ProxTV.jl

This package wraps https://github.com/albarji/proxTV in Julia.

https://github.com/JuliaSmoothOptimizers/ProxTV.jl

Science Score: 54.0%

This score indicates how likely this project is to be science-related based on various indicators:

✓
CITATION.cff file
Found CITATION.cff file
✓
codemeta.json file
Found codemeta.json file
✓
.zenodo.json file
Found .zenodo.json file
○
DOI references
✓
Academic publication links
Links to: zenodo.org
○
Academic email domains
○
Institutional organization owner
○
JOSS paper metadata
○
Scientific vocabulary similarity
Low similarity (15.1%) to scientific vocabulary

Last synced: 9 months ago · JSON representation ·

Repository

This package wraps https://github.com/albarji/proxTV in Julia.

Basic Info

Host: GitHub
Owner: JuliaSmoothOptimizers
License: mpl-2.0
Language: Julia
Default Branch: main
Size: 651 KB

Statistics

Stars: 3
Watchers: 1
Forks: 2
Open Issues: 5
Releases: 2

Created almost 2 years ago · Last pushed 10 months ago

Metadata Files

Readme License Citation

ProxTV

ProxTV.jl is a Julia package that provides a collection of exact and inexact proximal operators. This includes the Total Variation (TV) regularization with any p-norm.

This package is a Julia implementation of the ProxTV package for MATLAB and Python which is available here. Behind those implementations, there is a C++ library that provides the core of the proximal operators.

How to Use

The package is designed to be easy to use and to provide a consistent interface for all the implemented proximal operators.

Installation

You can install ProxTV.jl using the Julia package manager. From the Julia REPL, type ] to enter the Pkg REPL mode and run:

julia pkg> add ProxTV

Example

Here is an example of how to use ProxTV.jl to compute the proximal operator of the Total Variation (TV) regularization with a p-norm on a 1D signal.

```julia using ProxTV

n = 1000 x = range(0, 2π; length=n) truesignal = sin.(x) noisysignal = truesignal + 0.1 * randn(n) recoveredsignal = similar(noisy_signal) # output buffer

h = NormTVp(1.0, 1.0, n) prox!(recoveredsignal, h, noisysignal, 1.0)

```

Result

Comprehensive documentation and more examples can be found in the online documentation.

Features

Fast computation of Lp-norm and Total Variation proximal operators
Support for 1D, 2D, and nD signals
Support for any p-norm (L1, L2, and custom p-norms) with p ≥ 1
Weighted regularization
many variants (see src/libproxtv.jl)
Integration with ShiftedProximalOperators.jl for RegularizedOptimization.jl

Tests

ProxTV.jl includes comprehensive tests for:

Core Lp-norm and TV functions
Integration with ShiftedProximalOperators

Build your prox API

If you want to integrate your proximal operator in RegularizedOptimization.jl, see Build your own prox API.

How to Cite

If you use ProxTV.jl in your work, please cite using the reference given in CITATION.cff.

Contributing

If you want to make contributions of any kind, please first that a look into our contributing guide directly on GitHub.

Contributors

Owner

Name: JuliaSmoothOptimizers
Login: JuliaSmoothOptimizers
Kind: organization
Location: DOI: 10.5281/zenodo.2655082

Website: https://juliasmoothoptimizers.github.io
Repositories: 63
Profile: https://github.com/JuliaSmoothOptimizers

Infrastructure and Solvers for Continuous Optimization in Julia

Citation (CITATION.cff)

# Go to https://citation-file-format.github.io/cff-initializer-javascript/#/ to finish this
cff-version: 1.2.0
title: ProxTV.jl
message: >-
  If you use this software, please cite it using the
  metadata from this file.
type: software
authors:
  - given-names: Nathan Allaire
    email: nathan.allaire@polymtl.ca

GitHub Events

Total

Pull request event: 1

Last Year

Pull request event: 1

Issues and Pull Requests

Last synced: 10 months ago

All Time

Total issues: 0
Total pull requests: 60
Average time to close issues: N/A
Average time to close pull requests: 25 days
Total issue authors: 0
Total pull request authors: 4
Average comments per issue: 0
Average comments per pull request: 0.38
Merged pull requests: 52
Bot issues: 0
Bot pull requests: 11

Past Year

Issues: 0
Pull requests: 60
Average time to close issues: N/A
Average time to close pull requests: 25 days
Issue authors: 0
Pull request authors: 4
Average comments per issue: 0
Average comments per pull request: 0.38
Merged pull requests: 52
Bot issues: 0
Bot pull requests: 11

View more stats

Top Authors

Issue Authors

Pull Request Authors

nathanemac (48)
dependabot[bot] (9)
github-actions[bot] (2)
abelsiqueira (1)

Top Labels

Issue Labels

Pull Request Labels

dependencies (9) github_actions (4) chore (2)

Dependencies

.github/workflows/CompatHelper.yml actions

julia-actions/cache v2 composite
julia-actions/setup-julia v2 composite

.github/workflows/Docs.yml actions

actions/checkout v4 composite
julia-actions/cache v2 composite
julia-actions/setup-julia v2 composite

.github/workflows/Lint.yml actions

actions/cache v4 composite
actions/checkout v4 composite
actions/setup-python v5 composite
julia-actions/cache v2 composite
julia-actions/setup-julia v2 composite
lycheeverse/lychee-action v1 composite

.github/workflows/PreCommitUpdate.yml actions

actions/checkout v4 composite
actions/setup-python v5 composite
peter-evans/create-pull-request v6 composite

.github/workflows/ReusableTest.yml actions

actions/checkout v4 composite
codecov/codecov-action v4 composite
julia-actions/cache v2 composite
julia-actions/julia-buildpkg v1 composite
julia-actions/julia-processcoverage v1 composite
julia-actions/julia-runtest v1 composite
julia-actions/setup-julia v2 composite

.github/workflows/TagBot.yml actions

JuliaRegistries/TagBot v1 composite

.github/workflows/Test.yml actions

.github/workflows/TestOnPRs.yml actions

.github/workflows/ci.yml actions

actions/checkout v2 composite
julia-actions/setup-julia v1 composite

ecosyste.ms

Data

Tools

Indexes

Applications

Experiments

Open Source Science