LeetCode #2301 — HARD

Match Substring After Replacement

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 two strings s and sub. You are also given a 2D character array mappings where mappings[i] = [oldi, newi] indicates that you may perform the following operation any number of times:

  • Replace a character oldi of sub with newi.

Each character in sub cannot be replaced more than once.

Return true if it is possible to make sub a substring of s by replacing zero or more characters according to mappings. Otherwise, return false.

A substring is a contiguous non-empty sequence of characters within a string.

Example 1:

Input: s = "fool3e7bar", sub = "leet", mappings = [["e","3"],["t","7"],["t","8"]]
Output: true
Explanation: Replace the first 'e' in sub with '3' and 't' in sub with '7'.
Now sub = "l3e7" is a substring of s, so we return true.

Example 2:

Input: s = "fooleetbar", sub = "f00l", mappings = [["o","0"]]
Output: false
Explanation: The string "f00l" is not a substring of s and no replacements can be made.
Note that we cannot replace '0' with 'o'.

Example 3:

Input: s = "Fool33tbaR", sub = "leetd", mappings = [["e","3"],["t","7"],["t","8"],["d","b"],["p","b"]]
Output: true
Explanation: Replace the first and second 'e' in sub with '3' and 'd' in sub with 'b'.
Now sub = "l33tb" is a substring of s, so we return true.

Constraints:

  • 1 <= sub.length <= s.length <= 5000
  • 0 <= mappings.length <= 1000
  • mappings[i].length == 2
  • oldi != newi
  • s and sub consist of uppercase and lowercase English letters and digits.
  • oldi and newi are either uppercase or lowercase English letters or digits.
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 two strings s and sub. You are also given a 2D character array mappings where mappings[i] = [oldi, newi] indicates that you may perform the following operation any number of times: Replace a character oldi of sub with newi. Each character in sub cannot be replaced more than once. Return true if it is possible to make sub a substring of s by replacing zero or more characters according to mappings. Otherwise, return false. A substring is a contiguous non-empty sequence of characters within a string.

Baseline thinking

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

Pattern signal: Array · Hash Map · String Matching

Example 1

"fool3e7bar"
"leet"
[["e","3"],["t","7"],["t","8"]]

Example 2

"fooleetbar"
"f00l"
[["o","0"]]

Example 3

"Fool33tbaR"
"leetd"
[["e","3"],["t","7"],["t","8"],["d","b"],["p","b"]]

Related Problems

  • Design Add and Search Words Data Structure (design-add-and-search-words-data-structure)
  • Number of Subarrays That Match a Pattern II (number-of-subarrays-that-match-a-pattern-ii)
Step 02

Core Insight

What unlocks the optimal approach

  • Enumerate all substrings of s with the same length as sub, and compare each substring to sub for equality.
  • How can you quickly tell if a character of s can result from replacing the corresponding character in sub?
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 #2301: Match Substring After Replacement
class Solution {
    public boolean matchReplacement(String s, String sub, char[][] mappings) {
        Map<Character, Set<Character>> d = new HashMap<>();
        for (var e : mappings) {
            d.computeIfAbsent(e[0], k -> new HashSet<>()).add(e[1]);
        }
        int m = s.length(), n = sub.length();
        for (int i = 0; i < m - n + 1; ++i) {
            boolean ok = true;
            for (int j = 0; j < n && ok; ++j) {
                char a = s.charAt(i + j), b = sub.charAt(j);
                if (a != b && !d.getOrDefault(b, Collections.emptySet()).contains(a)) {
                    ok = false;
                }
            }
            if (ok) {
                return true;
            }
        }
        return false;
    }
}
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(m × n)
Space
O(C^2)

Approach Breakdown

BRUTE FORCE
O(n × m) time
O(1) space

At each of the n starting positions in the text, compare up to m characters with the pattern. If a mismatch occurs, shift by one and restart. Worst case (e.g., searching "aab" in "aaaa...a") checks m characters at nearly every position: O(n × m).

KMP / Z-ALGO
O(n + m) time
O(m) space

KMP and Z-algorithm preprocess the pattern in O(m) to build a failure/Z-array, then scan the text in O(n) — never backtracking. Total: O(n + m). Rabin-Karp uses rolling hashes for O(n + m) expected time. All beat the O(n × m) brute force of checking every position.

Shortcut: Preprocessing avoids backtracking → O(n + m). The failure function is the key insight.
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.