Merge branch 'main' into compathelper/new_version/2022-02-23-00-26-03-176-01601683381

Author: SebastianAment

Project.toml

@@ -1,7 +1,7 @@
 name = "CompressedSensing"
 uuid = "adc28fdd-7b77-41a2-89a7-a1a193b336a0"
 authors = ["Sebastian Ament <[email protected]>"]
-version = "1.0.0"
+version = "1.0.1"
 
 [deps]
 Clp = "e2554f3b-3117-50c0-817c-e040a3ddf72d"
@@ -16,9 +16,9 @@ UpdatableQRFactorizations = "8b110c62-ada2-4dd7-b53a-29f29fe8f7f4"
 WoodburyFactorizations = "9f1bac23-581c-4ebc-bd36-df60d764636d"
 
 [compat]
-Clp = "0.9"
+Clp = "0.9, 1"
 ECOS = "0.14, 1"
-JuMP = "0.22"
+JuMP = "0.22, 0.23"
 LazyInverses = "1.1"
 StatsBase = "0.33"
 UpdatableQRFactorizations = "1.0"

src/basispursuit.jl

@@ -45,12 +45,12 @@ end
 const bp_candes = candes_reweighting
 
 # using LinearAlgebraExtensions: LowRank
-# TODO: stop when w has converged, instead of maxiter
+# IDEA: stop when w has converged, instead of maxiter
 function ard_weights!(w, A, x, ε::Real, iter::Int = 8)
     if any(==(0), w)
         error("weights cannot be zero")
     end
-    # nzind = x.nzind # this could speed up the weight computation
+    # nzind = x.nzind # this could speed up the weight computation, could also drop zeros from model before reweighting ...
     # Ai = @view A[:, nzind]
     # wx = @. abs(x.nzval) / w[nzind]
     # K = Woodbury(ε*I(size(A, 1)), Ai, WX, Ai')
@@ -66,6 +66,7 @@ end
 
 ard_function(A::AbstractMatrix, ε::Real) = (w, x) -> ard_weights!(w, A, x, ε)
 
+# IDEA: generalize for general objectives via Jacobian (linearization)
 function ard_reweighting(A::AbstractMatrix, b::AbstractVector, ε::Real = 1e-2;
                                                             maxiter::Int = 8)
     basispursuit_reweighting(A, b, ard_function(A, ε), maxiter = maxiter)
@@ -137,7 +138,7 @@ end
 
 ################################ (F)ISTA ########################################
 struct FISTA end
-# TODO: use homotopy to select appropriate λ
+# IDEA: use homotopy to select appropriate λ
 
 # shrinkage and thresholding operator
 shrinkage(x::Real, α::Real) = sign(x) * max(abs(x)-α, zero(x))

src/matchingpursuit.jl

@@ -86,7 +86,7 @@ function omp(A::AbstractMatOrFac, b::AbstractVector, k::Int)
 end
 
 function omp(A::AbstractMatOrFac, b::AbstractVector;
-    max_residual = eps(eltype(A)), sparsity = size(A, 2))
+    max_residual = eps(eltype(A)), sparsity = min(size(A)...))
     omp(A, b, max_residual, sparsity)
 end