hello-algo/en/codes/javascript/chapter_dynamic_programming/min_path_sum.js

122 lines
3.6 KiB
JavaScript

/**
* File: min_path_sum.js
* Created Time: 2023-08-23
* Author: Gaofer Chou (gaofer-chou@qq.com)
*/
/* Minimum path sum: Brute force search */
function minPathSumDFS(grid, i, j) {
// If it's the top-left cell, terminate the search
if (i === 0 && j === 0) {
return grid[0][0];
}
// If the row or column index is out of bounds, return a +∞ cost
if (i < 0 || j < 0) {
return Infinity;
}
// Calculate the minimum path cost from the top-left to (i-1, j) and (i, j-1)
const up = minPathSumDFS(grid, i - 1, j);
const left = minPathSumDFS(grid, i, j - 1);
// Return the minimum path cost from the top-left to (i, j)
return Math.min(left, up) + grid[i][j];
}
/* Minimum path sum: Memoized search */
function minPathSumDFSMem(grid, mem, i, j) {
// If it's the top-left cell, terminate the search
if (i === 0 && j === 0) {
return grid[0][0];
}
// If the row or column index is out of bounds, return a +∞ cost
if (i < 0 || j < 0) {
return Infinity;
}
// If there is a record, return it
if (mem[i][j] !== -1) {
return mem[i][j];
}
// The minimum path cost from the left and top cells
const up = minPathSumDFSMem(grid, mem, i - 1, j);
const left = minPathSumDFSMem(grid, mem, i, j - 1);
// Record and return the minimum path cost from the top-left to (i, j)
mem[i][j] = Math.min(left, up) + grid[i][j];
return mem[i][j];
}
/* Minimum path sum: Dynamic programming */
function minPathSumDP(grid) {
const n = grid.length,
m = grid[0].length;
// Initialize dp table
const dp = Array.from({ length: n }, () =>
Array.from({ length: m }, () => 0)
);
dp[0][0] = grid[0][0];
// State transition: first row
for (let j = 1; j < m; j++) {
dp[0][j] = dp[0][j - 1] + grid[0][j];
}
// State transition: first column
for (let i = 1; i < n; i++) {
dp[i][0] = dp[i - 1][0] + grid[i][0];
}
// State transition: the rest of the rows and columns
for (let i = 1; i < n; i++) {
for (let j = 1; j < m; j++) {
dp[i][j] = Math.min(dp[i][j - 1], dp[i - 1][j]) + grid[i][j];
}
}
return dp[n - 1][m - 1];
}
/* Minimum Path Sum: Space-optimized dynamic programming */
function minPathSumDPComp(grid) {
const n = grid.length,
m = grid[0].length;
// Initialize dp table
const dp = new Array(m);
// State transition: first row
dp[0] = grid[0][0];
for (let j = 1; j < m; j++) {
dp[j] = dp[j - 1] + grid[0][j];
}
// State transition: the rest of the rows
for (let i = 1; i < n; i++) {
// State transition: first column
dp[0] = dp[0] + grid[i][0];
// State transition: the rest of the columns
for (let j = 1; j < m; j++) {
dp[j] = Math.min(dp[j - 1], dp[j]) + grid[i][j];
}
}
return dp[m - 1];
}
/* Driver Code */
const grid = [
[1, 3, 1, 5],
[2, 2, 4, 2],
[5, 3, 2, 1],
[4, 3, 5, 2],
];
const n = grid.length,
m = grid[0].length;
// Brute force search
let res = minPathSumDFS(grid, n - 1, m - 1);
console.log(`从左上角到右下角的最小路径和为 ${res}`);
// Memoized search
const mem = Array.from({ length: n }, () =>
Array.from({ length: m }, () => -1)
);
res = minPathSumDFSMem(grid, mem, n - 1, m - 1);
console.log(`从左上角到右下角的最小路径和为 ${res}`);
// Dynamic programming
res = minPathSumDP(grid);
console.log(`从左上角到右下角的最小路径和为 ${res}`);
// Space-optimized dynamic programming
res = minPathSumDPComp(grid);
console.log(`从左上角到右下角的最小路径和为 ${res}`);