There's loads of usecases in logistics where you'd like to optimise a system while being subjected to constraints. In this series of videos we'd like to highlight a tool that can handle a subset of these problems called cvxpy.
This is the new problem definition with constraints;
import cvxpy as cp import pandas as pd df = pd.read_csv("/path/to/stigler.csv") price = df['price_cents'].values x = cp.Variable(price.shape) objective = cp.Minimize(cp.sum(price*x)) constraints = [ x >= 0, cp.sum(df['vitamin_c_mg'].values * x) >= 75, cp.sum(df['iron_mg'].values * x) >= 12, ] prob = cp.Problem(objective, constraints) prob.solve()
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