JavaAlgorithms/DynamicProgramming/MemoizationTechniqueKnapsack.java
2021-09-17 20:02:57 +03:00

60 lines
1.3 KiB
Java

package DynamicProgramming;
// Here is the top-down approach of
// dynamic programming
public class MemoizationTechniqueKnapsack {
//A utility function that returns
//maximum of two integers
static int max(int a, int b) {
return (a > b) ? a : b;
}
//Returns the value of maximum profit
static int knapSackRec(int W, int wt[], int val[], int n, int[][] dp) {
// Base condition
if (n == 0 || W == 0)
return 0;
if (dp[n][W] != -1)
return dp[n][W];
if (wt[n - 1] > W)
// Store the value of function call
// stack in table before return
return dp[n][W] = knapSackRec(W, wt, val, n - 1, dp);
else
// Return value of table after storing
return dp[n][W] = max((val[n - 1] + knapSackRec(W - wt[n - 1], wt, val, n - 1, dp)),
knapSackRec(W, wt, val, n - 1, dp));
}
static int knapSack(int W, int wt[], int val[], int N) {
// Declare the table dynamically
int dp[][] = new int[N + 1][W + 1];
// Loop to initially filled the
// table with -1
for (int i = 0; i < N + 1; i++)
for (int j = 0; j < W + 1; j++)
dp[i][j] = -1;
return knapSackRec(W, wt, val, N, dp);
}
//Driver Code
public static void main(String[] args) {
int val[] = { 60, 100, 120 };
int wt[] = { 10, 20, 30 };
int W = 50;
int N = val.length;
System.out.println(knapSack(W, wt, val, N));
}
}