152 simple minvar experiment

[tschm]

No description provided.

[github-actions]

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[tschm]

@kasperjo @phschiele
I am working on a l little talk about new cvxpy. I came across a few issues

[tschm]

Is this still a thing?
/Users/a13069q/github/cvxmarkowitz/.venv/lib/python3.10/site-packages/cvxpy/reductions/solvers/solving_chain.py:235: UserWarning: Your problem has too many parameters for efficient DPP compilation. We suggest setting 'ignore_dpp = True'.

[tschm]

        x = cp.Variable(n)
        L = cp.Parameter((n, n))

        objective = cp.Minimize(cp.norm2(L.T @ x))
        constraints = [cp.sum(x) == 1, x >= 0]

        prob = cp.Problem(objective, constraints)

How would I compile the problem now?

[tschm]

Ok, diving deeper into cvxpy:

def f2():
    U = uu
    x = cp.Variable(n)

    objective = cp.Minimize(cp.norm2(U @ x))
    constraints = [cp.sum(x) == 1, x >= 0]
    prob = cp.Problem(objective, constraints)

    data, solving_chain, inverse_data = prob.get_problem_data(
        solver
    )
    print(data)

    solution = solving_chain.solve_via_data(
        prob, data)

    prob.unpack_results(solution, solving_chain, inverse_data)
    return prob.value

I wonder how can I update data without calling prob.get_problem_data. I update values for one or all parameters. But when hitting solve it still runs self.get_problem_data?

[phschiele]

@tschm We did not remove the warning for a large number of parameters (yet)

You can call

    data, solving_chain, inverse_data = prob.get_problem_data(
        solver
    )

to canonicalise the problem ahead of time.

Then you just update the parameters and call solve. It will automatically use the cached canonicalisation map.

[tschm]

How can I change the backend to RUST? It tells me it is still using CPP backend...
@phschiele Time for Zoom call today or tomorrow?