Learn how to solve the knapsack problem with two common approaches: dynamic programming and greedy methods. Compare their pros and cons and decide which one to use.

However, MSSPs grow quickly and become computationally intractable for real-world size problems due to their space and time complexities. This paper presents a generalized knapsack-problem based ...

Explore the Knapsack algorithm in action with this TypeScript implementation that optimizes transaction selection based on account balances. This repository demonstrates how the Knapsack algorithm can ...

We introduce novel algorithms for solving dynamic programming problems in economics on a quantum annealer, a specialized quantum computer used for combinatorial optimization. Quantum annealers begin ...

When designing electronic circuits, we often need to solve discrete optimization problems. One of the basic methods for solving such problems is the method of dynamic programming. The paper is devoted ...

To solve the stochastic unit commitment problem efficiently, a solution method based on the Benders decomposition and modified stochastic dual dynamic programming algorithm is proposed.

An approximate value function of the original problem is formulated via the searching table model and approximate policy value iteration process to address the “curse of dimensionally” in traditional ...

This paper proposes an efficient parallel algorithm for an important class of dynamic programming problems that includes Viterbi, Needleman–Wunsch, Smith–Waterman, and Longest Common Subsequence. In ...