LeetCode #866 — MEDIUM

Prime Palindrome

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

Solve on LeetCode
The Problem

Problem Statement

Given an integer n, return the smallest prime palindrome greater than or equal to n.

An integer is prime if it has exactly two divisors: 1 and itself. Note that 1 is not a prime number.

  • For example, 2, 3, 5, 7, 11, and 13 are all primes.

An integer is a palindrome if it reads the same from left to right as it does from right to left.

  • For example, 101 and 12321 are palindromes.

The test cases are generated so that the answer always exists and is in the range [2, 2 * 108].

Example 1:

Input: n = 6
Output: 7

Example 2:

Input: n = 8
Output: 11

Example 3:

Input: n = 13
Output: 101

Constraints:

  • 1 <= n <= 108

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: Given an integer n, return the smallest prime palindrome greater than or equal to n. An integer is prime if it has exactly two divisors: 1 and itself. Note that 1 is not a prime number. For example, 2, 3, 5, 7, 11, and 13 are all primes. An integer is a palindrome if it reads the same from left to right as it does from right to left. For example, 101 and 12321 are palindromes. The test cases are generated so that the answer always exists and is in the range [2, 2 * 108].

Baseline thinking

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

Pattern signal: Math

Example 1

6

Example 2

8

Example 3

13

Related Problems

  • Sum of k-Mirror Numbers (sum-of-k-mirror-numbers)
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 #866: Prime Palindrome
class Solution {
    public int primePalindrome(int n) {
        while (true) {
            if (reverse(n) == n && isPrime(n)) {
                return n;
            }
            if (n > 10000000 && n < 100000000) {
                n = 100000000;
            }
            ++n;
        }
    }

    private boolean isPrime(int x) {
        if (x < 2) {
            return false;
        }
        for (int v = 2; v * v <= x; ++v) {
            if (x % v == 0) {
                return false;
            }
        }
        return true;
    }

    private int reverse(int x) {
        int res = 0;
        while (x != 0) {
            res = res * 10 + x % 10;
            x /= 10;
        }
        return res;
    }
}
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(log n)
Space
O(1)

Approach Breakdown

ITERATIVE
O(n) time
O(1) space

Simulate the process step by step — multiply n times, check each number up to n, or iterate through all possibilities. Each step is O(1), but doing it n times gives O(n). No extra space needed since we just track running state.

MATH INSIGHT
O(log n) time
O(1) space

Math problems often have a closed-form or O(log n) solution hidden behind an O(n) simulation. Modular arithmetic, fast exponentiation (repeated squaring), GCD (Euclidean algorithm), and number theory properties can dramatically reduce complexity.

Shortcut: Look for mathematical properties that eliminate iteration. Repeated squaring → O(log n). Modular arithmetic avoids overflow.
Coach Notes

Common Mistakes

Review these before coding to avoid predictable interview regressions.

Overflow in intermediate arithmetic

Wrong move: Temporary multiplications exceed integer bounds.

Usually fails on: Large inputs wrap around unexpectedly.

Fix: Use wider types, modular arithmetic, or rearranged operations.