/** * @file * @brief Implementation of the Unbounded 0/1 Knapsack Problem * * @details * The Unbounded 0/1 Knapsack problem allows taking unlimited quantities of each * item. The goal is to maximize the total value without exceeding the given * knapsack capacity. Unlike the 0/1 knapsack, where each item can be taken only * once, in this variation, any item can be picked any number of times as long * as the total weight stays within the knapsack's capacity. * * Given a set of N items, each with a weight and a value, represented by the * arrays `wt` and `val` respectively, and a knapsack with a weight limit W, the * task is to fill the knapsack to maximize the total value. * * @note weight and value of items is greater than zero * * ### Algorithm * The approach uses dynamic programming to build a solution iteratively. * A 2D array is used for memoization to store intermediate results, allowing * the function to avoid redundant calculations. * * @author [Sanskruti Yeole](https://github.com/yeolesanskruti) * @see dynamic_programming/0_1_knapsack.cpp */ #include // For using assert function to validate test cases #include // For fixed-width integer types like std::uint16_t #include // Standard input-output stream #include // Standard library for using dynamic arrays (vectors) /** * @namespace dynamic_programming * @brief Namespace for dynamic programming algorithms */ namespace dynamic_programming { /** * @namespace Knapsack * @brief Implementation of unbounded 0-1 knapsack problem */ namespace unbounded_knapsack { /** * @brief Recursive function to calculate the maximum value obtainable using * an unbounded knapsack approach. * * @param i Current index in the value and weight vectors. * @param W Remaining capacity of the knapsack. * @param val Vector of values corresponding to the items. * @note "val" data type can be changed according to the size of the input. * @param wt Vector of weights corresponding to the items. * @note "wt" data type can be changed according to the size of the input. * @param dp 2D vector for memoization to avoid redundant calculations. * @return The maximum value that can be obtained for the given index and * capacity. */ std::uint16_t KnapSackFilling(std::uint16_t i, std::uint16_t W, const std::vector& val, const std::vector& wt, std::vector>& dp) { if (i == 0) { if (wt[0] <= W) { return (W / wt[0]) * val[0]; // Take as many of the first item as possible } else { return 0; // Can't take the first item } } if (dp[i][W] != -1) return dp[i][W]; // Return result if available int nottake = KnapSackFilling(i - 1, W, val, wt, dp); // Value without taking item i int take = 0; if (W >= wt[i]) { take = val[i] + KnapSackFilling(i, W - wt[i], val, wt, dp); // Value taking item i } return dp[i][W] = std::max(take, nottake); // Store and return the maximum value } /** * @brief Wrapper function to initiate the unbounded knapsack calculation. * * @param N Number of items. * @param W Maximum weight capacity of the knapsack. * @param val Vector of values corresponding to the items. * @param wt Vector of weights corresponding to the items. * @return The maximum value that can be obtained for the given capacity. */ std::uint16_t unboundedKnapsack(std::uint16_t N, std::uint16_t W, const std::vector& val, const std::vector& wt) { if (N == 0) return 0; // Expect 0 since no items std::vector> dp( N, std::vector(W + 1, -1)); // Initialize memoization table return KnapSackFilling(N - 1, W, val, wt, dp); // Start the calculation } } // namespace unbounded_knapsack } // namespace dynamic_programming /** * @brief self test implementation * @return void */ static void tests() { // Test Case 1 std::uint16_t N1 = 4; // Number of items std::vector wt1 = {1, 3, 4, 5}; // Weights of the items std::vector val1 = {6, 1, 7, 7}; // Values of the items std::uint16_t W1 = 8; // Maximum capacity of the knapsack // Test the function and assert the expected output assert(dynamic_programming::unbounded_knapsack::unboundedKnapsack( N1, W1, val1, wt1) == 48); std::cout << "Maximum Knapsack value " << dynamic_programming::unbounded_knapsack::unboundedKnapsack( N1, W1, val1, wt1) << std::endl; // Test Case 2 std::uint16_t N2 = 3; // Number of items std::vector wt2 = {10, 20, 30}; // Weights of the items std::vector val2 = {60, 100, 120}; // Values of the items std::uint16_t W2 = 5; // Maximum capacity of the knapsack // Test the function and assert the expected output assert(dynamic_programming::unbounded_knapsack::unboundedKnapsack( N2, W2, val2, wt2) == 0); std::cout << "Maximum Knapsack value " << dynamic_programming::unbounded_knapsack::unboundedKnapsack( N2, W2, val2, wt2) << std::endl; // Test Case 3 std::uint16_t N3 = 3; // Number of items std::vector wt3 = {2, 4, 6}; // Weights of the items std::vector val3 = {5, 11, 13}; // Values of the items std::uint16_t W3 = 27; // Maximum capacity of the knapsack // Test the function and assert the expected output assert(dynamic_programming::unbounded_knapsack::unboundedKnapsack( N3, W3, val3, wt3) == 27); std::cout << "Maximum Knapsack value " << dynamic_programming::unbounded_knapsack::unboundedKnapsack( N3, W3, val3, wt3) << std::endl; // Test Case 4 std::uint16_t N4 = 0; // Number of items std::vector wt4 = {}; // Weights of the items std::vector val4 = {}; // Values of the items std::uint16_t W4 = 10; // Maximum capacity of the knapsack assert(dynamic_programming::unbounded_knapsack::unboundedKnapsack( N4, W4, val4, wt4) == 0); std::cout << "Maximum Knapsack value for empty arrays: " << dynamic_programming::unbounded_knapsack::unboundedKnapsack( N4, W4, val4, wt4) << std::endl; std::cout << "All test cases passed!" << std::endl; } /** * @brief main function * @return 0 on successful exit */ int main() { tests(); // Run self test implementation return 0; }