RegularizedLinearRTO

class cuqi.sampler.RegularizedLinearRTO(target, x0=None, maxit=100, stepsize='automatic', abstol=1e-10, adaptive=True, **kwargs)

Regularized Linear RTO (Randomize-Then-Optimize) sampler.

Samples posterior related to the inverse problem with Gaussian likelihood and implicit Gaussian prior, and where the forward model is Linear.

Parameters:

• target (cuqi.distribution.Posterior) – See cuqi.sampler.LinearRTO

• x0 (np.ndarray) – Initial point for the sampler. Optional.

• maxit (int) – Maximum number of iterations of the inner FISTA solver. Optional.

• stepsize (string or float) – If stepsize is a string and equals either “automatic”, then the stepsize is automatically estimated based on the spectral norm. If stepsize is a float, then this stepsize is used.

• abstol (float) – Absolute tolerance of the inner FISTA solver. Optional.

• callback (callable, Optional) – If set this function will be called after every sample. The signature of the callback function is callback(sample, sample_index), where sample is the current sample and sample_index is the index of the sample. An example is shown in demos/demo31_callback.py.

__init__(target, x0=None, maxit=100, stepsize='automatic', abstol=1e-10, adaptive=True, **kwargs)

Methods

__init__(target[, x0, maxit, stepsize, ...])
sample(N[, Nb])
sample_adapt(N[, Nb])
step(x)	Perform a single MCMC step
step_tune(x, *args, **kwargs)	Perform a single MCMC step and tune the sampler.
tune()	Tune the sampler parameters.

Attributes

dim
geometry
likelihood
likelihoods
model
models
prior
target