A simple Python class to solve the 0-1 Knapsack problem using brute-force search.
⚠️ This project is for my educational purposes.
- How combinatorial problems work
- Numpy operations and vectorized logic
- Class design in Python
- ✅ Clean class-based design
- 🔍 Getter methods for easy result access
- ⚙️ Constraint, Objective, and Solver setup via method calls
- ⚡ Uses NumPy for efficient vectorized operations
Clone the repo or copy the knapsack_solver.py file.
- Suitable for small problem sizes (due to brute-force complexity).
- Future work: implement dynamic programming or heuristic solvers.
import numpy as np
from knapsack_solver import KnapsacSolver
num_variable = 10 # ( < 30 or computer expolde 🤣)
cost = np.random.randint(low=10, high=100, size=num_variable)
weight = np.random.randint(low=10, high=100, size=num_variable)
max_weight = 100
solver = KnapsacSolver(num_variable)
solver.add_cost(cost)
solver.add_weight(weight)
solver.add_max_weight(max_weight)
# Solver (Only brute_force 😢)
solver.add_solver(solver='brute_force')
# call solver
solver.solve()
optimal_cost = solver.optimal_cost
optimal_choose = solver.optimal_choose
print(f"Optimal cost: {optimal_cost}")
print("Optimal selection(s):")
for selection in optimal_choose:
print(selection)