ProxNest.utils.create_options_dict(samplesL=1000.0, samplesD=10000.0, thinning=100.0, delta=1e-08, burn=100.0, sigma=1)

Compiles a dictionary of option parameters for sampling

Parameters

  • samplesL (int) – Number of live samples (default = 1e3).

  • samplesD (int) – Number of discarded samples (default = 1e4).

  • thinning (int) – Thinning factors (i.e. iterations per sample, default =1 1e2).

  • delta (float) – Discretisation stepsize (< Lipschitz constant of \(\nabla F\), default = 1e-8).

  • burn (int) – Number of burn in samples to be discarded (default = 1e2).

  • sigma (float) – Noise std of degraded image (default = 1).

Returns

Dictionary of sampling options.

Return type

dict

ProxNest.utils.create_parameters_dict(y=0, Phi=None, Psi=None, epsilon=0.001, tight=True, nu=1, tol=0.001, max_iter=200, verbose=1, u=0, pos=False, reality=False, l1weights=1, rel_obj=0)

Compiles a dictionary of parameters for code simplicity

Parameters

  • y (np.ndarray) – Measurements (default = 0).

  • Phi (linear operator) – Sensing operator (default = None).

  • Psi (linear operator) – Redundant dictionary (default = None).

  • epsilon (float) – Radius of the \(\ell_2\) ball (default = 1e-3).

  • tight (bool) – True if A is a tight frame or False otherwise (default = 1).

  • nu (float) – Bound on the squared-norm of the operator A, i.e. \(||A x||^2 <= \nu ||x||^2\) (default = 1).

  • tol (float) – Tolerance, i.e. the algorithms stops if \(\epsilon/(1-tol) <= ||y - A z||_2 <= \epsilon/(1+tol)\) (default = 1e-3).

  • max_iter (int) – Maximum number of iterations (default: 200).

  • verbose (int) – Verbosity level (0 = no log, 1 = summary at convergence, 2 = print main steps; default = 1).

  • u (np.ndarray) – Initial vector for the dual problem, same dimension as y (default = 0).

  • pos (bool) – Positivity flag (True = positive solution, False (default) general case).

  • reality (bool) – Reality flag (True = real solution, 0 (default) = general complex case).

  • l1weights (np.ndarray) – Reweighting of thresholding of \(\ell_1\)-norm (default = 1).

  • rel_obj (float) – Stopping criterion for \(\ell_1\) proximal sub-iterations (default = 0).

Returns

Dictionary of parameters.

Return type

dict