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Implement the Knapsack Algorithm to solve the problem of optimized resource allocation based on given weights and values. The algorithm should support both 0/1 Knapsack and Fractional Knapsack variations, allowing for flexible item selection based on capacity constraints.
Key Features:
0/1 Knapsack: Include or exclude items with integer constraints.
Fractional Knapsack: Allow partial selection of items.
Implement efficient time complexity (Dynamic Programming or Greedy approach based on the problem type).
Create modular functions for the algorithm for ease of integration.
Include unit tests to verify correctness of the implementation for different edge cases.
Expected Outcomes:
Efficient resource optimization based on defined weights and capacities.
Clear and documented code that can be extended for various use cases.
The text was updated successfully, but these errors were encountered:
Implement the Knapsack Algorithm to solve the problem of optimized resource allocation based on given weights and values. The algorithm should support both 0/1 Knapsack and Fractional Knapsack variations, allowing for flexible item selection based on capacity constraints.
Key Features:
0/1 Knapsack: Include or exclude items with integer constraints.
Fractional Knapsack: Allow partial selection of items.
Implement efficient time complexity (Dynamic Programming or Greedy approach based on the problem type).
Create modular functions for the algorithm for ease of integration.
Include unit tests to verify correctness of the implementation for different edge cases.
Expected Outcomes:
Efficient resource optimization based on defined weights and capacities.
Clear and documented code that can be extended for various use cases.
The text was updated successfully, but these errors were encountered: