simpeg.meta.MultiprocessingSumMetaSimulation.Jtvec#

MultiprocessingSumMetaSimulation.Jtvec(m, v, f=None)[source]#

Compute the Jacobian transpose times a vector for the model provided.

The Jacobian defines the derivative of the predicted data vector with respect to the model parameters. For a data vector d predicted for a set of model parameters m, the Jacobian is an (n_data, n_param) matrix whose elements are given by:

Jij=dimj

For a model m and vector v, the Jtvec method computes the matrix-vector product with the adjoint-sensitivity

u=JTv
Parameters:
m(n_param, ) numpy.ndarray

The model parameters.

v(n_data, ) numpy.ndarray

Vector we are multiplying.

fsimpeg.field.Fields, optional

If provided, fields will not need to be recomputed for the current model to compute Jtvec.

Returns:
(n_param, ) numpy.ndarray

The Jacobian transpose times a vector for the model and vector provided.