Longest Palindromic Subsequence (LPS)
Longest Palindromic Subsequence (LPS)
Section titled “Longest Palindromic Subsequence (LPS)”🎯 Problem Statement
Section titled “🎯 Problem Statement”Given a string s, find the length of the longest subsequence that is a palindrome. Unlike substring, a subsequence does NOT require contiguous characters — you can skip characters.
Example:
Input: s = "bbbab"Output: 4
Explanation: The longest palindromic subsequence is "bbbb" (length 4). Characters at positions 0,1,3,4: b, b, b, b.Input: s = "cbbd"Output: 2
Explanation: The longest palindromic subsequence is "bb" (length 2).🆚 Substring vs Subsequence
Section titled “🆚 Substring vs Subsequence”String: "babad"
Longest Palindromic SUBSTRING: "bab" or "aba" (CONTIGUOUS) positions 0-2 or 1-3
Longest Palindromic SUBSEQUENCE: "babab" or "badab" (NOT contiguous) positions 0,1,3,4,2 or 0,2,4,3,1
Subsequence can skip characters → more freedom → potentially longer result🧠 Step 1: Identify the State
Section titled “🧠 Step 1: Identify the State”dp[i][j] = length of longest palindromic subsequence in s[i..j] (inclusive)This is an interval DP problem — we solve for all substrings s[i..j] and combine them.
🔗 Step 2: Write the Recurrence
Section titled “🔗 Step 2: Write the Recurrence”If s[i] === s[j]: dp[i][j] = dp[i+1][j-1] + 2 // Match both ends → LPS extends by 2Else: dp[i][j] = max(dp[i+1][j], dp[i][j-1]) // Skip one end, take bestIntuition:
- If the two ends match, they can both be part of the palindrome → add 2 to the inner LPS
- If they don’t match, either skip the left character or the right character — take whichever gives the longer LPS
🏁 Step 3: Define Base Cases
Section titled “🏁 Step 3: Define Base Cases”dp[i][i] = 1 (single character is a palindrome of length 1)dp[i][j] = 0 if i > j (empty substring — no characters)💻 Approach 1: Interval DP — O(n²) time, O(n²) space
Section titled “💻 Approach 1: Interval DP — O(n²) time, O(n²) space”function longestPalindromeSubseq(s) { const n = s.length; const dp = Array.from({ length: n }, () => new Array(n).fill(0));
// Base: single characters for (let i = 0; i < n; i++) dp[i][i] = 1;
// Fill by length (increasing gap size) for (let len = 2; len <= n; len++) { for (let i = 0; i <= n - len; i++) { const j = i + len - 1;
if (s[i] === s[j]) { dp[i][j] = dp[i + 1][j - 1] + 2; } else { dp[i][j] = Math.max(dp[i + 1][j], dp[i][j - 1]); } } }
return dp[0][n - 1];}
console.log(longestPalindromeSubseq("bbbab")); // 4console.log(longestPalindromeSubseq("cbbd")); // 2DP Table Walkthrough: “bbbab”
Section titled “DP Table Walkthrough: “bbbab””n=5, s = b b b a b
Step 1: Initialize diagonals (i=j → dp=1) 0:b 1:b 2:b 3:a 4:b0:b 1 ? ? ? ?1:b 1 ? ? ?2:b 1 ? ?3:a 1 ?4:b 1
Step 2: Fill len=2 (gap=1) dp[0][1]: b===b → dp[1][0]+2 = 0+2 = 2 dp[1][2]: b===b → dp[2][1]+2 = 0+2 = 2 dp[2][3]: b!==a → max(dp[3][3]=1, dp[2][2]=1) = 1 dp[3][4]: a!==b → max(dp[4][4]=1, dp[3][3]=1) = 1
Step 3: Fill len=3 (gap=2) dp[0][2]: b===b → dp[1][1]+2 = 1+2 = 3 dp[1][3]: b!==a → max(dp[2][3]=1, dp[1][2]=2) = 2 dp[2][4]: b===b → dp[3][3]+2 = 1+2 = 3
Step 4: Fill len=4 (gap=3) dp[0][3]: b!==a → max(dp[1][3]=2, dp[0][2]=3) = 3 dp[1][4]: b===b → dp[2][3]+2 = 1+2 = 3
Step 5: Fill len=5 (gap=4) dp[0][4]: b===b → dp[1][3]+2 = 2+2 = 4 ← ANSWER
Answer: dp[0][4] = 4 ("b-b-b-b" using characters 0,1,3,4)💻 Approach 2: Space-Optimized (Two Rows) — O(n²) time, O(n) space
Section titled “💻 Approach 2: Space-Optimized (Two Rows) — O(n²) time, O(n) space”function longestPalindromeSubseq(s) { const n = s.length; const dp = new Array(n).fill(1); // Current row (diagonal)
for (let len = 2; len <= n; len++) { let prev = 0; // dp[i+1][j-1] for current j for (let i = 0; i <= n - len; i++) { const j = i + len - 1; const temp = dp[i]; // Save dp[i][j-1] before overwrite
if (s[i] === s[j]) { // dp[i][j] = dp[i+1][j-1] + 2 // dp[i] currently holds dp[i+1][j-1]... wait, let me rethink. // Actually, this optimization is complex. Let's keep the simpler version. }
prev = temp; } }
// For simplicity, stick with the 2D version or use reversed string trick}
// Alternative O(n) space using LCS with reversed string:function longestPalindromeSubseq(s) { const rev = s.split('').reverse().join(''); return longestCommonSubsequence(s, rev);}
function longestCommonSubsequence(a, b) { const n = b.length; let prev = new Array(n + 1).fill(0);
for (let i = 1; i <= a.length; i++) { const curr = new Array(n + 1).fill(0); for (let j = 1; j <= n; j++) { if (a[i - 1] === b[j - 1]) { curr[j] = prev[j - 1] + 1; } else { curr[j] = Math.max(prev[j], curr[j - 1]); } } prev = curr; }
return prev[n];}
console.log(longestPalindromeSubseq("bbbab")); // 4Why this works: The longest palindromic subsequence of s is exactly the Longest Common Subsequence between s and its reverse!
s = "bbbab"rev = "babbb"LCS(s, rev) = "bbbb" (length 4) — which is the LPS! ✓A palindrome reads the same forward and backward, so LPS(s) = LCS(s, rev(s)).
🎯 Variation: Minimum Insertions to Make Palindrome
Section titled “🎯 Variation: Minimum Insertions to Make Palindrome”Problem: Find the minimum number of characters to insert to make a string palindrome.
function minInsertions(s) { const n = s.length; const lps = longestPalindromeSubseq(s); return n - lps; // Insert the missing characters to make the palindrome}
console.log(minInsertions("mbadm")); // 2// "mbadm" → "mbdadbm" (insert 'b' and 'd')// LPS length = 3 ("mam" or "mdm")// n - LPS = 5 - 3 = 2 insertions📊 Complexity Summary
Section titled “📊 Complexity Summary”| Approach | Time | Space | Notes |
|---|---|---|---|
| Interval DP (2D) | O(n²) | O(n²) | Full table — easy to understand |
| LCS with reverse | O(n²) | O(n) | ✅ Best — reuses LCS optimization |
| Min Insertions | O(n²) | O(n) | n - LPS |
🔑 Key Takeaways
Section titled “🔑 Key Takeaways”- Interval DP: Fill table by length (gap size), not by index
- LPS vs Palindromic Substring: Subsequence can skip characters → longer results
- LPS = LCS(s, rev(s)): One of the most elegant algorithm relationships!
- Min insertions to make palindrome:
n - LPS— insert the missing characters that aren’t already paired in the LPS - Fill order matters: Always compute shorter intervals before longer ones (increase gap)
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