DCOPF.run

DCOPF.run(**kwargs) → bool

Run the routine.

Following kwargs go to self.init(): force_init, force_mats, force_constr, force_om.

Following kwargs go to self.solve(): solver, verbose, gp, qcp, requires_grad, enforce_dpp, ignore_dpp, method, and all rest.

Parameters:

force_initbool, optional

If True, force re-initialization. Defaults to False.

force_matsbool, optional

If True, force re-generating matrices. Defaults to False.

force_constrbool, optional

Whether to turn on all constraints.

force_ombool, optional

If True, force re-generating optimization model. Defaults to False.

solver: str, optional

The solver to use. For example, 'GUROBI', 'ECOS', 'SCS', or 'OSQP'.

verbosebool, optional

Overrides the default of hiding solver output and prints logging information describing CVXPY's compilation process.

gpbool, optional

If True, parses the problem as a disciplined geometric program instead of a disciplined convex program.

qcpbool, optional

If True, parses the problem as a disciplined quasiconvex program instead of a disciplined convex program.

requires_gradbool, optional

Makes it possible to compute gradients of a solution with respect to Parameters by calling problem.backward() after solving, or to compute perturbations to the variables given perturbations to Parameters by calling problem.derivative(). Gradients are only supported for DCP and DGP problems, not quasiconvex problems. When computing gradients (i.e., when this argument is True), the problem must satisfy the DPP rules.

enforce_dppbool, optional

When True, a DPPError will be thrown when trying to solve a non-DPP problem (instead of just a warning). Only relevant for problems involving Parameters. Defaults to False.

ignore_dppbool, optional

When True, DPP problems will be treated as non-DPP, which may speed up compilation. Defaults to False.

methodfunction, optional

A custom solve method to use.