I Knapsack Solver Algorithm
The Knapsack Solver Algorithm is an optimization technique used to solve the famous Knapsack Problem, which is a classic combinatorial problem in computer science and mathematics. In its simplest form, the problem involves a set of items, each with a weight and a value, and the goal is to determine the most valuable combination of items that can be packed into a knapsack with a limited weight capacity. The Knapsack Solver Algorithm helps to identify the optimal solution by exploring the problem space efficiently and making intelligent decisions about which items to include in the knapsack.
There are several variations of the Knapsack Solver Algorithm, including greedy, dynamic programming, and branch and bound methods. The greedy approach involves sorting items based on their value-to-weight ratio and iteratively adding items to the knapsack until the capacity is reached. This method is fast, but it may not always yield the optimal solution. Dynamic programming, on the other hand, constructs a table with a bottom-up approach, systematically solving subproblems and using their solutions to build the final solution. This technique guarantees an optimal solution but can be computationally expensive for large problem sizes. The branch and bound method explores the solution space using a depth-first search but prunes branches that cannot lead to an optimal solution, reducing the search space and computational effort. Depending on the specifics of the problem and computational resources available, different Knapsack Solver Algorithm variants may be more suitable for different scenarios.
namespace Algorithms.Knapsack
{
/// <summary>
/// Solves knapsack problem:
/// to maximize sum of values of taken items,
/// while sum of weights of taken items is less than capacity.
/// </summary>
/// <typeparam name="T">Type of items in knapsack.</typeparam>
public interface IKnapsackSolver<T> : IHeuristicKnapsackSolver<T>
{
}
}
