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Optimize and Format Knapsack Memoization Algorithm (#5685)

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PANKAJ PATWAL
2024-10-12 12:41:25 +05:30
committed by GitHub
parent b81671e66d
commit e38611e9db
2 changed files with 20 additions and 6 deletions
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11
src/main/java/com/thealgorithms/dynamicprogramming/KnapsackMemoization.java
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@ -1,5 +1,7 @@
package com.thealgorithms.dynamicprogramming;
import java.util.Arrays;
/**
* Recursive Solution for 0-1 knapsack with memoization
* This method is basically an extension to the recursive approach so that we
@ -15,10 +17,8 @@ public class KnapsackMemoization {
int[][] dpTable = new int[numOfItems + 1][capacity + 1];
// Loop to initially fill the table with -1
for (int i = 0; i < numOfItems + 1; i++) {
for (int j = 0; j < capacity + 1; j++) {
dpTable[i][j] = -1;
}
for (int[] table : dpTable) {
Arrays.fill(table, -1);
}
return solveKnapsackRecursive(capacity, weights, profits, numOfItems, dpTable);
@ -38,7 +38,6 @@ public class KnapsackMemoization {
if (weights[numOfItems - 1] > capacity) {
// Store the value of function call stack in table
dpTable[numOfItems][capacity] = solveKnapsackRecursive(capacity, weights, profits, numOfItems - 1, dpTable);
return dpTable[numOfItems][capacity];
} else {
// case 1. include the item, if it is less than the capacity
final int includeCurrentItem = profits[numOfItems - 1] + solveKnapsackRecursive(capacity - weights[numOfItems - 1], weights, profits, numOfItems - 1, dpTable);
@ -48,7 +47,7 @@ public class KnapsackMemoization {
// Store the value of function call stack in table and return
dpTable[numOfItems][capacity] = Math.max(includeCurrentItem, excludeCurrentItem);
return dpTable[numOfItems][capacity];
}
return dpTable[numOfItems][capacity];
}
}