Longest common subsequence problem
Algorithm · Core
Step through it: Dynamic Programming pattern
Step 1 of 6
Goal: how many ways can you climb 5 stairs taking 1 or 2 steps at a time? You reach stair 5 from stair 4 or stair 3, so ways(5) = ways(4) + ways(3). Working that out naively repeats the same sub-questions many times. Instead write each answer down once.
Step 2 of 6
Base cases: there is 1 way to be on stair 0 (do nothing) and 1 way to reach stair 1.
Step 3 of 6
Stair 2 is reached from stair 1 or stair 0.
Step 4 of 6
Stair 3 comes from stairs 2 and 1.
Step 5 of 6
Stair 4: 3 + 2 = 5. Stair 5: 5 + 3 = 8. Each answer reuses two stored answers, so n stairs take n steps instead of exponential time. Storing sub-answers like this is dynamic programming.
Step 6 of 6
Your turn.
The longest common subsequence (LCS) problem is the problem of finding the longest subsequence common to all sequences in a set of sequences (often just two sequences). It differs from the longest common substring problem: unlike substrings, subsequences are not required to occupy consecutive positions within the original sequences.
Application
The longest common subsequence problem is a classic computer science problem, the basis of data comparison programs such as the diff utility, and has applications in bioinformatics. It is also widely used by revision control systems such as Git for reconciling multiple changes made to a revision-controlled collection of files.
Example
- LCS for input Sequences
ABCDGHandAEDFHRisADHof length 3. - LCS for input Sequences
AGGTABandGXTXAYBisGTABof length 4.
Code
/**
* @param {string[]} set1
* @param {string[]} set2
* @return {string[]}
*/
export default function longestCommonSubsequence(set1, set2) {
// Init LCS matrix.
const lcsMatrix = Array(set2.length + 1).fill(null).map(() => Array(set1.length + 1).fill(null));
// Fill first row with zeros.
for (let columnIndex = 0; columnIndex <= set1.length; columnIndex += 1) {
lcsMatrix[0][columnIndex] = 0;
}
// Fill first column with zeros.
for (let rowIndex = 0; rowIndex <= set2.length; rowIndex += 1) {
lcsMatrix[rowIndex][0] = 0;
}
// Fill rest of the column that correspond to each of two strings.
for (let rowIndex = 1; rowIndex <= set2.length; rowIndex += 1) {
for (let columnIndex = 1; columnIndex <= set1.length; columnIndex += 1) {
if (set1[columnIndex - 1] === set2[rowIndex - 1]) {
lcsMatrix[rowIndex][columnIndex] = lcsMatrix[rowIndex - 1][columnIndex - 1] + 1;
} else {
lcsMatrix[rowIndex][columnIndex] = Math.max(
lcsMatrix[rowIndex - 1][columnIndex],
lcsMatrix[rowIndex][columnIndex - 1],
);
}
}
}
// Calculate LCS based on LCS matrix.
if (!lcsMatrix[set2.length][set1.length]) {
// If the length of largest common string is zero then return empty string.
return [''];
}
const longestSequence = [];
let columnIndex = set1.length;
let rowIndex = set2.length;
while (columnIndex > 0 || rowIndex > 0) {
if (set1[columnIndex - 1] === set2[rowIndex - 1]) {
// Move by diagonal left-top.
longestSequence.unshift(set1[columnIndex - 1]);
columnIndex -= 1;
rowIndex -= 1;
} else if (lcsMatrix[rowIndex][columnIndex] === lcsMatrix[rowIndex][columnIndex - 1]) {
// Move left.
columnIndex -= 1;
} else {
// Move up.
rowIndex -= 1;
}
}
return longestSequence;
}/* eslint-disable no-param-reassign */
/**
* Longest Common Subsequence (LCS) (Recursive Approach).
*
* @param {string} string1
* @param {string} string2
* @return {number}
*/
export default function longestCommonSubsequenceRecursive(string1, string2) {
/**
*
* @param {string} s1
* @param {string} s2
* @return {string} - returns the LCS (Longest Common Subsequence)
*/
const lcs = (s1, s2, memo = {}) => {
if (!s1 || !s2) return '';
if (memo[`${s1}:${s2}`]) return memo[`${s1}:${s2}`];
if (s1[0] === s2[0]) {
return s1[0] + lcs(s1.substring(1), s2.substring(1), memo);
}
const nextLcs1 = lcs(s1.substring(1), s2, memo);
const nextLcs2 = lcs(s1, s2.substring(1), memo);
const nextLongest = nextLcs1.length >= nextLcs2.length ? nextLcs1 : nextLcs2;
memo[`${s1}:${s2}`] = nextLongest;
return nextLongest;
};
return lcs(string1, string2);
}References
Practice
Dynamic Programming
- Climbing StairsEasy
- Fibonacci NumberEasy
- Min Cost Climbing StairsEasy
- Pascal's TriangleEasy
- House RobberMedium
- House Robber IIMedium
- Coin ChangeMedium
- Coin Change IIMedium
- Longest Increasing SubsequenceMedium
- Longest Common SubsequenceMedium
- Word BreakMedium
- Unique PathsMedium
- Unique Paths IIMedium
- Minimum Path SumMedium
- Jump GameMedium
- Partition Equal Subset SumMedium
- Target SumMedium
- Decode WaysMedium
- Maximum Product SubarrayMedium
- Longest Palindromic SubstringMedium
- Palindromic SubstringsMedium
- Best Time to Buy and Sell Stock with CooldownMedium
- TriangleMedium
- Maximal SquareMedium
- Edit DistanceHard
- Regular Expression MatchingHard
- Burst BalloonsHard
- Distinct SubsequencesHard
- Longest Valid ParenthesesHard
- Best Time to Buy and Sell Stock IVHard
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