0/1 Knapsack Problem using Dynamic Programming || GATECSE || DAA

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This video teaches how to solve the 0/1 Knapsack Problem using dynamic programming. The problem involves packing items with weights and values into a limited capacity knapsack. The goal is to maximize the total value while satisfying capacity constraints. The solution is to break the problem into smaller subproblems and solve them recursively, storing solutions in a tabular format.

What You Will Learn:

Dynamic Programming: An algorithmic technique to solve complex problems by breaking them down into smaller subproblems and solving them recursively.
Tabulation: Storing the solutions to subproblems in a tabular format to avoid solving the problem from scratch each time.
Memoization: A technique used in dynamic programming to store the solutions to subproblems in memory for faster computation. 💪 Tips and Tricks:
Break down the problem into smaller subproblems.
Use a recursive approach to solve the subproblems.
Store the solutions to subproblems in a tabular format.
Use memoization to avoid unnecessary computations.

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📚 Subject Wise Playlist 📚
▶️Data Structures: http://tinyurl.com/bwptf6f7
▶️Theory of Computation: http://tinyurl.com/5bhtzhtd
▶️Compiler Design: http://tinyurl.com/2p9wtykf
▶️Design and Analysis of Algorithms: http://tinyurl.com/ywk8uuzc
▶️Graph Theory: http://tinyurl.com/3e8mynaw
▶️Discrete Mathematics: http://tinyurl.com/y82r977y
▶️C Programming:http://tinyurl.com/2556mrmm

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