desc.objectives.LinearObjectiveFromUser

class desc.objectives.LinearObjectiveFromUser(fun, thing, target=None, bounds=None, weight=1, normalize=False, normalize_target=False, name='custom linear')Source

Wrap a user defined linear objective function.

The user supplied function should take one argument, params, which is a dictionary of parameters of an Optimizable “thing”.

The function should be JAX traceable and differentiable, and should return a single JAX array. The source code of the function must be visible to the inspect module for parsing.

Parameters:

• fun (callable) – Custom objective function.

• thing (Optimizable) – Object whose degrees of freedom are being constrained.

• target (dict of {float, ndarray}, optional) – Target value(s) of the objective. Only used if bounds is None. Should have the same tree structure as thing.params. Defaults to things.params.

• bounds (tuple of dict {float, ndarray}, optional) – Lower and upper bounds on the objective. Overrides target. Should have the same tree structure as thing.params.

• weight (dict of {float, ndarray}, optional) – Weighting to apply to the Objective, relative to other Objectives. Should be a scalar or have the same tree structure as thing.params.

• normalize (bool, optional) – Whether to compute the error in physical units or non-dimensionalize. Has no effect for this objective.

• normalize_target (bool, optional) – Whether target and bounds should be normalized before comparing to computed values. If normalize is True and the target is in physical units, this should also be set to True. Has no effect for this objective.

• name (str, optional) – Name of the objective function.

Methods

build([use_jit, verbose])	Build constant arrays.
compute(params[, constants])	Compute fixed degree of freedom errors.
compute_scalar(*args, **kwargs)	Compute the scalar form of the objective.
compute_scaled(*args, **kwargs)	Compute and apply weighting and normalization.
compute_scaled_error(*args, **kwargs)	Compute and apply the target/bounds, weighting, and normalization.
compute_unscaled(*args, **kwargs)	Compute the raw value of the objective.
copy([deepcopy])	Return a (deep)copy of this object.
equiv(other)	Compare equivalence between DESC objects.
grad(*args, **kwargs)	Compute gradient vector of self.compute_scalar wrt x.
hess(*args, **kwargs)	Compute Hessian matrix of self.compute_scalar wrt x.
jac_scaled(*args, **kwargs)	Compute Jacobian matrix of self.compute_scaled wrt x.
jac_scaled_error(*args, **kwargs)	Compute Jacobian matrix of self.compute_scaled_error wrt x.
jac_unscaled(*args, **kwargs)	Compute Jacobian matrix of self.compute_unscaled wrt x.
jit()	Apply JIT to compute methods, or re-apply after updating self.
jvp_scaled(v, x[, constants])	Compute Jacobian-vector product of self.compute_scaled.
jvp_scaled_error(v, x[, constants])	Compute Jacobian-vector product of self.compute_scaled_error.
jvp_unscaled(v, x[, constants])	Compute Jacobian-vector product of self.compute_unscaled.
load(load_from[, file_format])	Initialize from file.
print_value(*args, **kwargs)	Print the value of the objective.
save(file_name[, file_format, file_mode])	Save the object.
update_target(eq)	Update target values using an Equilibrium.
xs(*things)	Return a tuple of args required by this objective from optimizable things.

Attributes

bounds	Lower and upper bounds of the objective.
built	Whether the transforms have been precomputed (or not).
constants	Constant parameters such as transforms and profiles.
dim_f	Number of objective equations.
fixed	Whether the objective fixes individual parameters (or linear combo).
linear	Whether the objective is a linear function (or nonlinear).
name	Name of objective (str).
normalization	normalizing scale factor.
scalar	Whether default "compute" method is a scalar or vector.
target	Target value(s) of the objective.
things	Optimizable things that this objective is tied to.
weight	Weighting to apply to the Objective, relative to other Objectives.