TemporalLeastSquares

class pysit.objective_functions.TemporalLeastSquares(solver, parallel_wrap_shot=<pysit.util.parallel.ParallelWrapShotNull object>, imaging_period=1)

Bases: pysit.objective_functions.objective_function.ObjectiveFunctionBase

How to compute the parts of the objective you need to do optimization

Methods Summary

apply_hessian(self, shots, m0, m1[, …])
compute_gradient(self, shots, m0[, aux_info])	Compute the gradient for a set of shots.
evaluate(self, shots, m0, \*\*kwargs)	Evaluate the least squares objective function over a list of shots.

Methods Documentation

apply_hessian(self, shots, m0, m1, hessian_mode='approximate', levenberg_mu=0.0, *args, **kwargs)

compute_gradient(self, shots, m0, aux_info={}, **kwargs)

Compute the gradient for a set of shots.

Computes the gradient as

-F*(d - scriptF[m0]) = -sum(F*_s(d - scriptF_s[m0])) for s in shots

Parameters:

shots : list of pysit.Shot

List of Shots for which to compute the gradient.

m0 : ModelParameters

The base point about which to compute the gradient

evaluate(self, shots, m0, **kwargs)

Evaluate the least squares objective function over a list of shots.