LeetCode 75 C++ | Step-by-Step Solutions (LAN Academy) - LeetCode 1143: Longest Common Subsequence — DP Explained (2D → O(n) Space)

LeetCode 1143. Longest Common Subsequence (LCS) — find the length of the longest subsequence common to two strings. In this video you’ll learn: The DP state and recurrence for LCS How the 2D DP table works (match vs skip) How to optimize space from O(m·n) to O(n) (rolling array) Time complexity and common pitfalls DP Idea Let dp[i][j] be the LCS length for prefixes text1[0..i-1] and text2[0..j-1]. Transition: If text1[i-1] == text2[j-1]: dp[i][j] = dp[i-1][j-1] + 1 Else: dp[i][j] = max(dp[i-1][j], dp[i][j-1]) Complexity Time: O(m·n) Space: O(m·n) (or O(n) optimized) If this helped, subscribe for more clean LeetCode explanations (DP, Greedy, Graphs). Leetcode playlist: https://www.youtube.com/playlist?list=PLF0G-7pZcOza_jyAxwMpa5KnK3vST5smK LAN Academy: youtube.com/channel/UCdKpV0t_sLv9gUsxHOYrt7g?sub_confirmation=1 #LeetCode #DynamicProgramming #CodingInterview #CPlusPlus #leetcodecoding #leetcodesolution #leetcodechallenge #faang #fang #computerscience #cs #codingtiktok #coding #codinglife #boostofhope #fyp #codinginterview #softwareengineer #swe #tech #foryou #python #learntocode #google #techtok #programming #algorithms #learntocode @reper #foryou #viral #reper #urmaritori #for