Solver state
Given an AbstractManoptProblem
, that is a certain optimisation task, the state specifies the solver to use. It contains the parameters of a solver and all fields necessary during the algorithm, for example the current iterate, a StoppingCriterion
or a Stepsize
.
Manopt.AbstractManoptSolverState
— TypeAbstractManoptSolverState
A general super type for all solver states.
Fields
The following fields are assumed to be default. If you use different ones, adapt the the access functions get_iterate
and get_stopping_criterion
accordingly
p::P
: a point on the manifold $\mathcal M$storing the current iteratestop::StoppingCriterion
: a functor indicating that the stopping criterion is fulfilled
Manopt.get_state
— Functionget_state(s::AbstractManoptSolverState, recursive::Bool=true)
return the (one step) undecorated AbstractManoptSolverState
of the (possibly) decorated s
. As long as your decorated state stores the state within s.state
and the dispatch_objective_decorator
is set to Val{true}
, the internal state are extracted automatically.
By default the state that is stored within a decorated state is assumed to be at s.state
. Overwrite _get_state(s, ::Val{true}, recursive) to change this behaviour for your state
s` for both the recursive and the direct case.
If recursive
is set to false
, only the most outer decorator is taken away instead of all.
Manopt.get_count
— Functionget_count(ams::AbstractManoptSolverState, ::Symbol)
Obtain the count for a certain countable size, for example the :Iterations
. This function returns 0 if there was nothing to count
Available symbols from within the solver state
:Iterations
is passed on to thestop
field to obtain the iteration at which the solver stopped.
get_count(co::ManifoldCountObjective, s::Symbol, mode::Symbol=:None)
Get the number of counts for a certain symbol s
.
Depending on the mode
different results appear if the symbol does not exist in the dictionary
:None
: (default) silent mode, returns-1
for non-existing entries:warn
: issues a warning if a field does not exist:error
: issues an error if a field does not exist
Since every subtype of an AbstractManoptSolverState
directly relate to a solver, the concrete states are documented together with the corresponding solvers. This page documents the general features available for every state.
A first example is to obtain or set, the current iterate. This might be useful to continue investigation at the current iterate, or to set up a solver for a next experiment, respectively.
Manopt.get_iterate
— Functionget_iterate(O::AbstractManoptSolverState)
return the (last stored) iterate within AbstractManoptSolverState
s`. This should usually refer to a single point on the manifold the solver is working on
By default this also removes all decorators of the state beforehand.
get_iterate(agst::AbstractGradientSolverState)
return the iterate stored within gradient options. THe default returns agst.p
.
Manopt.set_iterate!
— Functionset_iterate!(s::AbstractManoptSolverState, M::AbstractManifold, p)
set the iterate within an AbstractManoptSolverState
to some (start) value p
.
set_iterate!(agst::AbstractGradientSolverState, M, p)
set the (current) iterate stored within an AbstractGradientSolverState
to p
. The default function modifies s.p
.
Manopt.get_gradient
— Methodget_gradient(s::AbstractManoptSolverState)
return the (last stored) gradient within AbstractManoptSolverState
s`. By default also undecorates the state beforehand
Manopt.set_gradient!
— Functionset_gradient!(s::AbstractManoptSolverState, M::AbstractManifold, p, X)
set the gradient within an (possibly decorated) AbstractManoptSolverState
to some (start) value X
in the tangent space at p
.
set_gradient!(agst::AbstractGradientSolverState, M, p, X)
set the (current) gradient stored within an AbstractGradientSolverState
to X
. The default function modifies s.X
.
An internal function working on the state and elements within a state is used to pass messages from (sub) activities of a state to the corresponding DebugMessages
Manopt.get_message
— Functionget_message(du::AbstractManoptSolverState)
get a message (String) from internal functors, in a summary. This should return any message a sub-step might have issued as well.
Furthermore, to access the stopping criterion use
Manopt.get_stopping_criterion
— Functionget_stopping_criterion(ams::AbstractManoptSolverState)
Return the StoppingCriterion
stored within the AbstractManoptSolverState
ams
.
For an undecorated state, this is assumed to be in ams.stop
. Overwrite _get_stopping_criterion(yms::YMS)
to change this for your manopt solver (yms
) assuming it has type YMS`.
Decorators for AbstractManoptSolverState
s
A solver state can be decorated using the following trait and function to initialize
Manopt.dispatch_state_decorator
— Functiondispatch_state_decorator(s::AbstractManoptSolverState)
Indicate internally, whether an AbstractManoptSolverState
s
is of decorating type, and stores (encapsulates) a state in itself, by default in the field s.state
.
Decorators indicate this by returning Val{true}
for further dispatch.
The default is Val{false}
, so by default a state is not decorated.
Manopt.is_state_decorator
— Functionis_state_decorator(s::AbstractManoptSolverState)
Indicate, whether AbstractManoptSolverState
s
are of decorator type.
Manopt.decorate_state!
— Functiondecorate_state!(s::AbstractManoptSolverState)
decorate the AbstractManoptSolverState
s
with specific decorators.
Optional arguments
optional arguments provide necessary details on the decorators.
debug=Array{Union{Symbol,DebugAction,String,Int},1}()
: a set of symbols representingDebugAction
s,Strings
used as dividers and a sub-sampling integer. These are passed as aDebugGroup
within:Iteration
to theDebugSolverState
decorator dictionary. Only exception is:Stop
that is passed to:Stop
.record=Array{Union{Symbol,RecordAction,Int},1}()
: specify recordings by usingSymbol
s orRecordAction
s directly. An integer can again be used for only recording every $i$th iteration.return_state=false
: indicate whether to wrap the options in aReturnSolverState
, indicating that the solver should return options and not (only) the minimizer.
other keywords are ignored.
See also
A simple example is the
Manopt.ReturnSolverState
— TypeReturnSolverState{O<:AbstractManoptSolverState} <: AbstractManoptSolverState
This internal type is used to indicate that the contained AbstractManoptSolverState
state
should be returned at the end of a solver instead of the usual minimizer.
See also
as well as DebugSolverState
and RecordSolverState
.
State actions
A state action is a struct for callback functions that can be attached within for example the just mentioned debug decorator or the record decorator.
Manopt.AbstractStateAction
— TypeAbstractStateAction
a common Type
for AbstractStateActions
that might be triggered in decorators, for example within the DebugSolverState
or within the RecordSolverState
.
Several state decorators or actions might store intermediate values like the (last) iterate to compute some change or the last gradient. In order to minimise the storage of these, there is a generic StoreStateAction
that acts as generic common storage that can be shared among different actions.
Manopt.StoreStateAction
— TypeStoreStateAction <: AbstractStateAction
internal storage for AbstractStateAction
s to store a tuple of fields from an AbstractManoptSolverState
s
This functor possesses the usual interface of functions called during an iteration and acts on (p, s, k)
, where p
is a AbstractManoptProblem
, s
is an AbstractManoptSolverState
and k
is the current iteration.
Fields
values
: a dictionary to store interim values based on certainSymbols
keys
: aVector
ofSymbols
to refer to fields ofAbstractManoptSolverState
point_values
: aNamedTuple
of mutable values of points on a manifold to be stored inStoreStateAction
. Manifold is later determined byAbstractManoptProblem
passed toupdate_storage!
.point_init
: aNamedTuple
of boolean values indicating whether a point inpoint_values
with matching key has been already initialized to a value. When it is false, it corresponds to a general value not being stored for the key present in the vectorkeys
.vector_values
: aNamedTuple
of mutable values of tangent vectors on a manifold to be stored inStoreStateAction
. Manifold is later determined byAbstractManoptProblem
passed toupdate_storage!
. It is not specified at which point the vectors are tangent but for storage it should not matter.vector_init
: aNamedTuple
of boolean values indicating whether a tangent vector invector_values
: with matching key has been already initialized to a value. When it is false, it corresponds to a general value not being stored for the key present in the vectorkeys
.once
: whether to update the internal values only once per iterationlastStored
: last iterate, where thisAbstractStateAction
was called (to determineonce
)
To handle the general storage, use get_storage
and has_storage
with keys as Symbol
s. For the point storage use PointStorageKey
. For tangent vector storage use VectorStorageKey
. Point and tangent storage have been optimized to be more efficient.
Constructors
StoreStateAction(s::Vector{Symbol})
This is equivalent as providing s
to the keyword store_fields
, just that here, no manifold is necessity for the construction.
StoreStateAction(M)
Keyword arguments
store_fields
(Symbol[]
)store_points
(Symbol[]
)store_vectors
(Symbol[]
)
as vectors of symbols each referring to fields of the state (lower case symbols) or semantic ones (upper case).
p_init
(rand(M)
) but making sure this is not a number but a (mutatable) arrayX_init
(zero_vector(M, p_init)
)
are used to initialize the point and vector storage, change these if you use other types (than the default) for your points/vectors on M
.
once
(true
) whether to update internal storage only once per iteration or on every update call
Manopt.get_storage
— Functionget_storage(a::AbstractStateAction, key::Symbol)
Return the internal value of the AbstractStateAction
a
at the Symbol
key
.
get_storage(a::AbstractStateAction, ::PointStorageKey{key}) where {key}
Return the internal value of the AbstractStateAction
a
at the Symbol
key
that represents a point.
get_storage(a::AbstractStateAction, ::VectorStorageKey{key}) where {key}
Return the internal value of the AbstractStateAction
a
at the Symbol
key
that represents a vector.
Manopt.has_storage
— Functionhas_storage(a::AbstractStateAction, key::Symbol)
Return whether the AbstractStateAction
a
has a value stored at the Symbol
key
.
has_storage(a::AbstractStateAction, ::PointStorageKey{key}) where {key}
Return whether the AbstractStateAction
a
has a point value stored at the Symbol
key
.
has_storage(a::AbstractStateAction, ::VectorStorageKey{key}) where {key}
Return whether the AbstractStateAction
a
has a point value stored at the Symbol
key
.
Manopt.update_storage!
— Functionupdate_storage!(a::AbstractStateAction, amp::AbstractManoptProblem, s::AbstractManoptSolverState)
Update the AbstractStateAction
a
internal values to the ones given on the AbstractManoptSolverState
s
. Optimized using the information from amp
update_storage!(a::AbstractStateAction, d::Dict{Symbol,<:Any})
Update the AbstractStateAction
a
internal values to the ones given in the dictionary d
. The values are merged, where the values from d
are preferred.
Manopt.PointStorageKey
— Typestruct PointStorageKey{key} end
Refer to point storage of StoreStateAction
in get_storage
and has_storage
functions
Manopt.VectorStorageKey
— Typestruct VectorStorageKey{key} end
Refer to tangent storage of StoreStateAction
in get_storage
and has_storage
functions
as well as two internal functions
Manopt._storage_copy_vector
— Function_storage_copy_vector(M::AbstractManifold, X)
Make a copy of tangent vector X
from manifold M
for storage in StoreStateAction
.
Manopt._storage_copy_point
— Function_storage_copy_point(M::AbstractManifold, p)
Make a copy of point p
from manifold M
for storage in StoreStateAction
.
Abstract states
In a few cases it is useful to have a hierarchy of types. These are
Manopt.AbstractSubProblemSolverState
— TypeAbstractSubProblemSolverState <: AbstractManoptSolverState
An abstract type for solvers that involve a subsolver.
Manopt.AbstractGradientSolverState
— TypeAbstractGradientSolverState <: AbstractManoptSolverState
A generic AbstractManoptSolverState
type for gradient based options data.
It assumes that
- the iterate is stored in the field
p
- the gradient at
p
is stored inX
.
See also
GradientDescentState
, StochasticGradientDescentState
, SubGradientMethodState
, QuasiNewtonState
.
Manopt.AbstractHessianSolverState
— TypeAbstractHessianSolverState <: AbstractGradientSolverState
An AbstractManoptSolverState
type to represent algorithms that employ the Hessian. These options are assumed to have a field (gradient
) to store the current gradient $\operatorname{grad}f(x)$
Manopt.AbstractPrimalDualSolverState
— TypeAbstractPrimalDualSolverState
A general type for all primal dual based options to be used within primal dual based algorithms
For the sub problem state, there are two access functions
Manopt.get_sub_problem
— Functionget_sub_problem(ams::AbstractSubProblemSolverState)
Access the sub problem of a solver state that involves a sub optimisation task. By default this returns ams.sub_problem
.
Manopt.get_sub_state
— Functionget_sub_state(ams::AbstractSubProblemSolverState)
Access the sub state of a solver state that involves a sub optimisation task. By default this returns ams.sub_state
.