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Package parsimony :: Package algorithms :: Module proximal :: Class CONESTA
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Class CONESTA

source code

            object --+        
                     |        
   bases.BaseAlgorithm --+    
                         |    
   bases.ExplicitAlgorithm --+
                             |
                object --+   |
                         |   |
  bases.IterativeAlgorithm --+
                             |
                object --+   |
                         |   |
bases.InformationAlgorithm --+
                             |
                            CONESTA

COntinuation with NEsterov smoothing in a Soft-Thresholding Algorithm,
or CONESTA for short.

Parameters
----------
mu_min : Non-negative float. A "very small" mu to use as a lower bound for
        mu.

tau : Float, 0 < tau < 1. The rate at which eps is decreasing. Default
        is 0.5.

eps : Positive float. Tolerance for the stopping criterion.

info : List or tuple of utils.Info. What, if any, extra run information
        should be stored. Default is an empty list, which means that no
        run information is computed nor returned.

max_iter : Non-negative integer. Maximum allowed number of iterations.

min_iter : Non-negative integer less than or equal to max_iter. Minimum
        number of iterations that must be performed. Default is 1.
Instance Methods [hide private]
 
__init__(self, mu_min=5e-08, tau=0.5, info=[], eps=5e-08, max_iter=10000, min_iter=1, simulation=False)
x.__init__(...) initializes x; see help(type(x)) for signature		 source code
 
run(self, function, *args, **kwargs)
This function obtains a minimiser of a give function.		 source code

Inherited from bases.BaseAlgorithm: get_params, set_params

Inherited from bases.IterativeAlgorithm: iter_reset

Inherited from bases.InformationAlgorithm: check_info_compatibility, info_copy, info_get, info_provided, info_requested, info_reset, info_set

Inherited from object: __delattr__, __format__, __getattribute__, __hash__, __new__, __reduce__, __reduce_ex__, __repr__, __setattr__, __sizeof__, __str__, __subclasshook__

Static Methods [hide private]

Inherited from bases.BaseAlgorithm: check_compatibility

Class Variables [hide private]
  INTERFACES = [<class 'parsimony.functions.properties.NesterovF...
  INFO_PROVIDED = ['ok', 'converged', 'num_iter', 'continuations...
  __abstractmethods__ = frozenset([])
  _abc_negative_cache_version = 14

Inherited from bases.ExplicitAlgorithm: __metaclass__

Inherited from bases.BaseAlgorithm (private): _abc_cache, _abc_negative_cache, _abc_registry

Properties [hide private]

Inherited from object: __class__

Method Details [hide private]

__init__(self, mu_min=5e-08, tau=0.5, info=[], eps=5e-08, max_iter=10000, min_iter=1, simulation=False)
(Constructor)

source code 

x.__init__(...) initializes x; see help(type(x)) for signature

Overrides: object.__init__
(inherited documentation)

run(self, function, *args, **kwargs)

source code  
This function obtains a minimiser of a give function.

Parameters
----------
function : The function to minimise.

x : A starting point.
Decorators:
  • @bases.force_reset
  • @bases.check_compatibility
Overrides: bases.ExplicitAlgorithm.run
(inherited documentation)

Class Variable Details [hide private]

INTERFACES

Value:
[<class 'parsimony.functions.properties.NesterovFunction'>,
 <class 'parsimony.functions.properties.StepSize'>,
 <class 'parsimony.functions.properties.ProximalOperator'>,
 <class 'parsimony.functions.properties.Continuation'>,
 <class 'parsimony.functions.properties.DualFunction'>]

INFO_PROVIDED

Value:
['ok',
 'converged',
 'num_iter',
 'continuations',
 'time',
 'fvalue',
 'gap',
 'mu']

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