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krahets
2024-04-11 01:11:20 +08:00
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8
docs/chapter_dynamic_programming/unbounded_knapsack_problem.md
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@ -327,7 +327,7 @@ $$
```kotlin title="unbounded_knapsack.kt"
/* 完全背包：动态规划 */
fun unboundedKnapsackDP(wgt: IntArray, value: IntArray, cap: Int): Int {
fun unboundedKnapsackDP(wgt: IntArray, _val: IntArray, cap: Int): Int {
val n = wgt.size
// 初始化 dp 表
val dp = Array(n + 1) { IntArray(cap + 1) }
@ -339,7 +339,7 @@ $$
dp[i][c] = dp[i - 1][c]
} else {
// 不选和选物品 i 这两种方案的较大值
dp[i][c] = max(dp[i - 1][c], dp[i][c - wgt[i - 1]] + value[i - 1])
dp[i][c] = max(dp[i - 1][c], dp[i][c - wgt[i - 1]] + _val[i - 1])
}
}
}
@ -684,7 +684,7 @@ $$
/* 完全背包：空间优化后的动态规划 */
fun unboundedKnapsackDPComp(
wgt: IntArray,
value: IntArray,
_val: IntArray,
cap: Int
): Int {
val n = wgt.size
@ -698,7 +698,7 @@ $$
dp[c] = dp[c]
} else {
// 不选和选物品 i 这两种方案的较大值
dp[c] = max(dp[c], dp[c - wgt[i - 1]] + value[i - 1])
dp[c] = max(dp[c], dp[c - wgt[i - 1]] + _val[i - 1])
}
}
}