Regression from ECOS causing CVXPY test failures

[PTNobel]

We were investigating some of the test case failures as we migrate the CVXPY test suite from ECOS to Clarabel in cvxpy/cvxpy#2479

It seems at least some of the cases are related to when ECOS correctly reports PRIMAL INFEASIBLE, but Clarabel reports InsufficientProgress instead.

This breaks our disciplined quasiconvex programing work and is consistent with issues that the EE 364a course staff had with Clarabel when assigning bisection methods in the homework.

Frankly, I'm a little worried about what to do here; the 1.5.x CVXPY release apparently regresses the DQCP support compared to prior releases. I'm not sure if this is just an issue of different default feastols or if this is from a new infeasibility detection approach.

Here's a minimized example:

import scipy.sparse as sp
import numpy as np

def make_array(d):
    out = sp.dok_matrix((8, 3))
    for (i, j), v in d.items():
        out[i, j] = v
    return out.tocsc()

G = make_array({
    (0, 0): -1.0,
    (2, 0): 1.0,
    (4, 0): -1.0,
    (1, 1): -1.0,
    (3, 1): 1.0,
    (5, 1): -1.0,
    (6, 1): -1.0,
    (4, 2): 72.001953125,
    (5, 2): -1.0,
    (6, 2): 1.0})
h = np.array([ 0.,  0., 12.,  6.,  0.,  0.,  0.,  2.])
c = np.array([0., 0., 0.])
nonneg = 5
soc = [3]

import ecos

solution = ecos.solve(c, G, h, {'l': 5, 'q': [3]})


import clarabel

P = sp.dok_matrix((3, 3)).tocsc()

cones = [clarabel.NonnegativeConeT(5), clarabel.SecondOrderConeT(3)]
settings = clarabel.DefaultSettings()
solver = clarabel.DefaultSolver(P,c,G,h,cones,settings)
solution = solver.solve()

[mipals]

Having looked the code I do not think there is anything to worry about. It seems as the old infeasibility checks are still present. Adding the ones from the preprint seem to fix the issue (see here).

The only thing that is not really described in the preprint is a multiplication with 1000, which Paul also mentions in a comment in the code should be fixed

Clarabel.rs/src/solver/implementations/default/info.rs

Line 317 in d1ca25d

//PJG hardcoded factor 1000 here should be fixed

I am not sure what fix Paul was thinking of, but I simply removed the multiplication.

[mipals]

Having looked at this again, I can see that I missed the scalings that happens in the updating of info. As such the above is not really a fix. However, I did notice that a normx is missing here

Clarabel.rs/src/solver/implementations/default/info.rs

Line 115 in d1ca25d

self.res_primal_inf = residuals.rx_inf.norm_scaled(dinv) / T::max(T::one(), normz);

For the simple problem here, it does seem like the big reason that it fail is due to the multiplication of 1000, as the ktratio never gets above 1e9 before InsufficientProgress is reported.