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.
You can download the dataset here.
You can also pull it locally by running this on the terminal;
You need to install some tools before starting your notebook.
pip install cvxpy pandas
The notebook in this video contains the following code;
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)) prob = cp.Problem(objective) prob.solve()
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