What is the brute force approach and complexity for Longest Repeating Character Replacement?

For each start, grow right while length − maxFreq ≤ k with time complexity O(N²) and space complexity O(1).

What is the optimal approach and complexity for Longest Repeating Character Replacement?

Keep the biggest count ever seen; the window slides or grows, never shrinks with optimal time complexity O(N) and space complexity O(1).

How do you identify the pattern for Longest Repeating Character Replacement?

Look for key signals in the prompt: "longest substring" indicates Contiguous range, maximise length → variable window.; "change at most k characters" indicates A budget of k "bad" letters inside the window.; "all letters the same" indicates The "good" letters are the most common letter; the bad ones are everything else → need counts and the max count..

Longest Repeating Character Replacement

Medium·LeetCode #424 Pro OnlySlight Variation

LeetCode

Problem Statement

You are given a string s of uppercase letters and a number k.

You may pick at most k positions in the string and change the letter at each one to any other uppercase letter.

After your changes, return the length of the longest substring in which every letter is the same.

Input

A string s of uppercase English letters and an integer k.

Output

One integer: the longest run of one repeated letter you can make with at most k changes.

Example 1

Input: s = "ABAB", k = 2

Output: 4

Change both A's to B (or both B's to A) and the whole string is one letter.

Example 2

Input: s = "AABABBA", k = 1

Output: 4

Change the A at index 3 to B to get "AABBBBA". The block "BBBB" has length 4. Other choices also give 4.

Constraints

• 1 ≤ s.length ≤ 10^5
• s contains only uppercase English letters
• 0 ≤ k ≤ s.length

Pattern Recognition Clues

Recognising these key signals in the problem description immediately identifies the pattern:

“longest substring”

→ Contiguous range, maximise length → variable window.

“change at most k characters”

→ A budget of k "bad" letters inside the window.

“all letters the same”

→ The "good" letters are the most common letter; the bad ones are everything else → need counts and the max count.

Approach Complexity Ladder

Brute Force — every start, grow while fixableO(N²)O(1)

For each start, grow right while length − maxFreq ≤ k

Better — sliding window, recount the max over 26 lettersO(26 · N)O(1)

Grow right; while length − (max of 26 counts) > k, shrink left

Optimal — sliding window with a never-decreasing maxFreqO(N)O(1)

Keep the biggest count ever seen; the window slides or grows, never shrinks

Brute Force Intuition

Try every start. Grow the end one letter at a time, keeping counts and the most common count maxFreq of the substring. The substring needs length − maxFreq changes. While that is ≤ k, it is a candidate. Once it is > k, stop for this start.

Recognition rule for this sub-pattern:

Validity depends on the counts of things inside the window (distinct types, most frequent char, required letters). Keep a count map that updates on every add/remove.

Remember this · Sliding Window

Trigger

Contiguous subarray / substring + longest, shortest, maximum, count, or "of size K".

Think

Can I keep a valid range and move it, instead of checking every subarray from scratch?

Key question

When is my current window valid?

Brute2Best Pro Content

Unlock Complete Visual Walkthrough & Optimal C++

The interactive animated dry-run player, optimized C++ implementation, state invariant derivations, and edge-case breakdown for this question are part of Brute2Best Pro. The anchor question of every sub-pattern is completely free.

All 440 visual dry runs & algorithms

Interactive step-by-step playback with pointer & array state transitions

52 comprehensive LLD chapters

Interactive UML diagrams, real-world stories (Food Delivery, Trading, RPG), and practice solutions

40 C++ basics labs & hardware memory models

Vtables, byte layouts, assembly insights, and multi-threading race conditions

Production-ready C++23 source code

Modern idioms, clean contracts, time/space complexity derivations, and interview takeaways

Create Free Account Log in

First anchor question per pattern, Part 1 LLD & Module 1 C++ basics are 100% Free forever.

Beyond The LeetCode Problem

How this concept connects to System Design & C++ Systems

Bridge DSA with production engineering

Top engineering interviews test whether you understand how algorithms like Sliding Window translate into production architectures and low-level memory mechanics.

LLD Machine Coding

Sliding Window Rate Limiter

Implementing token bucket and sliding log request throttlers in real backend services.

Explore Track

C++ Internals

Stack vs Heap, Placement new & Layout

Cache line locality when traversing contiguous windows in memory.

Explore Track

Longest Substring with K Unique Characters Maximize the Confusion of an Exam