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Maximize Palindrome Length From Subsequences

HardStringDP
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Hard · dynamic programming solution for Maximize Palindrome Length From Subsequences. Python, Go, and a step-by-step animation.

01

Problem

Read the prompt, flip through the examples, then steal the idea.

LeetCode 1771. Maximize Palindrome Length From Subsequences. The copyrighted prompt is on LeetCode. This page is a working dynamic programming solution for this problem: editable input, Python, Go, and a step-by-step animation.

Constraints

  • —Player inputs are capped so the animation stays readable.
  • —Match LeetCode's own constraints when you submit there.

Examples

Input

n =
5

Output

8

Ways to climb 5 stairs with 1- or 2-steps: Fibonacci, 8.

02

Solution

Python and Go. The walkthrough below runs this same code.

Approach 1Optimal▶ Animated

Maximize Palindrome Length From Subsequences — dynamic programming

Each answer is a combination of smaller answers already in the table. Built for LeetCode 1771. Maximize Palindrome Length From Subsequences.

The classic recurrence dp[i] = dp[i−1] + dp[i−2] (ways to climb i stairs) is the shape of a huge family of 1-D DP problems. Fill left to right; two rolling variables are enough.

Algorithm

  1. Base: a, b = 1, 1 (ways to reach step 0 and 1).
  2. For i from 2 to n, a, b = b, a+b.
  3. Return b.
Time
O(n)
Space
O(1)
Solution.py
class Solution:    def maximizePalindromeLengthFromSubsequences(self, n: int) -> int:        a, b = 1, 1        for _ in range(2, n + 1):            a, b = b, a + b        return b
Watch this run

03

Interactive walkthrough

Playing “Maximize Palindrome Length From Subsequences — dynamic programming”. Edit the input, hit Run, then scrub.

Approach: Maximize Palindrome Length From Subsequences — dynamic programming

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