Merge branch 'nestedRegTerms' of github.com:tknopp/RegularizedLeastSq…

…uares.jl into nestedRegTerms

.github/workflows/ci.yml

@@ -15,9 +15,7 @@ jobs:
       fail-fast: false
       matrix:
         version:
-          - '1.6' # Replace this with the minimum Julia version that your package supports. E.g. if your package requires Julia 1.5 or higher, change this to '1.5'.
           - '1' # Leave this line unchanged. '1' will automatically expand to the latest stable 1.x release of Julia.
-          - 'nightly'
         os:
           - ubuntu-latest
         arch:

LICENSE.md

@@ -1,9 +1,21 @@
-The RegularizedLeastSquares.jl package is licensed under the MIT "Expat" License:
+MIT License
 
-Copyright (c) 2017: Tobias Knopp.
+Copyright (c) 2023 Tobias Knopp <tobias.knopp@tuhh.de> and contributors
 
-Permission is hereby granted, free of charge, to any person obtaining a copy of this software and associated documentation files (the "Software"), to deal in the Software without restriction, including without limitation the rights to use, copy, modify, merge, publish, distribute, sublicense, and/or sell copies of the Software, and to permit persons to whom the Software is furnished to do so, subject to the following conditions:
+Permission is hereby granted, free of charge, to any person obtaining a copy
+of this software and associated documentation files (the "Software"), to deal
+in the Software without restriction, including without limitation the rights
+to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
+copies of the Software, and to permit persons to whom the Software is
+furnished to do so, subject to the following conditions:
 
-The above copyright notice and this permission notice shall be included in all copies or substantial portions of the Software.
+The above copyright notice and this permission notice shall be included in all
+copies or substantial portions of the Software.
 
-THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE SOFTWARE.
+THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
+IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
+FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
+AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
+LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
+OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
+SOFTWARE.

Project.toml

@@ -12,14 +12,18 @@ Random = "9a3f8284-a2c9-5f02-9a11-845980a1fd5c"
 SparseArrays = "2f01184e-e22b-5df5-ae63-d93ebab69eaf"
 StatsBase = "2913bbd2-ae8a-5f71-8c99-4fb6c76f3a91"
 VectorizationBase = "3d5dd08c-fd9d-11e8-17fa-ed2836048c2f"
-LinearOperators = "5c8ed15e-5a4c-59e4-a42b-c7e8811fb125"
 FastClosures = "9aa1b823-49e4-5ca5-8b0f-3971ec8bab6a"
+LinearOperators = "5c8ed15e-5a4c-59e4-a42b-c7e8811fb125"
+LinearOperatorCollection = "a4a2c56f-fead-462a-a3ab-85921a5f2575"
+InteractiveUtils = "b77e0a4c-d291-57a0-90e8-8db25a27a240"
+
 
 [compat]
 FLoops = "0.2"
 IterativeSolvers = "0.9"
 ProgressMeter = "1.2"
-LinearOperators = "2.5"
+LinearOperators = "2.3.3"
+LinearOperatorCollection = "1.0"
 StatsBase = "0.33"
 VectorizationBase = "0.19, 0.21"
 julia = "1.6"

README.md

@@ -1,15 +1,15 @@
 # RegularizedLeastSquares
 
-[![Build status](https://github.com/tknopp/RegularizedLeastSquares.jl/workflows/CI/badge.svg)](https://github.com/tknopp/RegularizedLeastSquares.jl/actions)
+[![Build status](https://github.com/JuliaImageRecon/RegularizedLeastSquares.jl/workflows/CI/badge.svg)](https://github.com/JuliaImageRecon/RegularizedLeastSquares.jl/actions)
 
-[![codecov.io](http://codecov.io/github/tknopp/RegularizedLeastSquares.jl/coverage.svg?branch=master)](http://codecov.io/github/tknopp/RegularizedLeastSquares.jl?branch=master)
+[![codecov.io](http://codecov.io/github/JuliaImageRecon/RegularizedLeastSquares.jl/coverage.svg?branch=master)](http://codecov.io/github/JuliaImageRecon/RegularizedLeastSquares.jl?branch=master)
 
 
 # Documentation
 
 Read the documentation here:
 
-[![](https://img.shields.io/badge/docs-latest-blue.svg)](https://tknopp.github.io/RegularizedLeastSquares.jl/latest)
+[![](https://img.shields.io/badge/docs-latest-blue.svg)](https://JuliaImageRecon.github.io/RegularizedLeastSquares.jl/latest)
 
 # Community Standards
 

docs/src/regularization.md

@@ -29,7 +29,7 @@ So far, the following common regularizers are implemented:
  * Positivity constrained ("Positive")
  * Projection onto a convex set ("Proj")
 
-To build any of the implemted regularizers, one can use the methods `Regularization(name::String, λ::AbstractFloat; kargs...)` with the corresponding name (in brackets in the list above). For example, an $l_1$-regularizer can be build with
+To build any of the implemented regularizers, one can use the methods `Regularization(name::String, λ::AbstractFloat; kargs...)` with the corresponding name (in brackets in the list above). For example, an $l_1$-regularizer can be build with
 ```julia
 shape = (256,256) # size of the underlying Array
 λ = 1.e-3         # regularization parameter

docs/src/solvers.md

@@ -2,11 +2,10 @@
 
 ## Implemented Solvers
 So far, RegularizedLeastSquares.jl provides implementations for the following solvers:
-* Kaczrmarz algorithm ("kaczmarz")
+* Kaczmarz algorithm ("Kaczmarz")
 * CGNR ("cgnr")
 * Dax algorithm (with Kaczmarz) for unconstrained problems ("daxkaczmarz")
 * Dax algorithm for constrained problems ("daxconstrained")
-* Solver for the Fused Lasso problem ("fusedlasso")
 * Fast Iterative Shrinkage Thresholding Algorithm ("fista")
 * Alternating Direction of Multipliers Method ("admm")
 * Split Bregman method for constrained inverse Problems ("splitBregman")
@@ -19,7 +18,7 @@ To create a solver, one can invoke the method `createLinearSolver` as in
 solver = createLinearSolver("admm",A; reg=reg, ρ=0.1, iterations=20)
 ```
 Here `A` denotes the system matrix and reg is either a `Regularization` or a`Vector{Regularization}`. All further solver parameters can be passed as keyword arguments. To make things more compact, it can be usefull to collect all parameters
-in a `Dict{Symnbol,Any}`. In this way, the code snippet above can be written as
+in a `Dict{Symbol,Any}`. In this way, the code snippet above can be written as
 ```julia
 params=Dict{Symbol,Any}()
 params[:reg] = reg

src/ADMM.jl

@@ -59,8 +59,8 @@ creates an `ADMM` object for the system matrix `A`.
 * (`relTol::Real=eps()`)     - rel tolerance for stopping criterion
 * (`tolInner::Real=1.e-5`)   - rel tolerance for CG stopping criterion
 """
-function ADMM(A::matT, x::Vector{T}=zeros(eltype(A),size(A,2)); reg=L1Regularization(λ[1])
-            , λ=[0.0]
+function ADMM(A::matT, x::Vector{T}=zeros(eltype(A),size(A,2)); λ=[0.0]
+            , reg=L1Regularization(λ[1])
             , regTrafo=nothing
             , AᴴA::opT=nothing
             , precon=Identity()

src/DAXConstrained.jl

@@ -196,7 +196,7 @@ function lentcensormin!(x::Vector{T},y::Vector{T}) where {T<:Real}
   end
 end
 
-"""This funtion saves the denominators to compute αl in denom and the rowindices,
+"""This function saves the denominators to compute αl in denom and the rowindices,
   which lead to an update of cl in rowindex."""
 function initkaczmarzconstraineddax(S::AbstractMatrix,ɛ::Number,weights::Vector)
   length(weights)==size(S,1) ? nothing : error("number of weights must equal number of equations")
@@ -214,7 +214,7 @@ function initkaczmarzconstraineddax(S::AbstractMatrix,ɛ::Number,weights::Vector
 end
 
 """
-This funtion saves the denominators to compute αl in denom and the rowindices,
+This function saves the denominators to compute αl in denom and the rowindices,
 which lead to an update of cl in rowindex.
 """
 function initkaczmarzconstraineddaxfft(S::AbstractMatrix,ɛ::Number,weights::Vector)

src/DAXKaczmarz.jl

@@ -20,8 +20,8 @@ mutable struct DaxKaczmarz{matT,T,U} <: AbstractLinearSolver
   iterationsInner::Int64
 end
 
-"""This function solves a unconstrained linear least squaares problem using an algorithm proposed in [1] combined with a randomized version of kaczmarz [2].
-Returns an approximate solution to the linear leaast squares problem Sᵀx = u.
+"""This function solves a unconstrained linear least squares problem using an algorithm proposed in [1] combined with a randomized version of kaczmarz [2].
+Returns an approximate solution to the linear least squares problem Sᵀx = u.
 
 [1] Dax, A. On Row Relaxation Methods for Large Constrained Least Squares Problems. SIAM J. Sci. Comput. 14, 570–584 (1993).
 [2] Strohmer, T. & Vershynin, R. A Randomized Kaczmarz Algorithm with Exponential Convergence. J. Fourier Anal. Appl. 15, 262–278 (2008).
@@ -71,7 +71,7 @@ function DaxKaczmarz(S, b=nothing; λ::Real=0.0
     push!(reg, RealRegularization())
   end
   if !isempty(reg) && !isnothing(sparseTrafo)
-    reg = map(r -> SparseRegularization(r, sparseTrafo), reg)
+    reg = map(r -> TransformedRegularization(r, sparseTrafo), reg)
   end
   return DaxKaczmarz(S,u,reg, Float64(λ), denom,rowindex,sumrowweights,zk,bk,xl,yl,εw,τl,αl
                   ,T.(weights) ,iterations,iterationsInner)
@@ -162,7 +162,7 @@ end
 
 @inline done(solver::DaxKaczmarz,iteration::Int) = iteration>=solver.iterations
 
-"""This funtion saves the denominators to compute αl in denom and the rowindices,
+"""This function saves the denominators to compute αl in denom and the rowindices,
   which lead to an update of cl in rowindex.
 """
 function initkaczmarzdax(S::AbstractMatrix, ɛ, weights::Vector)

src/Direct.jl

@@ -3,7 +3,7 @@ export pseudoinverse, directSolver, PseudoInverse, DirectSolver
 
 ### Direct Solver ###
 
-mutable struct DirectSolver <: AbstractLinearSolver
+mutable struct DirectSolver #<: AbstractLinearSolver
   A
   params
 end
@@ -46,7 +46,7 @@ end
 
 ###  Pseudoinverse ###
 
-mutable struct PseudoInverse <: AbstractLinearSolver
+mutable struct PseudoInverse #<: AbstractLinearSolver
   A
   params
 end