A Julia package for defining and working with linear maps, also known as linear transformations or linear operators acting on vectors. The only requirement for a LinearMap is that it can act on a vector (by multiplication) efficiently.
Updated to the new terminology issymmetric
instead of issym
. Note that the corresponding keyword argument for the LinearMap
constructor has been modified accordingly.
Internal changes to better ensure type stability, especially for FunctionMap
objects, but also for linear combinations and compositions.
Install with the package manager, i.e. Pkg.add("LinearMaps")
.
Several iterative linear algebra methods such as linear solvers or eigensolvers only require an efficient evaluation of the matrix vector product, where the concept of a matrix can be formalized / generalized to a linear map (or linear operator in the special case of a square matrix).
The LinearMaps package provides the following functionality:
An AbstractLinearMap
type that shares with the AbstractMatrix
type that it responds to the functions size
, eltype
, isreal
, issymmetric
, ishermitian
and isposdef
, transpose
and ctranspose
and multiplication with a vector using both *
or the in-place version A_mul_B!
. Depending on the subtype, also At_mul_B
, At_mul_B!
, Ac_mul_B
and Ac_mul_B!
are supported. Linear algebra functions that uses duck-typing for its arguments can handle AbstractLinearMap
objects similar to AbstractMatrix
objects, provided that they can be written using the above methods. Unlike AbstractMatrix
types, AbstractLinearMap
objects cannot be indexed, neither using getindex
or setindex!
.
A single method LinearMap
function that acts as a general purpose constructor (though it is not a real type) and allows to construct AbstractLinearMap
objects from functions, or to wrap objects of type AbstractMatrix
or AbstractLinearMap
. This method thus can also be used to (re)define the properties (isreal
, issymmetric
, ishermitian
, isposdef
) of the corresponding linear map.
A framework for combining objects of type AbstractLinearMap
and of type AbstractMatrix
using linear combinations, transposition and composition, where the linear map resulting from these operations is never explicitly evaluated but only its matrix vector product is defined (i.e. lazy evaluation). The matrix vector product is written to minimize memory allocation by using a minimal number of temporary vectors. There is full support for the in-place version A_mul_B!
, which should be preferred for higher efficiency in critical algorithms. In addition, it tries to recognize the properties of combinations of linear maps. In particular, compositions such as A'*A
for arbitrary A
or even A'*B*C*B'*A
with arbitrary A
and B
and positive definite C
are recognized as being positive definite and hermitian. In case a certain property of the resulting AbstractLinearMap
object is not correctly inferred, the LinearMap
method can be called to redefine the properties.
LinearMap
General purpose method to construct AbstractLinearMap objects of specific types, as described in the Types section below
LinearMap(A::AbstractMatrix[; isreal::Bool, issymmetric::Bool, ishermitian::Bool, isposdef::Bool])
LinearMap(A::AbstractLinearMap[; isreal::Bool, issym::Bool, ishermitian::Bool, isposdef::Bool])
Create a WrappedMap
object that will respond to the methods isreal
, issymmetric
, ishermitian
, isposdef
with the values provided by the keyword arguments. The default values correspond to the result of calling these methods on the argument A
. This allows to use an AbstractMatrix
within the AbstractLinearMap
framework and to redefine the properties of an existing AbstractLinearMap
.
LinearMap(f, [fc = nothing], M::Int, [N::Int = M, eltype::Type = Float64]; ismutating::Bool, issymmetric::Bool, ishermitian::Bool, isposdef::Bool])
Create FunctionMap
object that wraps a function describing the action of the linear map on a vector. The corresponding properties of the linear map can also be specified. Here, f
represents the function implementing the action of the linear map on a vector, either as returning the result (i.e. f(src::AbstractVector) -> dest::AbstractVector
) when ismutating = false
(default) or as a mutating function that accepts a vector for the destination (i.e. f(dest::AbstractVector,src::AbstractVector) -> dest
).
A second function can optionally be provided that implements the action of the adjoint (transposed) linear map. Here, it is always assumed that this represents the conjugate transpose, though this is of course equivalent to the normal transpose for real linear maps. Furthermore, the conjugate transpose also enables the use of At_mul_B(!)
using some extra conjugation calls on the input and output vector. If no second function is provided, than At_mul_B(!)
and Ac_mul_B(!)
cannot be used with this linear map, unless it is symmetric or hermitian.
M
is the number of rows (length of the output vectors) and N
the number of columns (length of the input vectors). When the latter is not specified, N = M
.
Finally, one can specify the eltype
of the resulting linear map as final normal argument, where a default value of Float64
is assumed. If the function acts as a complex linear map, than one should provide a complex type such as Complex128
.
The keyword arguments and their default values are:
ismutating [=false]
: false
if the function f
accepts a single vector argument corresponding to the input, and true
if they accept two vector arguments where the first will be mutated so as to contain the result. In both cases, the resulting A::FunctionMap
will support both the mutating as nonmutating matrix vector multiplication.issymmetric [=false]
: whether the function represents the multiplication with a symmetric matrix. If true
, this will automatically enable A'*x
and A.'*x
.ishermitian [=T<:Real && issymmetric]
: whether the function represents the multiplication with a hermitian matrix. If true
, this will automatically enable A'*x
and A.'*x
.isposdef [=false]
: whether the function represents the multiplication with a positive definite matrix.Base.full(linearmap)
Creates a full matrix representation of the linearmap object, by multiplying it with the successive basis vectors. This is mostly for testing purposes
All matrix multiplication methods and the corresponding mutating versions.
None of the types below need to be constructed directly; they arise from performing operations between AbstractLinearMap
objects or by calling the LinearMap
method described above.
AbstractLinearMap
Abstract supertype
FunctionMap
Type for wrapping an arbitrary function that is supposed to implement the matrix vector product as an AbstractLinearMap
.
WrappedMap
Type for wrapping an AbstractMatrix
or AbstractLinearMap
and to possible redefine the properties isreal
, issym
, ishermitian
and isposdef
. An AbstractMatrix
will automatically be converted to a WrappedMap
when it is combined with other AbstractLinearMap
objects via linear combination or composition (multiplication). Note that WrappedMap(mat1)*WrappedMap(mat2)
will never evaluate mat1*mat2
, since this is more costly then evaluating mat1*(mat2*x)
and the latter is the only operation that needs to be performed by AbstractLinearMap
objects anyway. While the cost of matrix addition is comparible to matrix vector multiplication, this too is not performed explicitly since this would require new storage of the same amount as of the original matrices.
IdentityMap
Type for representing the identity map of a certain size M=N
, obtained simply as IdentityMap{T}(M)
, IdentityMap(T,M)=IdentityMap(T,M,N)=IdentityMap(T,(M,N))
or even IdentityMap(M)=IdentityMap(M,N)=IdentityMap((M,N))
. If T
is not specified, Bool
is assumed, since operations between Bool
and any other Number
will always be converted to the type of the other Number
. If M!=N
, an error is returned. An IdentityMap
of the correct size and element type will automatically be created if LinearMap
objects are combined with I
, Julia's built in identity (UniformScaling
).
LinearCombination
, CompositeMap
, TransposeMap
and CTransposeMap
Used to add and multiply LinearMap
objects, don't need to be constructed explicitly.
The LinearMap
object combines well with the iterative eigensolver eigs
, which is the Julia wrapper for Arpack.
using LinearMaps
function leftdiff!(y::AbstractVector, x::AbstractVector) # left difference assuming periodic boundary conditions
N=length(x)
length(y)==N || throw(DimensionMismatch())
@inbounds for i=1:N
y[i]=x[i]-x[mod1(i-1,N)]
end
return y
end
function mrightdiff!(y::AbstractVector, x::AbstractVector) # minus right difference
N=length(x)
length(y)==N || throw(DimensionMismatch())
@inbounds for i=1:N
y[i]=x[i]-x[mod1(i+1,N)]
end
return y
end
D=LinearMap(leftdiff!, mrightdiff!, 100; ismutating=true)
eigs(D'*D;nev=3,which=:SR)
julia-observer-html-cut-paste-3__work
06/15/2014
9 days ago
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