Maximum sum of values of N items in 0-1 Knapsack by reducing weight of at most K items in half
Last Updated :
27 Jan, 2023
Given weights and values of N items and the capacity W of the knapsack. Also given that the weight of at most K items can be changed to half of its original weight. The task is to find the maximum sum of values of N items that can be obtained such that the sum of weights of items in knapsack does not exceed the given capacity W.
Examples:
Input: W = 4, K = 1, value = [17, 20, 10, 15], weight = [4, 2, 7, 5]
Output: 37
Explanation: Change the weight of at most K items to half of the weight in a optimal way to get maximum value. Decrease the weight of first item to half and add second item weight the resultant sum of value is 37 which is maximum
Input: W = 8, K = 2, value = [17, 20, 10, 15], weight = [4, 2, 7, 5]
Output: 52
Explanation: Change the weight of the last item and first item and the add the weight the of the 2nd item, The total sum value of item will be 52.
Approach: Given problem is the variation of the 0 1 knapsack problem. Flag indicates number of items whose weight has been reduced to half. At every recursive call maximum of following cases is calculated and returned:
- Base case: If the index exceeds the length of values then return zero
- If flag is equal to K, maximum of 2 cases is considered:
- Include item with full weight if item’s weight does not exceed remaining weight
- Skip the item
- If flag is less than K, maximum of 3 cases is considered:
- Include item with full weight if item’s weight does not exceed remaining weight
- Include item with half weight if item’s half weight does not exceed remaining weight
- Skip the item
C++
#include <bits/stdc++.h>
using namespace std;
int maximum( int value[], int weight[], int weight1,
int flag, int K, int index, int val_len)
{
if (index >= val_len) {
return 0;
}
if (flag == K) {
int skip = maximum(value, weight, weight1, flag, K,
index + 1, val_len);
int full = 0;
if (weight[index] <= weight1) {
full = value[index]
+ maximum(value, weight,
weight1 - weight[index], flag,
K, index + 1, val_len);
}
return max(full, skip);
}
else {
int skip = maximum(value, weight, weight1, flag, K,
index + 1, val_len);
int full = 0;
int half = 0;
if (weight[index] <= weight1) {
full = value[index]
+ maximum(value, weight,
weight1 - weight[index], flag,
K, index + 1, val_len);
}
if (weight[index] / 2 <= weight1) {
half = value[index]
+ maximum(value, weight,
weight1 - weight[index] / 2,
flag + 1, K, index + 1,
val_len);
}
return max(full, max(skip, half));
}
}
int main()
{
int value[] = { 17, 20, 10, 15 };
int weight[] = { 4, 2, 7, 5 };
int K = 1;
int W = 4;
int val_len = sizeof (value) / sizeof (value[0]);
cout << (maximum(value, weight, W, 0, K, 0, val_len));
return 0;
}
|
Java
import java.io.*;
import java.util.*;
class GFG {
static int maximum( int value[], int weight[],
int weight1, int flag, int K,
int index)
{
if (index >= value.length) {
return 0 ;
}
if (flag == K) {
int skip = maximum(value, weight, weight1, flag,
K, index + 1 );
int full = 0 ;
if (weight[index] <= weight1) {
full = value[index]
+ maximum(value, weight,
weight1 - weight[index],
flag, K, index + 1 );
}
return Math.max(full, skip);
}
else {
int skip = maximum(value, weight, weight1, flag,
K, index + 1 );
int full = 0 ;
int half = 0 ;
if (weight[index] <= weight1) {
full = value[index]
+ maximum(value, weight,
weight1 - weight[index],
flag, K, index + 1 );
}
if (weight[index] / 2 <= weight1) {
half
= value[index]
+ maximum(value, weight,
weight1 - weight[index] / 2 ,
flag, K, index + 1 );
}
return Math.max(full, Math.max(skip, half));
}
}
public static void main(String[] args) throws Exception
{
int value[] = { 17 , 20 , 10 , 15 };
int weight[] = { 4 , 2 , 7 , 5 };
int K = 1 ;
int W = 4 ;
System.out.println(
maximum(value, weight, W, 0 , K, 0 ));
}
}
|
Python3
def maximum(value,
weight, weight1,
flag, K, index, val_len):
if (index > = val_len):
return 0
if (flag = = K):
skip = maximum(value,
weight, weight1,
flag, K, index + 1 , val_len)
full = 0
if (weight[index] < = weight1):
full = value[index] + maximum(
value, weight,
weight1 - weight[index], flag,
K, index + 1 , val_len)
return max (full, skip)
else :
skip = maximum(
value, weight,
weight1, flag,
K, index + 1 , val_len)
full = 0
half = 0
if (weight[index] < = weight1):
full = value[index] + maximum(
value, weight,
weight1 - weight[index],
flag, K, index + 1 , val_len)
if (weight[index] / 2 < = weight1):
half = value[index] + maximum(
value, weight,
weight1 - weight[index] / 2 ,
flag, K, index + 1 , val_len)
return max (full,
max (skip, half))
value = [ 17 , 20 , 10 , 15 ]
weight = [ 4 , 2 , 7 , 5 ]
K = 1
W = 4
val_len = len (value)
print (maximum(value, weight, W,
0 , K, 0 , val_len))
|
C#
using System;
public class GFG {
static int maximum( int [] value, int [] weight,
int weight1, int flag, int K,
int index)
{
if (index >= value.Length) {
return 0;
}
if (flag == K) {
int skip = maximum(value, weight, weight1, flag,
K, index + 1);
int full = 0;
if (weight[index] <= weight1) {
full = value[index]
+ maximum(value, weight,
weight1 - weight[index],
flag, K, index + 1);
}
return Math.Max(full, skip);
}
else {
int skip = maximum(value, weight, weight1, flag,
K, index + 1);
int full = 0;
int half = 0;
if (weight[index] <= weight1) {
full = value[index]
+ maximum(value, weight,
weight1 - weight[index],
flag, K, index + 1);
}
if (weight[index] / 2 <= weight1) {
half
= value[index]
+ maximum(value, weight,
weight1 - weight[index] / 2,
flag, K, index + 1);
}
return Math.Max(full, Math.Max(skip, half));
}
}
public static void Main(String[] args)
{
int [] value = { 17, 20, 10, 15 };
int [] weight = { 4, 2, 7, 5 };
int K = 1;
int W = 4;
Console.WriteLine(
maximum(value, weight, W, 0, K, 0));
}
}
|
Javascript
<script>
function maximum(value,
weight , weight1,
flag , K , index)
{
if (index >= value.length) {
return 0;
}
if (flag == K) {
var skip = maximum(value,
weight, weight1,
flag, K, index + 1);
var full = 0;
if (weight[index] <= weight1) {
full = value[index]
+ maximum(
value, weight,
weight1 - weight[index], flag,
K, index + 1);
}
return Math.max(full, skip);
}
else {
var skip = maximum(
value, weight,
weight1, flag,
K, index + 1);
var full = 0;
var half = 0;
if (weight[index] <= weight1) {
full = value[index]
+ maximum(
value, weight,
weight1 - weight[index],
flag, K, index + 1);
}
if (weight[index] / 2 <= weight1) {
half = value[index]
+ maximum(
value, weight,
weight1 - weight[index] / 2,
flag, K, index + 1);
}
return Math.max(full,
Math.max(skip, half));
}
}
var value = [ 17, 20, 10, 15 ];
var weight = [ 4, 2, 7, 5 ];
var K = 1;
var W = 4;
document.write(
maximum(value, weight, W,
0, K, 0));
</script>
|
Time Complexity: O(3^N)
Auxiliary Space: O(N)
Given problem is the variation of the 0-1 knapsack problem. In traditional 0-1 Knapsack problem, we only have to choices: skip the i-th item or take it.
However, in this case we will have 3 choices as mentioned in the recursive approach.
Efficient Approach (Dynamic Programming):
In Dynamic programming, we will work considering the same cases as above. We shall consider a 3D DP table where the state DP[i][j][k] will denote the maximum value we can obtain if we are considering values from 1 to i-th, weight of the knapsack is j and we can half the weight of at most k values. Basically, we are adding one extra state, the number of weights that can be halved in a traditional 2-D 01 knapsack DP matrix.
Now, three possibilities can take place:
- Include item with full weight if the item’s weight does not exceed the remaining weight
- Include the item with half weight if the item’s half weight does not exceed the remaining weight
- Skip the item
Now we have to take the maximum of these three possibilities. If we do not take the i-th weight then dp[i][j][k] would remain equal to dp[i – 1][j][k], just llike traditional knapsack. If we include item with half weight then dp[i][j][k] would be equal to dp[i – 1][j – wt[i] / 2][k – 1] + val[i] as after including i-th value our remaining knapsack capacity would be j – wt[i] / 2 and our number of half operations would increase by 1. Similarly, if we include item with full weight then dp[i][j][k] would be equal to dp[i – 1][j – wt[i]][k] + val[i] as knapsack capacity in this case would reduce to j – wt[i].
We simply take the maximum of all three choices.
Below is the code implementation of above approach:
C++
#include <iostream>
#include <vector>
#include <cstring>
using namespace std;
int getMaximumNutrition( int W, vector< int > value, vector< int > weight, int x) {
int n=value.size();
int dp[n + 1][W + 1][x + 1];
memset (dp, 0, sizeof (dp));
for ( int i = 1;i <= n; i++)
{
for ( int j = 1;j <= W; j++)
{
int lim = x;
if (lim > i)
{
lim = i;
}
for ( int k = 0;k <= lim; k++)
{
dp[i][j][k] = max(dp[i - 1][j][k], dp[i][j][k]);
int val = j - (weight[i - 1] / 2);
if (val >= 0 && k > 0)
{
dp[i][j][k] = max(dp[i - 1][val][k - 1]+value[i - 1], dp[i][j][k]);
}
val = j - weight[i - 1];
if (val >= 0)
{
dp[i][j][k] = max(dp[i - 1][val][k] + value[i - 1], dp[i][j][k]);
}
}
}
}
return dp[n][W][x];
}
int main() {
vector< int > value = {17, 20, 10, 15};
vector< int > weight = {4, 2, 7, 5};
int x = 1;
int W = 4;
cout << getMaximumNutrition(W, value, weight, x);
return 0;
}
|
Java
import java.util.*;
public class Main {
static int getMaximumNutrition( int W, int [] value,
int [] weight, int x)
{
int n = value.length;
int [][][] dp = new int [n + 1 ][W + 1 ][x + 1 ];
for ( int i = 1 ; i <= n; i++) {
for ( int j = 1 ; j <= W; j++) {
int lim = x;
if (lim > i) {
lim = i;
}
for ( int k = 0 ; k <= lim; k++) {
dp[i][j][k]
= Math.max(dp[i - 1 ][j][k],
dp[i][j][k]);
int val = j - (weight[i - 1 ] / 2 );
if (val >= 0
&& k > 0 )
{
dp[i][j][k]
= Math.max(dp[i - 1 ][val][k - 1 ]
+ value[i - 1 ],
dp[i][j][k]);
}
val = j - weight[i - 1 ];
if (val
>= 0 )
{
dp[i][j][k]
= Math.max(dp[i - 1 ][val][k]
+ value[i - 1 ],
dp[i][j][k]);
}
}
}
}
return dp[n][W][x];
}
public static void main(String[] args)
{
int [] value = { 17 , 20 , 10 , 15 };
int [] weight = { 4 , 2 , 7 , 5 };
int x = 1 ;
int W = 4 ;
System.out.println(
getMaximumNutrition(W, value, weight, x));
}
}
|
C#
using System;
class GFG {
static int getMaximumNutrition( int W, int [] value,
int [] weight, int x)
{
int n = value.Length;
int [, , ] dp = new int [n + 1, W + 1, x + 1];
for ( int i = 1; i <= n; i++) {
for ( int j = 1; j <= W; j++) {
int lim = x;
if (lim > i) {
lim = i;
}
for ( int k = 0; k <= lim; k++) {
dp[i, j, k]
= Math.Max(dp[i - 1, j, k],
dp[i, j, k]);
int val = j - (weight[i - 1] / 2);
if (val >= 0
&& k > 0)
{
dp[i, j, k]
= Math.Max(dp[i - 1, val, k - 1]
+ value[i - 1],
dp[i, j, k]);
}
val = j - weight[i - 1];
if (val
>= 0)
{
dp[i, j, k]
= Math.Max(dp[i - 1, val, k]
+ value[i - 1],
dp[i, j, k]);
}
}
}
}
return dp[n, W, x];
}
static void Main()
{
int [] val = { 17, 20, 10, 15 };
int [] weight = { 4, 2, 7, 5 };
int x = 1;
int W = 4;
Console.Write(
getMaximumNutrition(W, val, weight, x));
}
}
|
Javascript
function getMaximumNutrition(W, value, weight, x) {
let n=value.length;
let dp = new Array(n+1);
for (let i = 0; i < n + 1; i++)
{
dp[i] = new Array(W+1);
for (let j = 0; j < W + 1; j++)
{
dp[i][j] = new Array(x+1);
for (let k = 0; k < x + 1; k++)
{
dp[i][j][k] = 0;
}
}
}
for (let i = 1;i <= n; i++)
{
for (let j = 1;j <= W; j++)
{
let lim = x;
if (lim > i)
{
lim = i;
}
for (let k = 0;k <= lim; k++)
{
dp[i][j][k] = Math.max(dp[i - 1][j][k], dp[i][j][k]);
let val = j - Math.round(weight[i - 1] / 2);
if (val >= 0 && k > 0)
{
dp[i][j][k] = Math.max(dp[i - 1][val][k - 1]+value[i - 1], dp[i][j][k]);
}
val = j - weight[i - 1];
if (val >= 0)
{
dp[i][j][k] = Math.max(dp[i - 1][val][k] + value[i - 1], dp[i][j][k]);
}
}
}
}
return dp[n][W][x];
}
let value = [17, 20, 10, 15];
let weight = [4, 2, 7, 5];
let x = 1;
let W = 4;
console.log(getMaximumNutrition(W, value, weight, x));
|
Python3
from typing import List , Tuple
def getMaximumNutrition(W: int , value: List [ int ], weight: List [ int ], x: int ) - > int :
n = len (value)
dp = [[[ 0 for _ in range (x + 1 )] for _ in range (W + 1 )] for _ in range (n + 1 )]
for i in range ( 1 , n + 1 ):
for j in range ( 1 , W + 1 ):
lim = min (x, i)
for k in range (lim + 1 ):
dp[i][j][k] = max (dp[i - 1 ][j][k], dp[i][j][k])
val = j - (weight[i - 1 ] / / 2 )
if val > = 0 and k > 0 :
dp[i][j][k] = max (dp[i - 1 ][val][k - 1 ] + value[i - 1 ], dp[i][j][k])
val = j - weight[i - 1 ]
if val > = 0 :
dp[i][j][k] = max (dp[i - 1 ][val][k] + value[i - 1 ], dp[i][j][k])
return dp[n][W][x]
if __name__ = = "__main__" :
value = [ 17 , 20 , 10 , 15 ]
weight = [ 4 , 2 , 7 , 5 ]
x = 1
W = 4
print (getMaximumNutrition(W, value, weight, x))
|
Time Complexity: O(n*W*K)
Auxiliary Space: O(n*W*K)
Note that space complexity can be further reduced to O(W*2*2) as for computing some dp[i][j][k] we only need to know values at current and previous i-th and k-th state. Time complexity will remain the same.
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