import marimo

__generated_with = "0.6.22"
app = marimo.App(width="medium")


@app.cell
def __():
    import cvxpy as cp
    import numpy as np
    import marimo as mo
    return cp, mo, np


@app.cell
def __(np):
    # cost vector (dollars per kg of food)
    c = np.array([6, 5, 2, 15])

    # Nutrient content matrix
    # Rows corresponding to grams of protein, carbohydrates, and fat per kg of food.
    # The food types in the columns are lentils, broccoli, rice, and almonds.
    A = np.array([
        [250, 28, 20, 210],
        [600, 60, 780, 200],
        [10, 3, 10, 500]
    ])

    # Minimum required nutrients vector (grams per day).
    b = np.array([50, 300, 70])
    return A, b, c


@app.cell
def __(A, b, c, cp):
    x = cp.Variable(4, nonneg=True)

    cost = c @ x

    constraints = [A @ x >= b]

    problem = cp.Problem(cp.Minimize(cost), constraints)
    problem.solve()

    print(x.value)
    print(cost.value)
    return constraints, cost, problem, x


if __name__ == "__main__":
    app.run()