0/1 Knapsack Problem — Dynamic Programming Explained with the Algorithm

Lecture Link

In this lesson, we tackle the 0/1 Knapsack Problem, a classic optimization problem in computer science and interview prep. We’ll motivate the problem, define it clearly, and then solve it using dynamic programming. You’ll see the pseudoalgorithm, learn why we use a table (columns = capacities 0…W, rows = items), and walk through a concrete example step by step. In the next video, we’ll implement it in Python.

What you’ll learn:

  • What the 0/1 Knapsack Problem is used for (budgeting, scheduling, resource allocation, portfolios)

  • The DP intuition: solve small capacities first, reuse results

  • How to build the DP table: rows = items, columns = capacities

  • The key choice at each cell: take or skip the item

  • How to trace back selected items from the final table

  • Clean pseudoalgorithm you can convert to code

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