TemporalLeastSquares

class pysit.objective_functions.temporal_least_squares.TemporalLeastSquares(solver, parallel_wrap_shot=<pysit.util.parallel.ParallelWrapShotNull object at 0x2b1f750>)[source]

Bases: pysit.objective_functions.objective_function.ObjectiveFunctionBase

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

Methods Summary

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

Methods Documentation

apply_hessian(shots, m0, m1, hessian_mode='approximate', levenberg_mu=0.0, *args, **kwargs)[source]
compute_gradient(shots, m0, aux_info={}, **kwargs)[source]

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(shots, m0, **kwargs)[source]

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

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