LeetCode #214 — HARD

Shortest Palindrome

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 a string s. You can convert s to a palindrome by adding characters in front of it.

Return the shortest palindrome you can find by performing this transformation.

Example 1:

Input: s = "aacecaaa"
Output: "aaacecaaa"

Example 2:

Input: s = "abcd"
Output: "dcbabcd"

Constraints:

  • 0 <= s.length <= 5 * 104
  • s consists of lowercase English letters only.
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 a string s. You can convert s to a palindrome by adding characters in front of it. Return the shortest palindrome you can find by performing this transformation.

Baseline thinking

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

Pattern signal: String Matching

Example 1

"aacecaaa"

Example 2

"abcd"

Related Problems

  • Longest Palindromic Substring (longest-palindromic-substring)
  • Find the Index of the First Occurrence in a String (find-the-index-of-the-first-occurrence-in-a-string)
  • Palindrome Pairs (palindrome-pairs)
  • Maximum Deletions on a String (maximum-deletions-on-a-string)
  • Smallest Palindromic Rearrangement I (smallest-palindromic-rearrangement-i)
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
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 #214: Shortest Palindrome
class Solution {
    public String shortestPalindrome(String s) {
        int base = 131;
        int mul = 1;
        int mod = (int) 1e9 + 7;
        int prefix = 0, suffix = 0;
        int idx = 0;
        int n = s.length();
        for (int i = 0; i < n; ++i) {
            int t = s.charAt(i) - 'a' + 1;
            prefix = (int) (((long) prefix * base + t) % mod);
            suffix = (int) ((suffix + (long) t * mul) % mod);
            mul = (int) (((long) mul * base) % mod);
            if (prefix == suffix) {
                idx = i + 1;
            }
        }
        if (idx == n) {
            return s;
        }
        return new StringBuilder(s.substring(idx)).reverse().toString() + s;
    }
}
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

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.