of s. If no good partition of s exists, return -1.

Note that:

  • The value of a string is its result when interpreted as an integer. For example, the value of "123" is 123 and the value of "1" is 1.
  • A substring is a contiguous sequence of characters within a string.

Example 1:

Input: s = "165462", k = 60
Output: 4
Explanation: We can partition the string into substrings "16", "54", "6", and "2". Each substring has a value less than or equal to k = 60.
It can be shown that we cannot partition the string into less than 4 substrings.

Example 2:

Input: s = "238182", k = 5
Output: -1
Explanation: There is no good partition for this string.

Constraints:

  • 1 <= s.length <= 105
  • s[i] is a digit from '1' to '9'.
  • 1 <= k <= 109
Patterns Used
📊Dynamic Programming→🎯Greedy→

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 a string s consisting of digits from 1 to 9 and an integer k. A partition of a string s is called good if: Each digit of s is part of exactly one substring. The value of each substring is less than or equal to k. Return the minimum number of substrings in a good partition of s. If no good partition of s exists, return -1. Note that: The value of a string is its result when interpreted as an integer. For example, the value of "123" is 123 and the value of "1" is 1. A substring is a contiguous sequence of characters within a string.

Baseline thinking

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

Pattern signal: Dynamic Programming · Greedy

Example 1

"165462"
60

Example 2

"238182"
5
Step 02

Core Insight

What unlocks the optimal approach

  • No official hints in dataset. Start from constraints and look for a monotonic or reusable state.
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 #2522: Partition String Into Substrings With Values at Most K
class Solution {
    private Integer[] f;
    private int n;
    private String s;
    private int k;
    private int inf = 1 << 30;

    public int minimumPartition(String s, int k) {
        n = s.length();
        f = new Integer[n];
        this.s = s;
        this.k = k;
        int ans = dfs(0);
        return ans < inf ? ans : -1;
    }

    private int dfs(int i) {
        if (i >= n) {
            return 0;
        }
        if (f[i] != null) {
            return f[i];
        }
        int res = inf;
        long v = 0;
        for (int j = i; j < n; ++j) {
            v = v * 10 + (s.charAt(j) - '0');
            if (v > k) {
                break;
            }
            res = Math.min(res, dfs(j + 1));
        }
        return f[i] = res + 1;
    }
}
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)
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.

Using greedy without proof

Wrong move: Locally optimal choices may fail globally.

Usually fails on: Counterexamples appear on crafted input orderings.

Fix: Verify with exchange argument or monotonic objective before committing.

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