jaxopt.CvxpyQP

class jaxopt.CvxpyQP(solver='OSQP', implicit_diff_solve=None)[source]

Wraps CVXPY’s quadratic solver with implicit diff support.

No support for matvec, pytrees, jit and vmap. Meant to be run on CPU. Provide high precision solutions.

The objective function is:

0.5 * x^T Q x + c^T x subject to Gx <= h, Ax = b.
Parameters

  • solver (str) –

  • implicit_diff_solve (Optional[Callable]) –

solver

string specifying the underlying solver used by Cvxpy, in "OSQP", "ECOS", "SCS" (default: "OSQP").

Type

str

__init__(solver='OSQP', implicit_diff_solve=None)
Parameters

  • solver (str) –

  • implicit_diff_solve (Optional[Callable]) –

Return type

None

Methods

__init__([solver, implicit_diff_solve])

attribute_names()

attribute_values()

l2_optimality_error(params, params_obj, ...)

Computes the L2 norm of the KKT residuals.

run(init_params, params_obj[, params_eq, ...])

Runs the quadratic programming solver in CVXPY.

Attributes

implicit_diff_solve

solver

l2_optimality_error(params, params_obj, params_eq, params_ineq)[source]

Computes the L2 norm of the KKT residuals.

Return type

OptStep

Parameters

  • params (Array) –

  • params_obj (Tuple[Array, Array]) –

  • params_eq (Optional[Tuple[Array, Array]]) –

  • params_ineq (Optional[Tuple[Array, Array]]) –

run(init_params, params_obj, params_eq=None, params_ineq=None)[source]

Runs the quadratic programming solver in CVXPY.

The returned params contains both the primal and dual solutions.

Parameters

  • init_params (Optional[Array]) – ignored.

  • params_obj (Tuple[Array, Array]) – (Q, c).

  • params_eq (Optional[Tuple[Array, Array]]) – (A, b) or None if no equality constraints.

  • params_ineq (Optional[Tuple[Array, Array]]) – (G, h) or None if no inequality constraints.

Return type

OptStep

Returns

(params, state), params = (primal_var, dual_var_eq, dual_var_ineq)