pyfaust.factparams.ParamsPalm4MSA Class Reference

The class intents to set input parameters for the Palm4MSA algorithm. More...

Public Member Functions
def	__init__ (self, constraints, stop_crit, init_facts=None, is_update_way_R2L=False, init_lambda=1.0, step_size=10.0 **-16, constant_step_size=False, is_verbose=False, norm2_max_iter=100, norm2_threshold=1e-6, grad_calc_opt_mode=ParamsFact.EXTERNAL_OPT, **kwargs)
	Constructor. More...
def	are_constraints_consistent (self, M)
	This method verifies that the constraints are shape-consistent to the matrix/array M to factorize and with each other. More...
def	__repr__ (self)
	Returns object representation. More...
Public Member Functions inherited from pyfaust.factparams.ParamsFact
def	__init__ (self, num_facts, is_update_way_R2L, init_lambda, constraints, step_size, constant_step_size, is_verbose, factor_format='dynamic', packing_RL=True, no_normalization=False, no_lambda=False, norm2_max_iter=100, norm2_threshold=1e-6, grad_calc_opt_mode=EXTERNAL_OPT, **kwargs)

Additional Inherited Members
Static Public Member Functions inherited from pyfaust.factparams.ParamsFact
def	factor_format_str2int (factor_format)
def	factor_format_int2str (factor_format)
def	get_constraints (projs)
	Returns a python list of constraints from the projs which is a ConstralintList or a list/tuple that can be a mix of ConstraintGeneric or pyfaust.proj.proj_gen. More...
Public Attributes inherited from pyfaust.factparams.ParamsFact
	step_size
	constant_step_size
	norm2_max_iter
	norm2_threshold
	factor_format
	packing_RL
	no_normalization
	no_lambda
	use_MHTP

Detailed Description

The class intents to set input parameters for the Palm4MSA algorithm.

See also

ParamsPalm4MSA.__init__, pyfaust.fact.palm4msa

Constructor & Destructor Documentation

__init__()

def pyfaust.factparams.ParamsPalm4MSA.__init__	(		self,
			constraints,
			stop_crit,
			init_facts = None,
			is_update_way_R2L = False,
			init_lambda = 1.0,
			step_size = 10.0**-16,
			constant_step_size = False,
			is_verbose = False,
			norm2_max_iter = 100,
			norm2_threshold = 1e-6,
			grad_calc_opt_mode = ParamsFact.EXTERNAL_OPT,
		**	kwargs
	)

Constructor.

Parameters

constraints	(ConstraintList or list[pyfaust.proj.proj_gen]). The number of items determines the number of matrix factors.
stop_crit	(pyfaust.factparams.StoppingCriterion) defines the algorithm stopping criterion.
init_facts	(list[np.ndarray or scipy.sparse.csr_matrix]) if defined, pyfaust.fact.palm4msa will initialize the factors with init_facts (by default, None, implies that the first factor to be updated is initialized to zero and the others to identity. Note that the so called first factor can be the rightmost or the leftmost depending on the is_update_way_R2L argument). Note also that the matrices must be np.ndarray if the backend argument of pyfaust.fact.palm4msa is equal to 2016, otherwise (backend==2020) it is possible to use np.ndarray or scipy.sparse.csr_matrix (depending of the ParamsPalm4MSA.factor_format attribute).
is_update_way_R2L	(bool) if True pyfaust.fact.palm4msa will update factors from the right to the left, otherwise it's done in reverse order.
init_lambda	(float) the scale scalar initial value (by default the value is one).
step_size	(float) the initial step of the PALM descent.
constant_step_size	(bool) if True the step_size keeps constant along the algorithm iterations otherwise it is updated before every factor update.
is_verbose	(bool) True to enable the verbose mode.
norm2_max_iter	(float) maximum number of iterations of power iteration algorithm. Used for computing 2-norm.
norm2_threshold	(float) power iteration algorithm threshold (default to 1e-6). Used for computing 2-norm.
grad_calc_opt_mode	(float) the mode used for computing the PALM gradient. It can be one value among pyfaust.factparams.ParamsFact.EXTERNAL_OPT, pyfaust.factparams.ParamsFact.INTERNAL_OPT or pyfaust.factparams.ParamsFact.DISABLED_OPT. This parameter is experimental, its value shouldn't be changed.
no_normalization	(bool) False (by default), if True it disables the normalization of prox output matrix in PALM4MSA algorithm. Note that this option is experimental.
no_lambda	(bool) False (by default), if True it disables the lambda scalar factor in the PALM4MSA algorithm which consists basically to set it always to one (it lowers also the algorithm cost).

Member Function Documentation

__repr__()

def pyfaust.factparams.ParamsPalm4MSA.__repr__

(

self

)

Returns object representation.

Reimplemented from pyfaust.factparams.ParamsFact.

are_constraints_consistent()

def pyfaust.factparams.ParamsPalm4MSA.are_constraints_consistent	(		self,
			M
	)

This method verifies that the constraints are shape-consistent to the matrix/array M to factorize and with each other.

Returns

True if the constraints are consistent, raises a ValueError otherwise.

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The documentation for this class was generated from the following file:

• factparams.py