2D Dynamic Programming

Table of Contents

• 62. Unique Paths (Medium)
• 1143. Longest Common Subsequence (Medium)

62. Unique Paths

• LeetCode | LeetCode CH (Medium)

• Tags: math, dynamic programming, combinatorics

• Count the number of unique paths to reach the bottom-right corner of a m x n grid.

62. Unique Paths - Python Solution

# DP - 2D
def uniquePaths(m: int, n: int) -> int:
    if m == 1 or n == 1:
        return 1

    dp = [[1] * n for _ in range(m)]

    for i in range(1, m):
        for j in range(1, n):
            dp[i][j] = dp[i - 1][j] + dp[i][j - 1]

    return dp[-1][-1]


print(uniquePaths(m=3, n=7))  # 28
# [[1, 1, 1,  1,  1,  1,  1],
#  [1, 2, 3,  4,  5,  6,  7],
#  [1, 3, 6, 10, 15, 21, 28]]

62. Unique Paths - C++ Solution

#include <iostream>
#include <vector>
using namespace std;

int uniquePaths(int m, int n) {
    vector dp(m, vector<int>(n, 1));

    for (int i = 1; i < m; i++) {
        for (int j = 1; j < n; j++) {
            dp[i][j] = dp[i - 1][j] + dp[i][j - 1];
        }
    }

    return dp[m - 1][n - 1];
}

int main() {
    int m = 3, n = 7;
    cout << uniquePaths(m, n) << endl;  // 28
    return 0;
}

1143. Longest Common Subsequence

• LeetCode | LeetCode CH (Medium)

• Tags: string, dynamic programming

1143. Longest Common Subsequence - Python Solution

# DP (LCS)
def longestCommonSubsequence(text1: str, text2: str) -> int:
    """
    Computes the length of the longest common subsequence between two strings.

    dp[i][j]: the length of the LCS between text1[:i] and text2[:j].

    Args:
        text1 (str): The first string.
        text2 (str): The second string.

    Returns:
        int: The length of the longest common subsequence.

    Example:
        >>> longestCommonSubsequence("abcde", "ace")
        3
        >>> longestCommonSubsequence("abc", "abc")
        3
        >>> longestCommonSubsequence("abc", "def")
        0
    """

    m, n = len(text1), len(text2)
    dp = [[0] * (n + 1) for _ in range(m + 1)]

    for i in range(1, m + 1):
        for j in range(1, n + 1):
            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])

    return dp[-1][-1]


if __name__ == "__main__":
    import doctest

    doctest.testmod()

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