From b70dd12d5ea80e0ec5ee151efb849f1e4fe7b984 Mon Sep 17 00:00:00 2001
From: wuaoxiang <os.popen@gmail.com>
Date: Tue, 23 Jan 2024 22:47:21 +0800
Subject: [PATCH] solve minimum-falling-path-sum with cache of recursive

---
 src/dp/minimum_falling_path_sum.rs | 53 +++++++++++++++++++++++-------
 1 file changed, 42 insertions(+), 11 deletions(-)

diff --git a/src/dp/minimum_falling_path_sum.rs b/src/dp/minimum_falling_path_sum.rs
index cba93f4..047d7eb 100644
--- a/src/dp/minimum_falling_path_sum.rs
+++ b/src/dp/minimum_falling_path_sum.rs
@@ -1,32 +1,63 @@
 //! https://leetcode.cn/problems/minimum-falling-path-sum
+
+/*
+here is a leetcode question: Given an n x n array of integers matrix, return the minimum sum of any falling path through matrix.
+
+A falling path starts at any element in the first row and chooses the element in the next row that is either directly below or diagonally left/right. Specifically, the next element from position (row, col) will be (row + 1, col - 1), (row + 1, col), or (row + 1, col + 1).
+
+below is my Rust code, pass 70/100 test case but time limit exceed
+
+I don't know how to solve this problem using dynamtic programing
+
+but I have a optimize idea is add a cache arg in search function, to cache each input/output of search, can you help me to impl it?
+*/
+
+/// time limit exceed
 fn min_falling_path_sum(matrix: Vec<Vec<i32>>) -> i32 {
     let m = matrix.len();
     let n = matrix[0].len();
+    let mut cache = vec![vec![None; n]; m]; // Create a cache with the same dimensions as matrix
 
     let mut min = i32::MAX;
     for j in 0..n {
-        min = min.min(search(0, j, m, n, 0, &matrix));
+        min = min.min(search(0, j, &matrix, &mut cache));
     }
 
     min
 }
 
-fn search(row: usize, col:usize, m: usize, n: usize, mut sum: i32, matrix: &Vec<Vec<i32>>) -> i32 {
-    if row == m {
-        return sum;
+fn search(row: usize, col: usize, matrix: &Vec<Vec<i32>>, cache: &mut Vec<Vec<Option<i32>>>) -> i32 {
+    if row == matrix.len() {
+        return 0; // We've reached the bottom of the matrix, no more values to add
     }
+
+    // If we have a cached value, return it
+    if let Some(cached) = cache[row][col] {
+        return cached;
+    }
+
     let cur_val = matrix[row][col];
-    sum += cur_val;
-    let mut min = search(row+1,col, m, n, sum, matrix);
-    if col >= 1 {
-        min = min.min(search(row+1,col-1, m, n, sum, matrix));
+    let mut min_path = cur_val + search(row + 1, col, matrix, cache); // Vertical
+
+    if col > 0 {
+        min_path = min_path.min(cur_val + search(row + 1, col - 1, matrix, cache)); // Diagonally left
     }
-    if col < n-1 {
-        min = min.min(search(row+1,col+1, m, n, sum, matrix));
+    if col + 1 < matrix[0].len() {
+        min_path = min_path.min(cur_val + search(row + 1, col + 1, matrix, cache)); // Diagonally right
     }
-    min
+
+    // Store the result in the cache before returning
+    cache[row][col] = Some(min_path);
+    min_path
 }
 
+/*
+## DP solution
+assume m,j is rows,columns of matrix
+answer = min(sum[m-1][0], sum[m-1][1], ..., sum[m-1][n-1])
+sum[m-1][0]=matrix[m-1][0] + min(sum[m-2][0], sum[m-2][1])
+*/
+
 #[test]
 fn test() {
     for (matrix, min_path_sum) in [