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hello-algo/codes/dart/chapter_dynamic_programming/unbounded_knapsack.dart

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/**
* File: unbounded_knapsack.dart
* Created Time: 2023-08-11
* Author: liuyuxin (gvenusleo@gmail.com)
*/
import 'dart:math';
/* 完全背包:动态规划 */
int unboundedKnapsackDP(List<int> wgt, List<int> val, int cap) {
int n = wgt.length;
// 初始化 dp 表
List<List<int>> dp = List.generate(n + 1, (index) => List.filled(cap + 1, 0));
// 状态转移
for (int i = 1; i <= n; i++) {
for (int c = 1; c <= cap; c++) {
if (wgt[i - 1] > c) {
// 若超过背包容量,则不选物品 i
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]] + val[i - 1]);
}
}
}
return dp[n][cap];
}
/* 完全背包:状态压缩后的动态规划 */
int unboundedKnapsackDPComp(List<int> wgt, List<int> val, int cap) {
int n = wgt.length;
// 初始化 dp 表
List<int> dp = List.filled(cap + 1, 0);
// 状态转移
for (int i = 1; i <= n; i++) {
for (int c = 1; c <= cap; c++) {
if (wgt[i - 1] > c) {
// 若超过背包容量,则不选物品 i
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]] + val[i - 1]);
}
}
}
return dp[cap];
}
/* Driver Code */
void main() {
List<int> wgt = [1, 2, 3];
List<int> val = [5, 11, 15];
int cap = 4;
// 动态规划
int res = unboundedKnapsackDP(wgt, val, cap);
print("不超过背包容量的最大物品价值为 $res");
// 状态压缩后的动态规划
int resComp = unboundedKnapsackDPComp(wgt, val, cap);
print("不超过背包容量的最大物品价值为 $resComp");
}