SimPEG.simulation.ExponentialSinusoidSimulation.Jvec#
- ExponentialSinusoidSimulation.Jvec(m, v, f=None)[source]#
Compute the Jacobian 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 \(\mathbf{d}\) predicted for a set of model parameters \(\mathbf{m}\), the Jacobian is an (n_data, n_param) matrix whose elements are given by:
\[J_{ij} = \frac{\partial d_i}{\partial m_j}\]For a model m and vector v, the
Jvecmethod computes the matrix-vector product\[\mathbf{u} = \mathbf{J \, v}\]- Parameters:
- m(
n_param, )numpy.ndarray The model parameters.
- v(
n_param, )numpy.ndarray Vector we are multiplying.
- f
SimPEG.field.Fields,optional If provided, fields will not need to be recomputed for the current model to compute Jvec.
- m(
- Returns:
- (
n_data, )numpy.ndarray The Jacobian times a vector for the model and vector provided.
- (