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docs/chapter_dynamic_programming/dp_solution_pipeline.md
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@ -349,11 +349,7 @@ $$
```kotlin title="min_path_sum.kt"
/* 最小路径和：暴力搜索 */
fun minPathSumDFS(
grid: Array<Array<Int>>,
i: Int,
j: Int
): Int {
fun minPathSumDFS(grid: Array<IntArray>, i: Int, j: Int): Int {
// 若为左上角单元格，则终止搜索
if (i == 0 && j == 0) {
return grid[0][0]
@ -366,7 +362,7 @@ $$
val up = minPathSumDFS(grid, i - 1, j)
val left = minPathSumDFS(grid, i, j - 1)
// 返回从左上角到 (i, j) 的最小路径代价
return (min(left.toDouble(), up.toDouble()) + grid[i][j]).toInt()
return min(left, up) + grid[i][j]
}
```
@ -711,8 +707,8 @@ $$
```kotlin title="min_path_sum.kt"
/* 最小路径和：记忆化搜索 */
fun minPathSumDFSMem(
grid: Array<Array<Int>>,
mem: Array<Array<Int>>,
grid: Array<IntArray>,
mem: Array<IntArray>,
i: Int,
j: Int
): Int {
@ -732,7 +728,7 @@ $$
val up = minPathSumDFSMem(grid, mem, i - 1, j)
val left = minPathSumDFSMem(grid, mem, i, j - 1)
// 记录并返回左上角到 (i, j) 的最小路径代价
mem[i][j] = (min(left.toDouble(), up.toDouble()) + grid[i][j]).toInt()
mem[i][j] = min(left, up) + grid[i][j]
return mem[i][j]
}
```
@ -1098,7 +1094,7 @@ $$
```kotlin title="min_path_sum.kt"
/* 最小路径和：动态规划 */
fun minPathSumDP(grid: Array<Array<Int>>): Int {
fun minPathSumDP(grid: Array<IntArray>): Int {
val n = grid.size
val m = grid[0].size
// 初始化 dp 表
@ -1115,8 +1111,7 @@ $$
// 状态转移：其余行和列
for (i in 1..<n) {
for (j in 1..<m) {
dp[i][j] =
(min(dp[i][j - 1].toDouble(), dp[i - 1][j].toDouble()) + grid[i][j]).toInt()
dp[i][j] = min(dp[i][j - 1], dp[i - 1][j]) + grid[i][j]
}
}
return dp[n - 1][m - 1]
@ -1500,7 +1495,7 @@ $$
```kotlin title="min_path_sum.kt"
/* 最小路径和：空间优化后的动态规划 */
fun minPathSumDPComp(grid: Array<Array<Int>>): Int {
fun minPathSumDPComp(grid: Array<IntArray>): Int {
val n = grid.size
val m = grid[0].size
// 初始化 dp 表
@ -1516,7 +1511,7 @@ $$
dp[0] = dp[0] + grid[i][0]
// 状态转移：其余列
for (j in 1..<m) {
dp[j] = (min(dp[j - 1].toDouble(), dp[j].toDouble()) + grid[i][j]).toInt()
dp[j] = min(dp[j - 1], dp[j]) + grid[i][j]
}
}
return dp[m - 1]