ValueError: Can't apply Jacobian with a quadratic objective

[mr-mikmik]

Hi all,
I am trying to get gradients on a QP solution, but I am having the following error: ValueError: Can't apply Jacobian with a quadratic objective.
This is the trace:

File python3.8/site-packages/cvxpylayers/torch/cvxpylayer.py:286, in _CvxpyLayerFn.<locals>._CvxpyLayerFnFn.backward(ctx, *dvars)
    284 grad = [[] for _ in range(len(param_order))]
    285 for i in range(ctx.batch_size):
--> 286     del_param_dict = compiler.apply_param_jac(
    287         dcs[i], -dAs[i], dbs[i])
    288     for j, p in enumerate(param_order):
    289         grad[j] += [to_torch(del_param_dict[p.id],
    290                              ctx.dtype, ctx.device).unsqueeze(0)]
File python3.8/site-packages/cvxpy/reductions/dcp2cone/cone_matrix_stuffing.py:222, in ParamConeProg.apply_param_jac(self, delc, delA, delb, active_params)
    214 """Multiplies by Jacobian of parameter mapping.
    215 
    216 Assumes delA is sparse.
   (...)
    219     A dictionary param.id -> dparam
    220 """
    221 if self.P is not None:
--> 222     raise ValueError("Can't apply Jacobian with a quadratic objective.")
    224 if active_params is None:
    225     active_params = {p.id for p in self.parameters}

using cvxpy 1.4.1 and cvxpylayers 0.0.1

I need help understanding why it does not work with quadratic objectives. Any insights will be much appreciated.
Thank you.

Just for reference, this is the form of my program:

$$\begin{array}{ccc} \underset{\mathbf x}{\min} & \frac{1}{2}\mathbf x^T\mathbf Q\mathbf x& \\ \text{s.t.} & \mathbf G \mathbf x \preceq \mathbf h & \\ & \mathbf A\mathbf x = \mathbf b, & \end{array}$$

where $\mathbf x$ is a cp.Variable, $\mathbf Q$ is a constant, and $\mathbf G$, $\mathbf h$, $\mathbf A$, and $\mathbf b$ are cp.Parameter.

For context, the goal is to optimize via the backpropagated gradients a set of parameters that produce $\mathbf G$, $\mathbf h$, $\mathbf A$, and $\mathbf b$.