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Using Java, can you provide an algorithm of the 0-1 Knapsack using the Backtracking method.

Using Java, can you provide an algorithm of the 0-1 Knapsack using the Backtracking method.

Database System Concepts
Database System Concepts
7th Edition
ISBN:9780078022159
Author:Abraham Silberschatz Professor, Henry F. Korth, S. Sudarshan
Publisher:Abraham Silberschatz Professor, Henry F. Korth, S. Sudarshan
Chapter1: Introduction
Section: Chapter Questions
Problem 1PE
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Using Java, can you provide an algorithm of the 0-1 Knapsack using the Backtracking method. Below is an example of the code from the textbook and data. Thanks

 

Suppose that n = 4, W16, and we have the following: i 1 2 3 4 Pi W₂ $20 $6 $50 10 $5 $10 5 W₂ Pi $40 2 $30 5
Transcribed Image Text:Suppose that n = 4, W16, and we have the following: i 1 2 3 4 Pi W₂ $20 $6 $50 10 $5 $10 5 W₂ Pi $40 2 $30 5
void knapsack (index i, { if } } int profit, int (weight <= W && profit > marprofit profit; numbest i; = bestset include; if (promising (i)) { include [i+1] knapsack(i+1, include [ + 1] knapsack(i+1, profit, weight); } bool promising (index i) { index j, k; int totweight; float bound; if (weight >= W) return false; else { weight) marprofit){ "yes"; profit + p[i+1], weight + w[i+1]); "no": j=i+1; bound profit; totweight weight; while (j<= n && totweight + w[j] <= W){ totweight = totweight + w[i]; bound bound + p[j]; j++; } k = ji if (k <=n) bound = bound (W- totweight) return bound > maxprofit; // This set is best // so far. // Set numbest to // number of items // considered. Set // bestset to this // solution. * // Include w[i+1]. // Do not include // w[i+1]. // Node is promising only // if we should expand to // its children. There must // be some capacity left for // the children. // Grab as many items as // possible. // Use k for consistency // with formula in text. p[k]/w[k]; // Grab fraction of kth // item.
Transcribed Image Text:void knapsack (index i, { if } } int profit, int (weight <= W && profit > marprofit profit; numbest i; = bestset include; if (promising (i)) { include [i+1] knapsack(i+1, include [ + 1] knapsack(i+1, profit, weight); } bool promising (index i) { index j, k; int totweight; float bound; if (weight >= W) return false; else { weight) marprofit){ "yes"; profit + p[i+1], weight + w[i+1]); "no": j=i+1; bound profit; totweight weight; while (j<= n && totweight + w[j] <= W){ totweight = totweight + w[i]; bound bound + p[j]; j++; } k = ji if (k <=n) bound = bound (W- totweight) return bound > maxprofit; // This set is best // so far. // Set numbest to // number of items // considered. Set // bestset to this // solution. * // Include w[i+1]. // Do not include // w[i+1]. // Node is promising only // if we should expand to // its children. There must // be some capacity left for // the children. // Grab as many items as // possible. // Use k for consistency // with formula in text. p[k]/w[k]; // Grab fraction of kth // item.
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