From f4f9b1abdadd243e1c5ea232a7cbb8679468ead2 Mon Sep 17 00:00:00 2001 From: "Documenter.jl" Date: Tue, 26 Nov 2024 15:53:34 +0000 Subject: [PATCH] build based on 30a7fc0 --- dev/.documenter-siteinfo.json | 2 +- dev/API/regularization/index.html | 6 +- dev/API/solvers/index.html | 14 ++-- .../examples/compressed_sensing/1405d532.png | Bin 0 -> 62590 bytes .../examples/compressed_sensing/2c4e4ab0.png | Bin 62507 -> 0 bytes .../examples/compressed_sensing/60f52727.png | Bin 0 -> 33819 bytes .../examples/compressed_sensing/7fb3e75a.png | Bin 33663 -> 0 bytes .../examples/compressed_sensing/index.html | 66 ++++++++-------- .../examples/computed_tomography/index.html | 2 +- .../examples/getting_started/index.html | 2 +- .../explanations/regularization/index.html | 2 +- dev/generated/howto/callbacks/03173a28.png | Bin 0 -> 180936 bytes dev/generated/howto/callbacks/32b1ce77.png | Bin 179745 -> 0 bytes dev/generated/howto/callbacks/index.html | 40 +++++----- .../howto/efficient_kaczmarz/index.html | 66 ++++++++-------- .../howto/gpu_acceleration/99f8bbf3.png | Bin 0 -> 15647 bytes .../howto/gpu_acceleration/d10dcde8.png | Bin 15965 -> 0 bytes .../howto/gpu_acceleration/index.html | 72 +++++++++--------- .../howto/multi_threading/index.html | 4 +- .../howto/normal_operator/index.html | 64 ++++++++-------- dev/generated/howto/plug-and-play/index.html | 32 ++++---- dev/generated/howto/weighting/index.html | 2 +- dev/index.html | 2 +- dev/objects.inv | Bin 1588 -> 1588 bytes dev/solvers/index.html | 2 +- 25 files changed, 189 insertions(+), 189 deletions(-) create mode 100644 dev/generated/examples/compressed_sensing/1405d532.png delete mode 100644 dev/generated/examples/compressed_sensing/2c4e4ab0.png create mode 100644 dev/generated/examples/compressed_sensing/60f52727.png delete mode 100644 dev/generated/examples/compressed_sensing/7fb3e75a.png create mode 100644 dev/generated/howto/callbacks/03173a28.png delete mode 100644 dev/generated/howto/callbacks/32b1ce77.png create mode 100644 dev/generated/howto/gpu_acceleration/99f8bbf3.png delete mode 100644 dev/generated/howto/gpu_acceleration/d10dcde8.png diff --git a/dev/.documenter-siteinfo.json b/dev/.documenter-siteinfo.json index 14ac288..8f1ac5c 100644 --- a/dev/.documenter-siteinfo.json +++ b/dev/.documenter-siteinfo.json @@ -1 +1 @@ -{"documenter":{"julia_version":"1.11.1","generation_timestamp":"2024-11-26T15:52:26","documenter_version":"1.8.0"}} \ No newline at end of file +{"documenter":{"julia_version":"1.11.1","generation_timestamp":"2024-11-26T15:53:28","documenter_version":"1.8.0"}} \ No newline at end of file diff --git a/dev/API/regularization/index.html b/dev/API/regularization/index.html index 55475cc..e26b369 100644 --- a/dev/API/regularization/index.html +++ b/dev/API/regularization/index.html @@ -1,5 +1,5 @@ -Regularization Terms · RegularizedLeastSquares.jl

API for Regularizers

This page contains documentation of the public API of the RegularizedLeastSquares. In the Julia REPL one can access this documentation by entering the help mode with ?

RegularizedLeastSquares.L21Regularization — Type
L21Regularization

Regularization term implementing the proximal map for group-soft-thresholding.

Arguments

  • λ - regularization paramter

Keywords

  • slices=1 - number of elements per group
source
RegularizedLeastSquares.LLRRegularization — Type
LLRRegularization

Regularization term implementing the proximal map for locally low rank (LLR) regularization using singular-value-thresholding.

Arguments

  • λ - regularization paramter

Keywords

  • shape::Tuple{Int} - dimensions of the image
  • blockSize::Tuple{Int}=(2,2) - size of patches to perform singular value thresholding on
  • randshift::Bool=true - randomly shifts the patches to ensure translation invariance
  • fullyOverlapping::Bool=false - choose between fully overlapping block or non-overlapping blocks
source
RegularizedLeastSquares.NuclearRegularization — Type
NuclearRegularization

Regularization term implementing the proximal map for singular value soft-thresholding.

Arguments:

  • λ - regularization paramter

Keywords

  • svtShape::NTuple - size of the underlying matrix
source
RegularizedLeastSquares.TVRegularization — Type
TVRegularization

Regularization term implementing the proximal map for TV regularization. Calculated with the Condat algorithm if the TV is calculated only along one real-valued dimension and with the Fast Gradient Projection algorithm otherwise.

Reference for the Condat algorithm: https://lcondat.github.io/publis/Condat-fast_TV-SPL-2013.pdf

Reference for the FGP algorithm: A. Beck and T. Teboulle, "Fast Gradient-Based Algorithms for Constrained Total Variation Image Denoising and Deblurring Problems", IEEE Trans. Image Process. 18(11), 2009

Arguments

  • λ::T - regularization parameter

Keywords

  • shape::NTuple - size of the underlying image
  • dims - Dimension to perform the TV along. If Integer, the Condat algorithm is called, and the FDG algorithm otherwise.
  • iterationsTV=20 - number of FGP iterations
source

Projection Regularization

Nested Regularization

RegularizedLeastSquares.innerreg — Method
innerreg(reg::AbstractNestedRegularization)

return the inner regularization term of reg. Nested regularization terms also implement the iteration interface.

source

Scaled Regularization

Misc. Nested Regularization

RegularizedLeastSquares.MaskedRegularization — Type
MaskedRegularization

Nested regularization term that only applies prox! and norm to elements of x for which the mask is true.

Examples

julia> positive = PositiveRegularization();
+Regularization Terms · RegularizedLeastSquares.jl
API for Regularizers
This page contains documentation of the public API of the RegularizedLeastSquares. In the Julia REPL one can access this documentation by entering the help mode with ?
RegularizedLeastSquares.L21Regularization — Type
L21Regularization
Regularization term implementing the proximal map for group-soft-thresholding.
Arguments
  • λ                  - regularization paramter
Keywords
  • slices=1           - number of elements per group
source
RegularizedLeastSquares.LLRRegularization — Type
LLRRegularization
Regularization term implementing the proximal map for locally low rank (LLR) regularization using singular-value-thresholding.
Arguments
  • λ                  - regularization paramter
Keywords
  • shape::Tuple{Int}            - dimensions of the image
  • blockSize::Tuple{Int}=(2,2)  - size of patches to perform singular value thresholding on
  • randshift::Bool=true         - randomly shifts the patches to ensure translation invariance
  • fullyOverlapping::Bool=false - choose between fully overlapping block or non-overlapping blocks
source
RegularizedLeastSquares.NuclearRegularization — Type
NuclearRegularization
Regularization term implementing the proximal map for singular value soft-thresholding.
Arguments:
  • λ           - regularization paramter
Keywords
  • svtShape::NTuple  - size of the underlying matrix
source
RegularizedLeastSquares.TVRegularization — Type
TVRegularization
Regularization term implementing the proximal map for TV regularization. Calculated with the Condat algorithm if the TV is calculated only along one real-valued dimension and with the Fast Gradient Projection algorithm otherwise.
Reference for the Condat algorithm: https://lcondat.github.io/publis/Condat-fast_TV-SPL-2013.pdf
Reference for the FGP algorithm: A. Beck and T. Teboulle, "Fast Gradient-Based Algorithms for Constrained Total Variation Image Denoising and Deblurring Problems", IEEE Trans. Image Process. 18(11), 2009
Arguments
  • λ::T                    - regularization parameter
Keywords
  • shape::NTuple           - size of the underlying image
  • dims                    - Dimension to perform the TV along. If Integer, the Condat algorithm is called, and the FDG algorithm otherwise.
  • iterationsTV=20         - number of FGP iterations
source
Projection Regularization
Nested Regularization
RegularizedLeastSquares.innerreg — Method
innerreg(reg::AbstractNestedRegularization)
return the inner regularization term of reg. Nested regularization terms also implement the iteration interface.
source
Scaled Regularization
Misc. Nested Regularization
RegularizedLeastSquares.MaskedRegularization — Type
MaskedRegularization
Nested regularization term that only applies prox! and norm to elements of x for which the mask is true.
Examples
julia> positive = PositiveRegularization();
 
 julia> masked = MaskedRegularization(reg, [true, false, true, false]);
 
@@ -8,11 +8,11 @@
   0.0
  -1.0
   0.0
- -1.0
source
RegularizedLeastSquares.TransformedRegularization — Type
TransformedRegularization(reg, trafo)
Nested regularization term that applies prox! or norm on z = trafo * x and returns (inplace) x = adjoint(trafo) * z.
Example
julia> core = L1Regularization(0.8)
 L1Regularization{Float64}(0.8)
 
 julia> wop = WaveletOp(Float32, shape = (32,32));
 
 julia> reg = TransformedRegularization(core, wop);
 
-julia> prox!(reg, randn(32*32)); # Apply soft-thresholding in Wavelet domain
source
RegularizedLeastSquares.PlugAndPlayRegularization — Type
    PlugAndPlayRegularization
Regularization term implementing a given plug-and-play proximal mapping. The actual regularization term is indirectly defined by the learned proximal mapping and as such there is no norm implemented.
Arguments
  • λ                  - regularization paramter
Keywords
  • model       - model applied to the image
  • shape       - dimensions of the image
  • input_transform - transform of image before model
source
Miscellaneous Functions
RegularizedLeastSquares.prox! — Method
prox!(reg::AbstractParameterizedRegularization, x)
perform the proximal mapping defined by reg on x. Uses the regularization parameter defined for reg.
source
RegularizedLeastSquares.prox! — Method
prox!(regType::Type{<:AbstractParameterizedRegularization}, x, λ; kwargs...)
construct a regularization term of type regType with given λ and kwargs and apply its prox! on x
source
LinearAlgebra.norm — Method
norm(reg::AbstractParameterizedRegularization, x)
returns the value of the reg regularization term on x. Uses the regularization parameter defined for reg.
source
LinearAlgebra.norm — Method
norm(regType::Type{<:AbstractParameterizedRegularization}, x, λ; kwargs...)
construct a regularization term of type regType with given λ and kwargs and apply its norm on x
source

+julia> prox!(reg, randn(32*32)); # Apply soft-thresholding in Wavelet domain
source
RegularizedLeastSquares.PlugAndPlayRegularization — Type
    PlugAndPlayRegularization

Regularization term implementing a given plug-and-play proximal mapping. The actual regularization term is indirectly defined by the learned proximal mapping and as such there is no norm implemented.

Arguments

  • λ - regularization paramter

Keywords

  • model - model applied to the image
  • shape - dimensions of the image
  • input_transform - transform of image before model
source

Miscellaneous Functions

RegularizedLeastSquares.prox! — Method
prox!(reg::AbstractParameterizedRegularization, x)

perform the proximal mapping defined by reg on x. Uses the regularization parameter defined for reg.

source
RegularizedLeastSquares.prox! — Method
prox!(regType::Type{<:AbstractParameterizedRegularization}, x, λ; kwargs...)

construct a regularization term of type regType with given λ and kwargs and apply its prox! on x

source
LinearAlgebra.norm — Method
norm(reg::AbstractParameterizedRegularization, x)

returns the value of the reg regularization term on x. Uses the regularization parameter defined for reg.

source
LinearAlgebra.norm — Method
norm(regType::Type{<:AbstractParameterizedRegularization}, x, λ; kwargs...)

construct a regularization term of type regType with given λ and kwargs and apply its norm on x

source
diff --git a/dev/API/solvers/index.html b/dev/API/solvers/index.html index 8450ba9..d1a0efc 100644 --- a/dev/API/solvers/index.html +++ b/dev/API/solvers/index.html @@ -40,10 +40,10 @@ end plot_trace (generic function with 1 method) -julia> x_approx = solve!(S, b; callbacks = [conv, plot_trace]);

The keyword callbacks allows you to pass a (vector of) callable objects that takes the arguments solver and iteration and prints, stores, or plots intermediate result.

See also StoreSolutionCallback, StoreConvergenceCallback, CompareSolutionCallback for a number of provided callback options.

source
RegularizedLeastSquares.init! — Method
init!(solver::AbstractLinearSolver, b; kwargs...)

Prepare the solver for iteration based on the given data vector b and kwargs.

source
RegularizedLeastSquares.init! — Method
init!(solver::AbstractLinearSolver, state::AbstractSolverState, b::AbstractMatrix; scheduler = SequentialState, kwargs...)

Initialize the solver with each column of b and pass the corresponding states to the scheduler.

source

ADMM

RegularizedLeastSquares.ADMM — Type
ADMM(A; AHA = A'*A, precon = Identity(), reg = L1Regularization(zero(real(eltype(AHA)))), regTrafo = opEye(eltype(AHA), size(AHA,1)), normalizeReg = NoNormalization(), rho = 1e-1, vary_rho = :none, iterations = 10, iterationsCG = 10, absTol = eps(real(eltype(AHA))), relTol = eps(real(eltype(AHA))), tolInner = 1e-5, verbose = false)
-ADMM( ; AHA = ,     precon = Identity(), reg = L1Regularization(zero(real(eltype(AHA)))), regTrafo = opEye(eltype(AHA), size(AHA,1)), normalizeReg = NoNormalization(), rho = 1e-1, vary_rho = :none, iterations = 10, iterationsCG = 10, absTol = eps(real(eltype(AHA))), relTol = eps(real(eltype(AHA))), tolInner = 1e-5, verbose = false)

Creates an ADMM object for the forward operator A or normal operator AHA.

Required Arguments

  • A - forward operator

OR

  • AHA - normal operator (as a keyword argument)

Optional Keyword Arguments

  • AHA - normal operator is optional if A is supplied
  • precon - preconditionner for the internal CG algorithm
  • reg::AbstractParameterizedRegularization - regularization term; can also be a vector of regularization terms
  • regTrafo - transformation to a space in which reg is applied; if reg is a vector, regTrafo has to be a vector of the same length. Use opEye(eltype(AHA), size(AHA,1)) if no transformation is desired.
  • normalizeReg::AbstractRegularizationNormalization - regularization normalization scheme; options are NoNormalization(), MeasurementBasedNormalization(), SystemMatrixBasedNormalization()
  • rho::Real - penalty of the augmented Lagrangian
  • vary_rho::Symbol - vary rho to balance primal and dual feasibility; options :none, :balance, :PnP
  • iterations::Int - maximum number of (outer) ADMM iterations
  • iterationsCG::Int - maximum number of (inner) CG iterations
  • absTol::Real - absolute tolerance for stopping criterion
  • relTol::Real - relative tolerance for stopping criterion
  • tolInner::Real - relative tolerance for CG stopping criterion
  • verbose::Bool - print residual in each iteration

ADMM differs from ISTA-type algorithms in the sense that the proximal operation is applied separately from the transformation to the space in which the penalty is applied. This is reflected by the interface which has reg and regTrafo as separate arguments. E.g., for a TV penalty, you should NOT set reg=TVRegularization, but instead use reg=L1Regularization(λ), regTrafo=RegularizedLeastSquares.GradientOp(Float64; shape=(Nx,Ny,Nz)).

See also createLinearSolver, solve!.

source

CGNR

RegularizedLeastSquares.CGNR — Type
CGNR(A; AHA = A' * A, reg = L2Regularization(zero(real(eltype(AHA)))), normalizeReg = NoNormalization(), iterations = 10, relTol = eps(real(eltype(AHA))))
-CGNR( ; AHA = ,       reg = L2Regularization(zero(real(eltype(AHA)))), normalizeReg = NoNormalization(), iterations = 10, relTol = eps(real(eltype(AHA))))

creates an CGNR object for the forward operator A or normal operator AHA.

Required Arguments

  • A - forward operator

OR

  • AHA - normal operator (as a keyword argument)

Optional Keyword Arguments

  • AHA - normal operator is optional if A is supplied
  • reg::AbstractParameterizedRegularization - regularization term; can also be a vector of regularization terms
  • normalizeReg::AbstractRegularizationNormalization - regularization normalization scheme; options are NoNormalization(), MeasurementBasedNormalization(), SystemMatrixBasedNormalization()
  • iterations::Int - maximum number of iterations
  • relTol::Real - tolerance for stopping criterion

See also createLinearSolver, solve!.

source

Kaczmarz

RegularizedLeastSquares.Kaczmarz — Type
Kaczmarz(A; reg = L2Regularization(0), normalizeReg = NoNormalization(), randomized=false, subMatrixFraction=0.15, shuffleRows=false, seed=1234, iterations=10)

Creates a Kaczmarz object for the forward operator A.

Required Arguments

  • A - forward operator

Optional Keyword Arguments

  • reg::AbstractParameterizedRegularization - regularization term
  • normalizeReg::AbstractRegularizationNormalization - regularization normalization scheme; options are NoNormalization(), MeasurementBasedNormalization(), SystemMatrixBasedNormalization()
  • randomized::Bool - randomize Kacmarz algorithm
  • subMatrixFraction::Real - fraction of rows used in randomized Kaczmarz algorithm
  • shuffleRows::Bool - randomize Kacmarz algorithm
  • seed::Int - seed for randomized algorithm
  • iterations::Int - number of iterations

See also createLinearSolver, solve!.

source

FISTA

RegularizedLeastSquares.FISTA — Type
FISTA(A; AHA=A'*A, reg=L1Regularization(zero(real(eltype(AHA)))), normalizeReg=NoNormalization(), iterations=50, verbose = false, rho = 0.95 / power_iterations(AHA), theta=1, relTol=eps(real(eltype(AHA))), restart = :none)
-FISTA( ; AHA=,     reg=L1Regularization(zero(real(eltype(AHA)))), normalizeReg=NoNormalization(), iterations=50, verbose = false, rho = 0.95 / power_iterations(AHA), theta=1, relTol=eps(real(eltype(AHA))), restart = :none)

creates a FISTA object for the forward operator A or normal operator AHA.

Required Arguments

  • A - forward operator

OR

  • AHA - normal operator (as a keyword argument)

Optional Keyword Arguments

  • AHA - normal operator is optional if A is supplied
  • precon - preconditionner for the internal CG algorithm
  • reg::AbstractParameterizedRegularization - regularization term; can also be a vector of regularization terms
  • normalizeReg::AbstractRegularizationNormalization - regularization normalization scheme; options are NoNormalization(), MeasurementBasedNormalization(), SystemMatrixBasedNormalization()
  • rho::Real - step size for gradient step; the default is 0.95 / max_eigenvalue as determined with power iterations.
  • theta::Real - parameter for predictor-corrector step
  • relTol::Real - tolerance for stopping criterion
  • iterations::Int - maximum number of iterations
  • restart::Symbol - :none, :gradient options for restarting
  • verbose::Bool - print residual in each iteration

See also createLinearSolver, solve!.

source

OptISTA

RegularizedLeastSquares.OptISTA — Type
OptISTA(A; AHA=A'*A, reg=L1Regularization(zero(real(eltype(AHA)))), normalizeReg=NoNormalization(), iterations=50, verbose = false, rho=0.95 / power_iterations(AHA), theta=1, relTol=eps(real(eltype(AHA))))
-OptISTA( ; AHA=,     reg=L1Regularization(zero(real(eltype(AHA)))), normalizeReg=NoNormalization(), iterations=50, verbose = false, rho=0.95 / power_iterations(AHA), theta=1, relTol=eps(real(eltype(AHA))))

creates a OptISTA object for the forward operator A or normal operator AHA. OptISTA has a 2x better worst-case bound than FISTA, but actual performance varies by application. It stores 2 extra intermediate variables the size of the image compared to FISTA.

Reference:

  • Uijeong Jang, Shuvomoy Das Gupta, Ernest K. Ryu, "Computer-Assisted Design of Accelerated Composite Optimization Methods: OptISTA," arXiv:2305.15704, 2023, [https://arxiv.org/abs/2305.15704]

Required Arguments

  • A - forward operator

OR

  • AHA - normal operator (as a keyword argument)

Optional Keyword Arguments

  • AHA - normal operator is optional if A is supplied
  • reg::AbstractParameterizedRegularization - regularization term
  • normalizeReg::AbstractRegularizationNormalization - regularization normalization scheme; options are NoNormalization(), MeasurementBasedNormalization(), SystemMatrixBasedNormalization()
  • rho::Real - step size for gradient step; the default is 0.95 / max_eigenvalue as determined with power iterations.
  • theta::Real - parameter for predictor-corrector step
  • relTol::Real - tolerance for stopping criterion
  • iterations::Int - maximum number of iterations
  • verbose::Bool - print residual in each iteration

See also createLinearSolver, solve!.

source

POGM

RegularizedLeastSquares.POGM — Type
POGM(A; AHA = A'*A, reg = L1Regularization(zero(real(eltype(AHA)))), normalizeReg = NoNormalization(), iterations = 50, verbose = false, rho = 0.95 / power_iterations(AHA), theta = 1, sigma_fac = 1, relTol = eps(real(eltype(AHA))), restart = :none)
-POGM( ; AHA = ,     reg = L1Regularization(zero(real(eltype(AHA)))), normalizeReg = NoNormalization(), iterations = 50, verbose = false, rho = 0.95 / power_iterations(AHA), theta = 1, sigma_fac = 1, relTol = eps(real(eltype(AHA))), restart = :none)

Creates a POGM object for the forward operator A or normal operator AHA. POGM has a 2x better worst-case bound than FISTA, but actual performance varies by application. It stores 3 extra intermediate variables the size of the image compared to FISTA. Only gradient restart scheme is implemented for now.

References:

  • A.B. Taylor, J.M. Hendrickx, F. Glineur, "Exact worst-case performance of first-order algorithms for composite convex optimization," Arxiv:1512.07516, 2015, SIAM J. Opt. 2017 [http://doi.org/10.1137/16m108104x]

  • Kim, D., & Fessler, J. A. (2018). Adaptive Restart of the Optimized Gradient Method for Convex Optimization. Journal of Optimization Theory and Applications, 178(1), 240–263. [https://doi.org/10.1007/s10957-018-1287-4]

    Required Arguments

    • A - forward operator

    OR

    • AHA - normal operator (as a keyword argument)

    Optional Keyword Arguments

    • AHA - normal operator is optional if A is supplied
    • reg::AbstractParameterizedRegularization - regularization term
    • normalizeReg::AbstractRegularizationNormalization - regularization normalization scheme; options are NoNormalization(), MeasurementBasedNormalization(), SystemMatrixBasedNormalization()
    • rho::Real - step size for gradient step; the default is 0.95 / max_eigenvalue as determined with power iterations.
    • theta::Real - parameter for predictor-corrector step
    • sigma_fac::Real - parameter for decreasing γ-momentum ∈ [0,1]
    • relTol::Real - tolerance for stopping criterion
    • iterations::Int - maximum number of iterations
    • restart::Symbol - :none, :gradient options for restarting
    • verbose::Bool - print residual in each iteration

See also createLinearSolver, solve!.

source

SplitBregman

RegularizedLeastSquares.SplitBregman — Type
SplitBregman(A; AHA = A'*A, precon = Identity(), reg = L1Regularization(zero(real(eltype(AHA)))), regTrafo = opEye(eltype(AHA), size(AHA,1)), normalizeReg = NoNormalization(), rho = 1e-1, iterations = 10, iterationsInner = 10, iterationsCG = 10, absTol = eps(real(eltype(AHA))), relTol = eps(real(eltype(AHA))), tolInner = 1e-5, verbose = false)
-SplitBregman( ; AHA = ,     precon = Identity(), reg = L1Regularization(zero(real(eltype(AHA)))), regTrafo = opEye(eltype(AHA), size(AHA,1)), normalizeReg = NoNormalization(), rho = 1e-1, iterations = 10, iterationsInner = 10, iterationsCG = 10, absTol = eps(real(eltype(AHA))), relTol = eps(real(eltype(AHA))), tolInner = 1e-5, verbose = false)

Creates a SplitBregman object for the forward operator A or normal operator AHA.

Required Arguments

  • A - forward operator

OR

  • AHA - normal operator (as a keyword argument)

Optional Keyword Arguments

  • AHA - normal operator is optional if A is supplied
  • precon - preconditionner for the internal CG algorithm
  • reg::AbstractParameterizedRegularization - regularization term; can also be a vector of regularization terms
  • regTrafo - transformation to a space in which reg is applied; if reg is a vector, regTrafo has to be a vector of the same length. Use opEye(eltype(AHA), size(AHA,1)) if no transformation is desired.
  • normalizeReg::AbstractRegularizationNormalization - regularization normalization scheme; options are NoNormalization(), MeasurementBasedNormalization(), SystemMatrixBasedNormalization()
  • rho::Real - weights for condition on regularized variables; can also be a vector for multiple regularization terms
  • iterations::Int - maximum number of outer iterations. Set to 1 for unconstraint split Bregman (equivalent to ADMM)
  • iterationsInner::Int - maximum number of inner iterations
  • iterationsCG::Int - maximum number of (inner) CG iterations
  • absTol::Real - absolute tolerance for stopping criterion
  • relTol::Real - relative tolerance for stopping criterion
  • tolInner::Real - relative tolerance for CG stopping criterion
  • verbose::Bool - print residual in each iteration

This algorithm solves the constraint problem (Eq. (4.7) in Tom Goldstein and Stanley Osher), i.e. ||R(x)||₁ such that ||Ax -b||₂² < σ². In order to solve the unconstraint problem (Eq. (4.8) in Tom Goldstein and Stanley Osher), i.e. ||Ax -b||₂² + λ ||R(x)||₁, you can either set iterations=1 or use ADMM instead, which is equivalent (iterations=1 in SplitBregman in implied in ADMM and the SplitBregman variable iterationsInner is simply called iterations in ADMM)

Like ADMM, SplitBregman differs from ISTA-type algorithms in the sense that the proximal operation is applied separately from the transformation to the space in which the penalty is applied. This is reflected by the interface which has reg and regTrafo as separate arguments. E.g., for a TV penalty, you should NOT set reg=TVRegularization, but instead use reg=L1Regularization(λ), regTrafo=RegularizedLeastSquares.GradientOp(Float64; shape=(Nx,Ny,Nz)).

See also createLinearSolver, solve!.

source

Miscellaneous

RegularizedLeastSquares.solverstate — Function
solverstate(solver::AbstractLinearSolver)

Return the current state of the solver

source
RegularizedLeastSquares.solversolution — Function
solversolution(solver::AbstractLinearSolver)

Return the current solution of the solver

source
solversolution(state::AbstractSolverState)

Return the current solution of the solver's state

source
RegularizedLeastSquares.solverconvergence — Function
solverconvergence(solver::AbstractLinearSolver)

Return a named tuple of the solvers current convergence metrics

source
RegularizedLeastSquares.StoreSolutionCallback — Type
StoreSolutionCallback(T)

Callback that accumlates the solvers solution per iteration. Results are stored in the solutions field.

source
RegularizedLeastSquares.StoreConvergenceCallback — Type
StoreConvergenceCallback()

Callback that accumlates the solvers convergence metrics per iteration. Results are stored in the convMeas field.

source
RegularizedLeastSquares.CompareSolutionCallback — Type
CompareSolutionCallback(ref, cmp)

Callback that compares the solvers current solution with the given reference via cmp(ref, solution) per iteration. Results are stored in the results field.

source
RegularizedLeastSquares.linearSolverList — Function

Return a list of all available linear solvers

source
RegularizedLeastSquares.createLinearSolver — Function
createLinearSolver(solver::AbstractLinearSolver, A; kargs...)

This method creates a solver. The supported solvers are methods typically used for solving regularized linear systems. All solvers return an approximate solution to Ax = b.

TODO: give a hint what solvers are available

source
RegularizedLeastSquares.applicableSolverList — Function
applicable(args...)

list all solvers that are applicable to the given arguments. Arguments are the same as for isapplicable without the solver type.

See also isapplicable, linearSolverList.

source
RegularizedLeastSquares.isapplicable — Function
isapplicable(solverType::Type{<:AbstractLinearSolver}, A, x, reg)

return true if a solver of type solverType is applicable to system matrix A, data x and regularization terms reg.

source
+julia> x_approx = solve!(S, b; callbacks = [conv, plot_trace]);

The keyword callbacks allows you to pass a (vector of) callable objects that takes the arguments solver and iteration and prints, stores, or plots intermediate result.

See also StoreSolutionCallback, StoreConvergenceCallback, CompareSolutionCallback for a number of provided callback options.

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RegularizedLeastSquares.init! — Method
init!(solver::AbstractLinearSolver, b; kwargs...)

Prepare the solver for iteration based on the given data vector b and kwargs.

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RegularizedLeastSquares.init! — Method
init!(solver::AbstractLinearSolver, state::AbstractSolverState, b::AbstractMatrix; scheduler = SequentialState, kwargs...)

Initialize the solver with each column of b and pass the corresponding states to the scheduler.

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ADMM

RegularizedLeastSquares.ADMM — Type
ADMM(A; AHA = A'*A, precon = Identity(), reg = L1Regularization(zero(real(eltype(AHA)))), regTrafo = opEye(eltype(AHA), size(AHA,1)), normalizeReg = NoNormalization(), rho = 1e-1, vary_rho = :none, iterations = 10, iterationsCG = 10, absTol = eps(real(eltype(AHA))), relTol = eps(real(eltype(AHA))), tolInner = 1e-5, verbose = false)
+ADMM( ; AHA = ,     precon = Identity(), reg = L1Regularization(zero(real(eltype(AHA)))), regTrafo = opEye(eltype(AHA), size(AHA,1)), normalizeReg = NoNormalization(), rho = 1e-1, vary_rho = :none, iterations = 10, iterationsCG = 10, absTol = eps(real(eltype(AHA))), relTol = eps(real(eltype(AHA))), tolInner = 1e-5, verbose = false)

Creates an ADMM object for the forward operator A or normal operator AHA.

Required Arguments

  • A - forward operator

OR

  • AHA - normal operator (as a keyword argument)

Optional Keyword Arguments

  • AHA - normal operator is optional if A is supplied
  • precon - preconditionner for the internal CG algorithm
  • reg::AbstractParameterizedRegularization - regularization term; can also be a vector of regularization terms
  • regTrafo - transformation to a space in which reg is applied; if reg is a vector, regTrafo has to be a vector of the same length. Use opEye(eltype(AHA), size(AHA,1)) if no transformation is desired.
  • normalizeReg::AbstractRegularizationNormalization - regularization normalization scheme; options are NoNormalization(), MeasurementBasedNormalization(), SystemMatrixBasedNormalization()
  • rho::Real - penalty of the augmented Lagrangian
  • vary_rho::Symbol - vary rho to balance primal and dual feasibility; options :none, :balance, :PnP
  • iterations::Int - maximum number of (outer) ADMM iterations
  • iterationsCG::Int - maximum number of (inner) CG iterations
  • absTol::Real - absolute tolerance for stopping criterion
  • relTol::Real - relative tolerance for stopping criterion
  • tolInner::Real - relative tolerance for CG stopping criterion
  • verbose::Bool - print residual in each iteration

ADMM differs from ISTA-type algorithms in the sense that the proximal operation is applied separately from the transformation to the space in which the penalty is applied. This is reflected by the interface which has reg and regTrafo as separate arguments. E.g., for a TV penalty, you should NOT set reg=TVRegularization, but instead use reg=L1Regularization(λ), regTrafo=RegularizedLeastSquares.GradientOp(Float64; shape=(Nx,Ny,Nz)).

See also createLinearSolver, solve!.

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CGNR

RegularizedLeastSquares.CGNR — Type
CGNR(A; AHA = A' * A, reg = L2Regularization(zero(real(eltype(AHA)))), normalizeReg = NoNormalization(), iterations = 10, relTol = eps(real(eltype(AHA))))
+CGNR( ; AHA = ,       reg = L2Regularization(zero(real(eltype(AHA)))), normalizeReg = NoNormalization(), iterations = 10, relTol = eps(real(eltype(AHA))))

creates an CGNR object for the forward operator A or normal operator AHA.

Required Arguments

  • A - forward operator

OR

  • AHA - normal operator (as a keyword argument)

Optional Keyword Arguments

  • AHA - normal operator is optional if A is supplied
  • reg::AbstractParameterizedRegularization - regularization term; can also be a vector of regularization terms
  • normalizeReg::AbstractRegularizationNormalization - regularization normalization scheme; options are NoNormalization(), MeasurementBasedNormalization(), SystemMatrixBasedNormalization()
  • iterations::Int - maximum number of iterations
  • relTol::Real - tolerance for stopping criterion

See also createLinearSolver, solve!.

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Kaczmarz

RegularizedLeastSquares.Kaczmarz — Type
Kaczmarz(A; reg = L2Regularization(0), normalizeReg = NoNormalization(), randomized=false, subMatrixFraction=0.15, shuffleRows=false, seed=1234, iterations=10)

Creates a Kaczmarz object for the forward operator A.

Required Arguments

  • A - forward operator

Optional Keyword Arguments

  • reg::AbstractParameterizedRegularization - regularization term
  • normalizeReg::AbstractRegularizationNormalization - regularization normalization scheme; options are NoNormalization(), MeasurementBasedNormalization(), SystemMatrixBasedNormalization()
  • randomized::Bool - randomize Kacmarz algorithm
  • subMatrixFraction::Real - fraction of rows used in randomized Kaczmarz algorithm
  • shuffleRows::Bool - randomize Kacmarz algorithm
  • seed::Int - seed for randomized algorithm
  • iterations::Int - number of iterations

See also createLinearSolver, solve!.

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FISTA

RegularizedLeastSquares.FISTA — Type
FISTA(A; AHA=A'*A, reg=L1Regularization(zero(real(eltype(AHA)))), normalizeReg=NoNormalization(), iterations=50, verbose = false, rho = 0.95 / power_iterations(AHA), theta=1, relTol=eps(real(eltype(AHA))), restart = :none)
+FISTA( ; AHA=,     reg=L1Regularization(zero(real(eltype(AHA)))), normalizeReg=NoNormalization(), iterations=50, verbose = false, rho = 0.95 / power_iterations(AHA), theta=1, relTol=eps(real(eltype(AHA))), restart = :none)

creates a FISTA object for the forward operator A or normal operator AHA.

Required Arguments

  • A - forward operator

OR

  • AHA - normal operator (as a keyword argument)

Optional Keyword Arguments

  • AHA - normal operator is optional if A is supplied
  • precon - preconditionner for the internal CG algorithm
  • reg::AbstractParameterizedRegularization - regularization term; can also be a vector of regularization terms
  • normalizeReg::AbstractRegularizationNormalization - regularization normalization scheme; options are NoNormalization(), MeasurementBasedNormalization(), SystemMatrixBasedNormalization()
  • rho::Real - step size for gradient step; the default is 0.95 / max_eigenvalue as determined with power iterations.
  • theta::Real - parameter for predictor-corrector step
  • relTol::Real - tolerance for stopping criterion
  • iterations::Int - maximum number of iterations
  • restart::Symbol - :none, :gradient options for restarting
  • verbose::Bool - print residual in each iteration

See also createLinearSolver, solve!.

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OptISTA

RegularizedLeastSquares.OptISTA — Type
OptISTA(A; AHA=A'*A, reg=L1Regularization(zero(real(eltype(AHA)))), normalizeReg=NoNormalization(), iterations=50, verbose = false, rho=0.95 / power_iterations(AHA), theta=1, relTol=eps(real(eltype(AHA))))
+OptISTA( ; AHA=,     reg=L1Regularization(zero(real(eltype(AHA)))), normalizeReg=NoNormalization(), iterations=50, verbose = false, rho=0.95 / power_iterations(AHA), theta=1, relTol=eps(real(eltype(AHA))))

creates a OptISTA object for the forward operator A or normal operator AHA. OptISTA has a 2x better worst-case bound than FISTA, but actual performance varies by application. It stores 2 extra intermediate variables the size of the image compared to FISTA.

Reference:

  • Uijeong Jang, Shuvomoy Das Gupta, Ernest K. Ryu, "Computer-Assisted Design of Accelerated Composite Optimization Methods: OptISTA," arXiv:2305.15704, 2023, [https://arxiv.org/abs/2305.15704]

Required Arguments

  • A - forward operator

OR

  • AHA - normal operator (as a keyword argument)

Optional Keyword Arguments

  • AHA - normal operator is optional if A is supplied
  • reg::AbstractParameterizedRegularization - regularization term
  • normalizeReg::AbstractRegularizationNormalization - regularization normalization scheme; options are NoNormalization(), MeasurementBasedNormalization(), SystemMatrixBasedNormalization()
  • rho::Real - step size for gradient step; the default is 0.95 / max_eigenvalue as determined with power iterations.
  • theta::Real - parameter for predictor-corrector step
  • relTol::Real - tolerance for stopping criterion
  • iterations::Int - maximum number of iterations
  • verbose::Bool - print residual in each iteration

See also createLinearSolver, solve!.

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POGM

RegularizedLeastSquares.POGM — Type
POGM(A; AHA = A'*A, reg = L1Regularization(zero(real(eltype(AHA)))), normalizeReg = NoNormalization(), iterations = 50, verbose = false, rho = 0.95 / power_iterations(AHA), theta = 1, sigma_fac = 1, relTol = eps(real(eltype(AHA))), restart = :none)
+POGM( ; AHA = ,     reg = L1Regularization(zero(real(eltype(AHA)))), normalizeReg = NoNormalization(), iterations = 50, verbose = false, rho = 0.95 / power_iterations(AHA), theta = 1, sigma_fac = 1, relTol = eps(real(eltype(AHA))), restart = :none)

Creates a POGM object for the forward operator A or normal operator AHA. POGM has a 2x better worst-case bound than FISTA, but actual performance varies by application. It stores 3 extra intermediate variables the size of the image compared to FISTA. Only gradient restart scheme is implemented for now.

References:

  • A.B. Taylor, J.M. Hendrickx, F. Glineur, "Exact worst-case performance of first-order algorithms for composite convex optimization," Arxiv:1512.07516, 2015, SIAM J. Opt. 2017 [http://doi.org/10.1137/16m108104x]

  • Kim, D., & Fessler, J. A. (2018). Adaptive Restart of the Optimized Gradient Method for Convex Optimization. Journal of Optimization Theory and Applications, 178(1), 240–263. [https://doi.org/10.1007/s10957-018-1287-4]

    Required Arguments

    • A - forward operator

    OR

    • AHA - normal operator (as a keyword argument)

    Optional Keyword Arguments

    • AHA - normal operator is optional if A is supplied
    • reg::AbstractParameterizedRegularization - regularization term
    • normalizeReg::AbstractRegularizationNormalization - regularization normalization scheme; options are NoNormalization(), MeasurementBasedNormalization(), SystemMatrixBasedNormalization()
    • rho::Real - step size for gradient step; the default is 0.95 / max_eigenvalue as determined with power iterations.
    • theta::Real - parameter for predictor-corrector step
    • sigma_fac::Real - parameter for decreasing γ-momentum ∈ [0,1]
    • relTol::Real - tolerance for stopping criterion
    • iterations::Int - maximum number of iterations
    • restart::Symbol - :none, :gradient options for restarting
    • verbose::Bool - print residual in each iteration

See also createLinearSolver, solve!.

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SplitBregman

RegularizedLeastSquares.SplitBregman — Type
SplitBregman(A; AHA = A'*A, precon = Identity(), reg = L1Regularization(zero(real(eltype(AHA)))), regTrafo = opEye(eltype(AHA), size(AHA,1)), normalizeReg = NoNormalization(), rho = 1e-1, iterations = 10, iterationsInner = 10, iterationsCG = 10, absTol = eps(real(eltype(AHA))), relTol = eps(real(eltype(AHA))), tolInner = 1e-5, verbose = false)
+SplitBregman( ; AHA = ,     precon = Identity(), reg = L1Regularization(zero(real(eltype(AHA)))), regTrafo = opEye(eltype(AHA), size(AHA,1)), normalizeReg = NoNormalization(), rho = 1e-1, iterations = 10, iterationsInner = 10, iterationsCG = 10, absTol = eps(real(eltype(AHA))), relTol = eps(real(eltype(AHA))), tolInner = 1e-5, verbose = false)

Creates a SplitBregman object for the forward operator A or normal operator AHA.

Required Arguments

  • A - forward operator

OR

  • AHA - normal operator (as a keyword argument)

Optional Keyword Arguments

  • AHA - normal operator is optional if A is supplied
  • precon - preconditionner for the internal CG algorithm
  • reg::AbstractParameterizedRegularization - regularization term; can also be a vector of regularization terms
  • regTrafo - transformation to a space in which reg is applied; if reg is a vector, regTrafo has to be a vector of the same length. Use opEye(eltype(AHA), size(AHA,1)) if no transformation is desired.
  • normalizeReg::AbstractRegularizationNormalization - regularization normalization scheme; options are NoNormalization(), MeasurementBasedNormalization(), SystemMatrixBasedNormalization()
  • rho::Real - weights for condition on regularized variables; can also be a vector for multiple regularization terms
  • iterations::Int - maximum number of outer iterations. Set to 1 for unconstraint split Bregman (equivalent to ADMM)
  • iterationsInner::Int - maximum number of inner iterations
  • iterationsCG::Int - maximum number of (inner) CG iterations
  • absTol::Real - absolute tolerance for stopping criterion
  • relTol::Real - relative tolerance for stopping criterion
  • tolInner::Real - relative tolerance for CG stopping criterion
  • verbose::Bool - print residual in each iteration

This algorithm solves the constraint problem (Eq. (4.7) in Tom Goldstein and Stanley Osher), i.e. ||R(x)||₁ such that ||Ax -b||₂² < σ². In order to solve the unconstraint problem (Eq. (4.8) in Tom Goldstein and Stanley Osher), i.e. ||Ax -b||₂² + λ ||R(x)||₁, you can either set iterations=1 or use ADMM instead, which is equivalent (iterations=1 in SplitBregman in implied in ADMM and the SplitBregman variable iterationsInner is simply called iterations in ADMM)

Like ADMM, SplitBregman differs from ISTA-type algorithms in the sense that the proximal operation is applied separately from the transformation to the space in which the penalty is applied. This is reflected by the interface which has reg and regTrafo as separate arguments. E.g., for a TV penalty, you should NOT set reg=TVRegularization, but instead use reg=L1Regularization(λ), regTrafo=RegularizedLeastSquares.GradientOp(Float64; shape=(Nx,Ny,Nz)).

See also createLinearSolver, solve!.

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Miscellaneous

RegularizedLeastSquares.solverstate — Function
solverstate(solver::AbstractLinearSolver)

Return the current state of the solver

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RegularizedLeastSquares.solversolution — Function
solversolution(solver::AbstractLinearSolver)

Return the current solution of the solver

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solversolution(state::AbstractSolverState)

Return the current solution of the solver's state

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RegularizedLeastSquares.solverconvergence — Function
solverconvergence(solver::AbstractLinearSolver)

Return a named tuple of the solvers current convergence metrics

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RegularizedLeastSquares.StoreSolutionCallback — Type
StoreSolutionCallback(T)

Callback that accumlates the solvers solution per iteration. Results are stored in the solutions field.

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RegularizedLeastSquares.StoreConvergenceCallback — Type
StoreConvergenceCallback()

Callback that accumlates the solvers convergence metrics per iteration. Results are stored in the convMeas field.

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RegularizedLeastSquares.CompareSolutionCallback — Type
CompareSolutionCallback(ref, cmp)

Callback that compares the solvers current solution with the given reference via cmp(ref, solution) per iteration. Results are stored in the results field.

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RegularizedLeastSquares.linearSolverList — Function

Return a list of all available linear solvers

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RegularizedLeastSquares.createLinearSolver — Function
createLinearSolver(solver::AbstractLinearSolver, A; kargs...)

This method creates a solver. The supported solvers are methods typically used for solving regularized linear systems. All solvers return an approximate solution to Ax = b.

TODO: give a hint what solvers are available

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RegularizedLeastSquares.applicableSolverList — Function
applicable(args...)

list all solvers that are applicable to the given arguments. Arguments are the same as for isapplicable without the solver type.

See also isapplicable, linearSolverList.

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RegularizedLeastSquares.isapplicable — Function
isapplicable(solverType::Type{<:AbstractLinearSolver}, A, x, reg)

return true if a solver of type solverType is applicable to system matrix A, data x and regularization terms reg.

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z^1To#d}TIFobl}c0K>RwKwgN56qFzMj{}M*bNq9lLX3n54k56>#r_NP00&HLz~q60 zXi%>8|KsF*Odlmo(MUP+d*GtD-+_K#(f_$j#Ixjo3UXZkUF`w9=>P3P|DQ(j|NqI) ZLdj>CDmezizd*pBjD(_im8fyh{{epWV3q&? diff --git a/dev/generated/examples/compressed_sensing/index.html b/dev/generated/examples/compressed_sensing/index.html index c48bec3..3181501 100644 --- a/dev/generated/examples/compressed_sensing/index.html +++ b/dev/generated/examples/compressed_sensing/index.html @@ -5,26 +5,26 @@ size(image)
(256, 256)

This produces a 256x256 image of a Shepp-Logan phantom.

In this example, we consider a problem in which we randomly sample a third of the pixels in the image. Such a problem and the corresponding measurement can be constructed with the packages LinearOperatorCollection and Random:

We first randomly shuffle the indices of the image and then select the first third of the indices to sample.

using Random, LinearOperatorCollection
 randomIndices = shuffle(eachindex(image))
 sampledIndices = sort(randomIndices[1:div(end, 3)])
21845-element Vector{Int64}:
+     2
+     3
      4
+     5
+     6
      7
-     9
-    11
-    15
-    18
-    20
-    25
-    26
+    13
+    19
+    22
     27
      ⋮
- 65492
- 65500
+ 65510
+ 65513
+ 65514
  65516
- 65522
+ 65519
  65523
  65524
- 65528
  65530
- 65535

Afterwards we build a sampling operator which samples the image at the selected indices.

A = SamplingOp(eltype(image), pattern = sampledIndices , shape = size(image));

Then we apply the sampling operator to the vectorized image to obtain the sampled measurement vector

b = A*vec(image);

To visualize our image we can use CairoMakie:

using CairoMakie
+ 65531

Afterwards we build a sampling operator which samples the image at the selected indices.

A = SamplingOp(eltype(image), pattern = sampledIndices , shape = size(image));

Then we apply the sampling operator to the vectorized image to obtain the sampled measurement vector

b = A*vec(image);

To visualize our image we can use CairoMakie:

using CairoMakie
 function plot_image(figPos, img; title = "", width = 150, height = 150)
   ax = CairoMakie.Axis(figPos; yreversed=true, title, width, height)
   hidedecorations!(ax)
@@ -36,30 +36,30 @@
 samplingMask[sampledIndices] .= true
 plot_image(fig[1,2], image .* samplingMask, title = "Sampled Image")
 resize_to_layout!(fig)
-fig
Example block output

As we can see in the right image, only a third of the pixels are sampled. The goal of the inverse problem is to recover the original image from this measurement vector.

Solving the Inverse Problem

To recover the image from the measurement vector, we solve the TV-regularized least squares problem:

\[\begin{equation} +figExample block output

As we can see in the right image, only a third of the pixels are sampled. The goal of the inverse problem is to recover the original image from this measurement vector.

Solving the Inverse Problem

To recover the image from the measurement vector, we solve the TV-regularized least squares problem:

\[\begin{equation} \underset{\mathbf{x}}{argmin} \frac{1}{2}\vert\vert \mathbf{A}\mathbf{x}-\mathbf{b} \vert\vert_2^2 + \lambda\vert\vert\mathbf{x}\vert\vert_{\text{TV}} . \end{equation}\]

For this purpose we build a TV regularizer with regularization parameter $λ=0.01$

using RegularizedLeastSquares
 reg = TVRegularization(0.01; shape=size(image));

We will use the Fast Iterative Shrinkage-Thresholding Algorithm (FISTA) to solve our inverse problem. Thus, we build the corresponding solver

solver = createLinearSolver(FISTA, A; reg=reg, iterations=20);

and apply it to our measurement vector

img_approx = solve!(solver,b)
65536-element Vector{Float32}:
- 4.475941f-19
- 9.203197f-19
- 2.1954707f-18
- 5.0659254f-18
- 1.1152266f-17
- 2.390461f-17
- 5.0998504f-17
- 1.070807f-16
- 2.1490663f-16
- 4.1222092f-16
+ 7.5375277f-19
+ 1.4254111f-18
+ 3.4523988f-18
+ 8.367589f-18
+ 1.9255124f-17
+ 4.2910904f-17
+ 9.2002786f-17
+ 1.8753667f-16
+ 3.830775f-16
+ 8.49308f-16
  ⋮
- 2.364637f-16
- 1.1878623f-16
- 5.4425447f-17
- 2.3837499f-17
- 1.1017589f-17
- 5.440978f-18
- 2.628318f-18
- 1.2022713f-18
- 6.2830215f-19

To visualize the reconstructed image, we need to reshape the result vector to the correct shape. Afterwards we can use CairoMakie again:

img_approx = reshape(img_approx,size(image));
+ 2.435243f-16
+ 1.2161483f-16
+ 5.90744f-17
+ 2.7788742f-17
+ 1.2509881f-17
+ 5.4832657f-18
+ 2.4100902f-18
+ 1.1232092f-18
+ 6.593036f-19

To visualize the reconstructed image, we need to reshape the result vector to the correct shape. Afterwards we can use CairoMakie again:

img_approx = reshape(img_approx,size(image));
 plot_image(fig[1,3], img_approx, title = "Reconstructed Image")
 resize_to_layout!(fig)
-fig
Example block output

This page was generated using Literate.jl.

+figExample block output

This page was generated using Literate.jl.

diff --git a/dev/generated/examples/computed_tomography/index.html b/dev/generated/examples/computed_tomography/index.html index 84703ed..e8ba3a2 100644 --- a/dev/generated/examples/computed_tomography/index.html +++ b/dev/generated/examples/computed_tomography/index.html @@ -62,4 +62,4 @@ 0.0

To visualize the reconstructed image, we need to reshape the result vector to the correct shape. Afterwards we can use CairoMakie again:

img_approx = reshape(img_approx,size(image));
 plot_image(fig[1,4], img_approx, title = "Reconstructed Image")
 resize_to_layout!(fig)
-fig
Example block output

This page was generated using Literate.jl.

+figExample block output

This page was generated using Literate.jl.

diff --git a/dev/generated/examples/getting_started/index.html b/dev/generated/examples/getting_started/index.html index 57ad664..180eaba 100644 --- a/dev/generated/examples/getting_started/index.html +++ b/dev/generated/examples/getting_started/index.html @@ -9,4 +9,4 @@ \underset{\mathbf{x}}{argmin} \frac{1}{2}\vert\vert \mathbf{A}\mathbf{x}-\mathbf{b} \vert\vert_2^2 + \lambda\vert\vert\mathbf{x}\vert\vert^2_2 . \end{equation}\]

The corresponding solver can be built with the $l^2_2$-regularization term:

solver = createLinearSolver(CGNR, A; reg = L2Regularization(0.0001), iterations=32);
 x_approx = solve!(solver, b)
-isapprox(x, x_approx, rtol = 0.001)
true

This page was generated using Literate.jl.

+isapprox(x, x_approx, rtol = 0.001)
true

This page was generated using Literate.jl.

diff --git a/dev/generated/explanations/regularization/index.html b/dev/generated/explanations/regularization/index.html index 51b8246..9d78088 100644 --- a/dev/generated/explanations/regularization/index.html +++ b/dev/generated/explanations/regularization/index.html @@ -55,4 +55,4 @@ plot_image(fig[1,4], img_prox_wavelet, title = "Reg. Wavelet Domain") resize_to_layout!(fig) figExample block output

Generally, regularization terms can be nested arbitrarly deep, adding new functionality with each layer. Each nested regularization term can return its inner regularization term. Furthermore, all regularization terms implement the iteration interface to iterate over the nesting. The innermost regularization term of a nested term must be a core regularization term and it can be returned by the sink function:

RegularizedLeastSquares.innerreg(reg) == core
true
sink(reg) == core
true
foreach(r -> println(nameof(typeof(r))), reg)
TransformedRegularization
-L1Regularization

This page was generated using Literate.jl.

+L1Regularization

This page was generated using Literate.jl.

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