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This is an implementation of the 0-1 knapsack problem in C using a greedy algorithm. The problem consists of a set of items, each with a weight and a value, and a knapsack with a maximum weight capacity. The goal is to determine the subset of items that maximizes the total value of the knapsack without exceeding its weight capacity.

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0-1 Knapsack Problem in C (Greedy)

This is an implementation of the 0-1 knapsack problem in C using a greedy algorithm. The problem consists of a set of items, each with a weight and a value, and a knapsack with a maximum weight capacity. The goal is to determine the subset of items that maximizes the total value of the knapsack without exceeding its weight capacity.

How to use

To use this implementation, include the 0-1knapsack_greedy.c file in your project and call the knapSackGreedy() function with the following parameters:

  • W: the maximum weight capacity of the knapsack
  • items[]: an array of Item structs, each containing a value and weight property
  • n: the number of items

The function will return the maximum value that can be put in the knapsack without exceeding its weight capacity.

#include "knapsack_greedy.c"

int main()
{
    int W = 50;
    Item items[] = {{60, 10}, {100, 20}, {120, 30}};
    int n = sizeof(items)/sizeof(items[0]);

    qsort(items, n, sizeof(items[0]), cmp);
    printf("%d", knapSackGreedy(W, items, n));

    return 0;
}

Time and Space Complexity

The time complexity of this implementation is O(nlogn) where n is the number of items, W is the knapsack capacity. The space complexity is O(n) for the sorting algorithm.

Note

This is a greedy algorithm, it guarantees that the solution is at least as good as the optimal solution, however, it doesn't guarantee that it is optimal.

I hope this implementation helps you solve the 0-1 knapsack problem in your project. If you have any questions or suggestions, feel free to reach out.

Copyright (c) 2022, Max Base

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This is an implementation of the 0-1 knapsack problem in C using a greedy algorithm. The problem consists of a set of items, each with a weight and a value, and a knapsack with a maximum weight capacity. The goal is to determine the subset of items that maximizes the total value of the knapsack without exceeding its weight capacity.

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