Optim

Optim: A mathematical optimization package for Julia - Published in JOSS (2018)

https://github.com/julianlsolvers/optim.jl

Science Score: 100.0%

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

✓
CITATION.cff file
Found CITATION.cff file
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codemeta.json file
Found codemeta.json file
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.zenodo.json file
Found .zenodo.json file
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DOI references
Found 5 DOI reference(s) in README and JOSS metadata
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Academic publication links
Links to: joss.theoj.org
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Committers with academic emails
10 of 122 committers (8.2%) from academic institutions
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Institutional organization owner
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JOSS paper metadata
Published in Journal of Open Source Software

Keywords

julia optim optimisation optimization unconstrained-optimisation unconstrained-optimization

Keywords from Contributors

fluxes the-human-brain graphics julialang literate-programming scientific-reports pde simulations ode geometry

Last synced: 4 months ago · JSON representation ·

Repository

Optimization functions for Julia

Basic Info

Host: GitHub
Owner: JuliaNLSolvers
License: other
Language: Julia
Default Branch: master
Homepage: https://julianlsolvers.github.io/Optim.jl/stable/
Size: 7.77 MB

Statistics

Stars: 1,171
Watchers: 30
Forks: 234
Open Issues: 105
Releases: 85

Topics

julia optim optimisation optimization unconstrained-optimisation unconstrained-optimization

Created over 13 years ago · Last pushed 4 months ago

Metadata Files

Readme Changelog Contributing License Citation

Optim.jl

Univariate and multivariate optimization in Julia.

Optim.jl is part of the JuliaNLSolvers family.

Help and support

For help and support, please post on the Optimization (Mathematical) section of the Julia discourse or the #math-optimization channel of the Julia slack.

Installation

Install Optim.jl using the Julia package manager: julia import Pkg Pkg.add("Optim")

Documentation

The online documentation is available at https://julianlsolvers.github.io/Optim.jl/stable.

Example

To minimize the Rosenbrock function, do: ```julia julia> using Optim

julia> rosenbrock(x) = (1.0 - x[1])^2 + 100.0 * (x[2] - x[1]^2)^2 rosenbrock (generic function with 1 method)

julia> result = optimize(rosenbrock, zeros(2), BFGS()) * Status: success

Candidate solution Final objective value: 5.471433e-17
Found with Algorithm: BFGS
Convergence measures |x - x'| = 3.47e-07 ≰ 0.0e+00 |x - x'|/|x'| = 3.47e-07 ≰ 0.0e+00 |f(x) - f(x')| = 6.59e-14 ≰ 0.0e+00 |f(x) - f(x')|/|f(x')| = 1.20e+03 ≰ 0.0e+00 |g(x)| = 2.33e-09 ≤ 1.0e-08
Work counters Seconds run: 0 (vs limit Inf) Iterations: 16 f(x) calls: 53 ∇f(x) calls: 53

julia> Optim.minimizer(result) 2-element Vector{Float64}: 0.9999999926033423 0.9999999852005355

julia> Optim.minimum(result) 5.471432670590216e-17 ```

To get information on the keywords used to construct method instances, use the Julia REPL help prompt (?) ```julia help?> LBFGS search: LBFGS

LBFGS ≡≡≡≡≡

Constructor ===========

LBFGS(; m::Integer = 10, alphaguess = LineSearches.InitialStatic(), linesearch = LineSearches.HagerZhang(), P=nothing, precondprep = (P, x) -> nothing, manifold = Flat(), scaleinvH0::Bool = P === nothing)

LBFGS has two special keywords; the memory length m, and the scaleinvH0 flag. The memory length determines how many previous Hessian approximations to store. When scaleinvH0 == true, then the initial guess in the two-loop recursion to approximate the inverse Hessian is the scaled identity, as can be found in Nocedal and Wright (2nd edition) (sec. 7.2).

In addition, LBFGS supports preconditioning via the P and precondprep keywords.

Description ===========

The LBFGS method implements the limited-memory BFGS algorithm as described in Nocedal and Wright (sec. 7.2, 2006) and original paper by Liu & Nocedal (1989). It is a quasi-Newton method that updates an approximation to the Hessian using past approximations as well as the gradient.

References ==========

•  Wright, S. J. and J. Nocedal (2006), Numerical optimization, 2nd edition.
   Springer

•  Liu, D. C. and Nocedal, J. (1989). "On the Limited Memory Method for
   Large Scale Optimization". Mathematical Programming B. 45 (3): 503–528

```

Use with JuMP

You can use Optim.jl with JuMP.jl as follows:

```julia julia> using JuMP, Optim

julia> model = Model(Optim.Optimizer);

julia> setoptimizerattribute(model, "method", BFGS())

julia> @variable(model, x[1:2]);

julia> @objective(model, Min, (1.0 - x[1])^2 + 100.0 * (x[2] - x[1]^2)^2) (x[1]² - 2 x[1] + 1) + (100.0 * ((-x[1]² + x[2]) ^ 2.0))

julia> optimize!(model)

julia> objective_value(model) 3.7218241804173566e-21

julia> value.(x) 2-element Vector{Float64}: 0.9999999999373603 0.99999999986862 ```

Citation

If you use Optim.jl in your work, please cite the following:

tex @article{mogensen2018optim, author = {Mogensen, Patrick Kofod and Riseth, Asbj{\o}rn Nilsen}, title = {Optim: A mathematical optimization package for {Julia}}, journal = {Journal of Open Source Software}, year = {2018}, volume = {3}, number = {24}, pages = {615}, doi = {10.21105/joss.00615} }

Owner

Name: JuliaNLSolvers
Login: JuliaNLSolvers
Kind: organization

Repositories: 16
Profile: https://github.com/JuliaNLSolvers

JOSS Publication

Optim: A mathematical optimization package for Julia

Published

April 05, 2018

DOI

10.21105/joss.00615

Volume 3, Issue 24, Page 615

Authors

Patrick K. Mogensen

University of Copenhagen

Asbjørn N. Riseth

University of Oxford

Editor

Lorena A Barba

Citation (CITATION.bib)

@article{Optim.jl-2018,
  author  = {Mogensen, Patrick Kofod and Riseth, Asbj{\o}rn Nilsen},
  title   = {Optim: A mathematical optimization package for {Julia}},
  journal = {Journal of Open Source Software},
  year    = {2018},
  volume  = {3},
  number  = {24},
  pages   = {615},
  doi     = {10.21105/joss.00615}
}

GitHub Events

Total

Create event: 36
Commit comment event: 11
Release event: 7
Issues event: 46
Watch event: 57
Delete event: 15
Issue comment event: 233
Push event: 122
Pull request review event: 23
Pull request review comment event: 26
Pull request event: 97
Fork event: 16

Last Year

Create event: 36
Commit comment event: 11
Release event: 7
Issues event: 46
Watch event: 57
Delete event: 15
Issue comment event: 233
Push event: 122
Pull request review event: 23
Pull request review comment event: 26
Pull request event: 97
Fork event: 16

Committers

Last synced: 5 months ago

All Time

Total Commits: 889
Total Committers: 122
Avg Commits per committer: 7.287
Development Distribution Score (DDS): 0.557

Past Year

Commits: 47
Committers: 15
Avg Commits per committer: 3.133
Development Distribution Score (DDS): 0.489

Top Committers

Name	Email	Commits
Patrick Kofod Mogensen	p**n@g**m	394
John Myles White	j**w@j**m	120
Asbjørn Nilsen Riseth	a**h@g**m	68
timholy	t**y@g**m	40
Christof Stocker	c**r@h**g	17
Asbjørn Nilsen Riseth	r**h@m**k	13
Miles Lubin	m**n@g**m	13
github-actions[bot]	4****]	10
Andreas Noack	a**n@g**m	9
Antoine Levitt	a**t@g**m	8
Christoph Ortner	c**0@g**m	7
Benoît Legat	b**t@g**m	6
Blake Johnson	b**4@g**m	6
Christopher Rackauckas	C**t@C**m	5
Kristoffer Carlsson	k**n@c**e	5
Yichao Yu	y**2@g**m	5
dependabot[bot]	4****]	5
Josua Grawitter	j**h@g**g	4
Arno Strouwen	a**n@t**e	4
Dahua Lin	l**a@g**m	4
Ben Kuhn	b**n@g**m	3
David Widmann	d****n	3
Jeffrey Regier	j****r	3
Jorge Fernandez-de-Cossio-Diaz	c****o	3
femtocleaner[bot]	f****]	3
abhro	5****o	3
Thomas R. Covert	t**c@a**u	3
Ron Rock	r**k@u**u	3
Michael Creel	m**l@u**t	3
Alexey Stukalov	a****t	3
and 92 more...

Committer Domains (Top 20 + Academic)

chalmers.se: 2 wikunia.de: 1 dynamic-oit-fishnet-d-25.princeton.edu: 1 rques.fr: 1 uci.edu: 1 coffeenco.de: 1 janelia.hhmi.org: 1 macmillen.net: 1 uab.cat: 1 uchicago.edu: 1 alum.mit.edu: 1 telenet.be: 1 greyage.org: 1 chrisrackauckas.com: 1 maths.ox.ac.uk: 1 hci-kdd.org: 1 alumni.epfl.ch: 1 rocher.lc: 1 ucdavis.edu: 1 purelymail.com: 1 umich.edu: 1

Issues and Pull Requests

Last synced: 4 months ago

All Time

Total issues: 141
Total pull requests: 171
Average time to close issues: 7 months
Average time to close pull requests: 3 months
Total issue authors: 103
Total pull request authors: 45
Average comments per issue: 2.72
Average comments per pull request: 3.4
Merged pull requests: 120
Bot issues: 0
Bot pull requests: 20

Past Year

Issues: 31
Pull requests: 89
Average time to close issues: 3 days
Average time to close pull requests: 13 days
Issue authors: 18
Pull request authors: 17
Average comments per issue: 0.48
Average comments per pull request: 2.63
Merged pull requests: 60
Bot issues: 0
Bot pull requests: 11

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Top Authors

Issue Authors

pkofod (13)
renatobellotti (6)
cseveren (4)
andreasnoack (3)
mahaa2 (3)
Moelf (3)
blegat (2)
sgaure (2)
roflmaostc (2)
Li-shiyue (2)
dhathri-auburn (2)
rsenne (2)
ForceBru (2)
KronosTheLate (2)
kbarros (2)

Pull Request Authors

pkofod (58)
dependabot[bot] (12)
abhro (8)
github-actions[bot] (7)
timholy (6)
blegat (6)
odow (5)
devmotion (5)
MilesCranmer (4)
ArnoStrouwen (4)
gdalle (4)
JoshuaLampert (2)
christianhauschel (2)
gwater (2)
thchr (2)

Top Labels

Issue Labels

bug (2) BREAKING (2) needs input (1) enhancement (1) trace (1)

Pull Request Labels

dependencies (12) github_actions (7) needs docs (2) needs tests (2)

Packages

Total packages: 3
Total downloads:
- julia 10,141 total

Total dependent packages: 292
(may contain duplicates)
Total dependent repositories: 150
(may contain duplicates)
Total versions: 249

juliahub.com: Optim

Optimization functions for Julia

Homepage: https://julianlsolvers.github.io/Optim.jl/stable/
Documentation: https://docs.juliahub.com/General/Optim/stable/
License: MIT
Latest release: 1.13.2
published 7 months ago

Versions: 59
Dependent Packages: 292
Dependent Repositories: 150
Downloads: 10,141 Total

Rankings

Forks count: 0.2%

Dependent packages count: 0.3%

Dependent repos count: 0.3%

Average: 0.3%

Stargazers count: 0.4%

Last synced: 4 months ago

proxy.golang.org: github.com/julianlsolvers/optim.jl

Documentation: https://pkg.go.dev/github.com/julianlsolvers/optim.jl#section-documentation
License: other
Latest release: v1.13.2
published 7 months ago

Versions: 95
Dependent Packages: 0
Dependent Repositories: 0

Rankings

Dependent packages count: 5.6%

Average: 5.8%

Dependent repos count: 5.9%

Last synced: 4 months ago

proxy.golang.org: github.com/JuliaNLSolvers/Optim.jl

Documentation: https://pkg.go.dev/github.com/JuliaNLSolvers/Optim.jl#section-documentation
License: other
Latest release: v1.13.2
published 7 months ago

Versions: 95
Dependent Packages: 0
Dependent Repositories: 0

Rankings

Dependent packages count: 5.6%

Average: 5.8%

Dependent repos count: 5.9%

Last synced: 4 months ago

Dependencies

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julia-actions/setup-julia v1 composite

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Optim

Science Score: 100.0%

Keywords

Keywords from Contributors

Repository

Basic Info

Statistics

Topics

Metadata Files

README.md

Optim.jl

Help and support

Installation

Documentation

Example

Use with JuMP

Citation

Owner

JOSS Publication

Optim: A mathematical optimization package for Julia

Authors

Editor

Tags

Citation (CITATION.bib)

GitHub Events

Total

Last Year

Committers

All Time

Past Year

Top Committers

Committer Domains (Top 20 + Academic)

Issues and Pull Requests

All Time

Past Year

Top Authors

Issue Authors

Pull Request Authors

Top Labels

Issue Labels

Pull Request Labels

Packages

juliahub.com: Optim

Rankings

proxy.golang.org: github.com/julianlsolvers/optim.jl

Rankings

proxy.golang.org: github.com/JuliaNLSolvers/Optim.jl

Rankings

Dependencies