Comments (3)
If we decide to completey avoid pretty printing, it works fine for some sfuff:
julia> NormL1()
NormL1{Float64}(1.0)
but it's quite horrible in other cases:
julia> IndAffine(randn(3,3),randn(3))
ProximalOperators.IndAffineDirect{Float64,Float64,Array{Float64,2},Array{Float64,1},LinearAlgebra.QRCompactWY{Float64,Array{Float64,2}}}([0.7540902122955551 -0.5127888696402579 -0.41036024039021046; 0.3041711542264574 -0.8821531613533968 0.3595632195466501; -0.6471214161239671 0.7621617642057313 0.018528841747891156], [-0.38375757488811657, -1.3589036717591345, 0.7437271582522681], LinearAlgebra.QRCompactWY{Float64,Array{Float64,2}}([-1.0 -0.5341803635161604 0.8864194955698212; -0.2923389378982839 0.8453705336914357 -0.46016377902426897; -0.23394477519669718 -0.3748487888220175 -0.05009764812690726], [1.754090212295555 0.6294821130997797 0.0; 5.0e-324 1.753598976639561 0.0; 2.297204718e-314 2.2972047183e-314 0.0]), [2.1888445724e-314, 2.1885385205e-314, 2.1885430936e-314])
from proximaloperators.jl.
from proximaloperators.jl.
Definitively agree we should simplify this:
julia> using ProximalOperators
julia> NormL1()
description : weighted L1 norm
domain : AbstractArray{Real}, AbstractArray{Complex}
expression : x ↦ λ||x||_1
parameters : λ = 1.0
We could have only the expression, something like:
julia> NormL1()
x ↦ 1.0 ||x||_1
or maybe even shorter:
julia> NormL1()
1.0 ||x||_1
from proximaloperators.jl.
Related Issues (20)
- Add prox! method for IndPSD handling matrices
- possible test failure in upcoming Julia version 1.5 HOT 1
- Projection on Stiefel manifold HOT 1
- Polishing docstrings
- Computing proximal operator of a constrained convex function HOT 5
- Nonnegative least squares example HOT 3
- TagBot trigger issue HOT 18
- TV norm prox HOT 6
- Replace exponential cone projection? HOT 3
- Lightweight “base” package with core definitions
- Sums of norms HOT 2
- SqrNormL2 expects λ to be positive HOT 3
- Is ProximableFunction no longer supported? HOT 2
- calculus function fields HOT 4
- nonconvex prox calculus rule HOT 1
- Multi-threaded operators evaluation
- GPU support via CuArrays.jl HOT 12
- Prox of Sum HOT 2
- Locally Low-Rank operator HOT 2
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from proximaloperators.jl.