Dynamic Programming: Knapsack

This lecture covers the concept of dynamic programming applied to the Knapsack problem, where items with different weights and profits need to be selected to maximize profit within a given capacity. The instructor explains the process step by step, from defining the problem to finding the optimal solution using dynamic programming. Various strategies and algorithms are discussed, including NP-hard problems and time complexity analysis. The lecture concludes with the computation of the longest path in a directed acyclic graph to solve the Knapsack problem efficiently.