现有一个容量大小为V的背包和N件物品,每件物品有两个属性,体积和价值,请问这个背包最多能装价值为多少的物品?
现有一个容量大小为V的背包和N件物品,每件物品有两个属性,体积和价值,请问这个背包最多能装价值为多少的物品?
第一行两个整数V和n。
接下来n行,每行两个整数体积和价值。1≤N≤1000,1≤V≤20000。
每件物品的体积和价值范围在[1,500]。
输出背包最多能装的物品价值。
6 3 3 5 2 4 4 2
9
#include <bits/stdc++.h> using namespace std; int main(){ int n,c; cin>>n>>c; int w[n], v[n]; for(int i=0;i<n;i++) cin>>w[i]>>v[i]; int dp[c+1]; memset(dp,0,sizeof(dp)); for(int i=0;i<n;i++) for(int j=c;j>=w[i];j--) dp[j] = max(dp[j], dp[j-w[i]]+v[i]); cout<<dp[c]<<endl; return 0; }
import sys input = [] for line in sys.stdin: input.append(line.strip().split()) volumn = int(input[0][0]) weights = [int(x[0]) for x in input[1:]] values = [int(x[1]) for x in input[1:]] num_items = len(weights) dp = [[0 for _ in range(volumn+1)] for _ in range(num_items)] for i in range(0, weights[-1]): dp[-1][i] = 0 for i in range(weights[-1], volumn+1): dp[-1][i] = values[-1] for i in range(num_items-2, 0, -1): for j in range(volumn+1): if j < weights[i]: dp[i][j] = dp[i+1][j] else: dp[i][j] = max(dp[i+1][j], dp[i+1][j-weights[i]]+values[i]) if weights[0] > volumn: print(dp[1][volumn]) else: print(max(dp[1][volumn], dp[1][volumn-weights[0]]+values[0]))90%的python代码。。最后一次不需要循环。
V, n = input().split() V = int(V) n = int(n) W=[] P=[] for i in range(n): w,p = input().split() W.append(int(w)) P.append(int(p)) def knapsack_loop(): f = [[0 for i in range(V+1)] for j in range(n)] for y in range(W[n-1],V+1): f[n-1][y] = P[n-1] for i in range(n-2,-1,-1): for y in range(V+1): if y>=W[i]: f[i][y] = max(f[i+1][y],f[i+1][y-W[i]]+P[i]) else: f[i][y] = f[i+1][y] return f f=knapsack_loop() print(f[0][V])
#include <iostream> #include <algorithm> using namespace std; const int N = 20010; int dp[N]; int n,v; int vi[N]; int wi[N]; int main() { scanf("%d%d",&v,&n); for(int i = 0;i<n;i++) scanf("%d%d",&vi[i],&wi[i]); for(int i = 1;i <= n;i++) for(int j = v;j >= 1;j--) if(vi[i - 1] <= j) dp[j] = max(dp[j],dp[j - vi[i - 1]] + wi[i - 1]); cout<<dp[v]; return 0; }
#include<iostream> #include<vector> using namespace std; int main() { int V,N; cin>>V>>N; vector<int> dp(V+1); vector<int> weight(N+1); vector<int> value(N+1); for(int i=1;i<=N;i++) { cin>>weight[i]>>value[i]; } for(int i=1;i<=N;i++) { for(int j=V;j>=weight[i];j--) { dp[j]=max(dp[j-weight[i]]+value[i], dp[j]); } } cout<<dp[V]; return 0; }一维dp
#include<iostream> #include<vector> #include<algorithm> using namespace std; int main(){ int n,m;//n个数 m:包体积 cin>>m>>n; if(n==0 || m==0){ cout<<0; return 0; } vector<int> V;//价值 vector<int> A;//体积 int x, y; for (int i = 0; i < n; ++i) { cin >> x >> y; A.push_back(x); V.push_back(y); } vector<int> ans(m+1,0); for(int i=1;i<=n;++i){ for(int j=m;j>0;--j){ if(j>=A[i-1]) ans[j]=max(ans[j],V[i-1]+ans[j-A[i-1]]); } } /* vector<vector<int>> ans(n+1,vector<int>(m+1,0)); for(int i=1;i<=n;++i){ for(int j=1;j<=m;++j){ if(A[i-1]>j){ ans[i][j]=ans[i-1][j]; }else{ ans[i][j]=max(ans[i-1][j],V[i-1]+ans[i-1][j-A[i-1]]); } } } */ cout<<ans[m]; return 0; }
def knapsack(V , n , vw ): dp=[[0]*(V+1) for _ in range(n+1)] for i in range(1,n+1): vi=vw[i-1][0] wi=vw[i-1][1] for j in range(1,V+1): if vi>j: dp[i][j]=dp[i-1][j] else: dp[i][j]=max(dp[i-1][j],dp[i-1][j-vi]+wi) return dp[-1][-1] V , n =map(int,input().split()) vw=[] for _ in range(n): v,w=map(int,input().split()) vw.append([v,w]) print(knapsack(V , n , vw ))
#include <iostream> #include <vector> using namespace std; int main() { // Read inputs unsigned W, N; // Weight Limit, Number of items cin >> W >> N; vector<int> weight; vector<int> val; for (unsigned i = 0; i < N; i++) { int x1, x2; cin >> x1 >> x2; weight.push_back(x1); val.push_back(x2); } // Core code vector<int> dp(W + 1, 0); for (unsigned i = 0; i < N; i++) { for (unsigned w = W; w >= weight[i]; w--) { dp[w] = max(dp[w], dp[w - weight[i]] + val[i]); } } // Output cout << dp[W]; return 0; }
package main import "fmt" func main() { var v int var n int fmt.Scan(&v,&n) volumes := make([]int,n) values := make([]int,n) var volume int var value int for i:=0;i<n;i++{ fmt.Scan(&volume,&value) volumes[i] = volume values[i] = value } maxValue := knapsack(v,n,volumes,values) fmt.Println(maxValue) } func knapsack(v,n int,volumes,values []int) int { dp := make([][]int,n+1) for i:=0;i<=n;i++{ dp[i] = make([]int,v+1) } for i:=1;i<=n;i++{ volume := volumes[i-1] value := values[i-1] for j:=1;j<=v;j++{ if j>=volume { dp[i][j]=max(dp[i-1][j],dp[i-1][j-volume]+value) }else { dp[i][j]=dp[i-1][j] } } } return dp[n][v] } func max(a,b int) int { if a>b { return a } return b }Go语言这么不受待见?居然没有专门的插入格式
import java.util.Scanner; public class Main { public static void main(String[] args){ Scanner in = new Scanner(System.in); int V = in.nextInt(); int n = in.nextInt(); int[][] pack = new int [n + 1][2]; for(int i = 1; i <= n; i++){ pack[i][0] = in.nextInt(); pack[i][1] = in.nextInt(); } int[][] dp = new int[n + 1][V + 1]; for(int i = 1; i <= n; i++){ for(int j = 1; j <= V; j++){ // 此时物品体积大于背包容积,故不能放进背包,因此最大价值为 if(pack[i][0] > j){ dp[i][j] = dp[i - 1][j]; } else{ // 此时空间够大,可以选择物品i和不选择物品i // 不选择,则为dp[i - 1][j],即为前i-1个物品在空间为j时的最优解 // 选择时,则为前i - 1个物品,在空间为j - 物品i所占空间的最优解 dp[i][j] = Math.max(dp[i - 1][j], (dp[i - 1][j - pack[i][0]] + pack[i][1])); } } } // 输出整个dp数组 for(int[] i : dp){ for(int j : i){ System.out.print(j + " "); } System.out.println(); } System.out.println(dp[n][V]); } }
import java.util.Scanner; public class Main{ public static void main(String[] args){ Scanner sc = new Scanner(System.in); int V = sc.nextInt(); int n = sc.nextInt(); Thing[] things = new Thing[n]; for(int i=0;i<n;i++){ int v; int w; v=sc.nextInt(); w=sc.nextInt(); Thing thing = new Thing(v,w); things[i]=thing; } int[] dp = new int[V+1]; for(int i=0;i<n;i++){ for(int j=V;j>0;j--){ int max = dp[j]; if(j-things[i].vo>=0 && max<things[i].we+dp[j-things[i].vo]){ max = things[i].we+dp[j-things[i].vo]; } if(max>dp[j]){ dp[j]=max; } } } System.out.println(dp[V]); } } class Thing{ int vo; int we; public Thing(int vo,int we){ this.vo = vo; this.we = we; } }
#include<iostream> #include<math.h> using namespace std; int main(){ int n,c; cin>>c>>n; int w[n],v[n],dp[c+1]; for(int i=0;i<n;i++) cin>>w[i]>>v[i]; for(int j=0;j<=c;j++) if(w[0]<=j) dp[j]=v[0]; else dp[j]=0; for(int i=1;i<n;i++) for(int j=c;j>=w[i];j--){ dp[j]=max(dp[j], v[i]+dp[j-w[i]]); } cout<<dp[c]<<endl; return 0; }
C++实现,时间复杂度O(NV),空间复杂度O(V)。
#include<bits/stdc++.h> using namespace std; int main() { int V, N; cin >> V >> N; vector<int> dp(V + 1, 0); vector<int> vVct, wVct; vVct.reserve(N + 1); wVct.reserve(N + 1); int tmpV, tmpW; for (int i = 0; i < N; ++i) { cin >> tmpV >> tmpW; vVct.push_back(tmpV); wVct.push_back(tmpW); } for (int i = 0; i < N; ++i) { for (int j = V; j >= vVct[i]; --j) { dp[j] = max(dp[j], dp[j - vVct[i]] + wVct[i]); } } cout << dp[V] << endl; return 0; }
input1 = list(map(int,input().strip().split())) KG = input1[0] nums = input1[1] weights = [] values = [] for i in range(nums): input_wuti = list(map(int,input().strip().split())) weights.append(input_wuti[0]) values.append(input_wuti[1]) V = [[0]*(KG+1) for j in range(len(weights)+1)] # 初始化一个二维数组 for k in range(1,len(weights)+1): # 这里k从1开始,因为当提供的物品为0个时,总价值必然为0,也就是V[0][h]=0 for h in range(1,KG+1): # 这里h从1开始,因为当背包体积为0时,总价值必然为0,也就是V[k][0]=0 if h-weights[k-1]<0: V[k][h] = V[k-1][h] else: V[k][h]=max(V[k-1][h], V[k-1][h-weights[k-1]]+values[k-1]) print(V[len(weights)][KG])这个是用一维数组的情况,通过率也是90%,也是超时的问题,代码思路应该没问题,如下:
task={} n,m = map(int,input().strip().split()) # n为总体积,m为物品个数 a,b = [], [] # a为体积,b为价值 for i in range(m): a_, b_ = map(int,input().strip().split()) a.append(a_) b.append(b_) max_value = [0]*(n+1) # max_value[j]表示总体积为j时的最大价值,max_value[0]必然为0 temp = [k for k in range(1,n+1)][::-1] # 逆序 for i in range(m): for j in temp: # 注意这里要反向枚举,所以前面做了逆序 if j>=a[i]: # 当前剩余体积比遇到的物品的体积大,就把物品的价值加上,再与不把物品加上去的情况进行比较 max_value[j] = max(max_value[j],max_value[j-a[i]]+b[i]) print(max_value[n])