# \[ANN\] Clarabel.jl: New convex optimization solver

**URL:** <https://discourse.julialang.org/t/ann-clarabel-jl-new-convex-optimization-solver/83915>\
**Category:** Optimization (Mathematical)\
**Created:** [July 7, 2022, 5:16pm UTC](https://discourse.julialang.org/t/ann-clarabel-jl-new-convex-optimization-solver/83915 "2022-07-07T17:16:51Z")\
**Posts on this page:** 20\
**Page:** 1

<div class="post-metadata">

**Author:** ![pgoulart](https://sea2.discourse-cdn.com/julialang/user_avatar/discourse.julialang.org/pgoulart/32/17514_2.png) [@pgoulart](https://discourse.julialang.org/u/pgoulart)\
**Post date:** [July 7, 2022, 5:16pm UTC](https://discourse.julialang.org/t/ann-clarabel-jl-new-convex-optimization-solver/83915/1 "2022-07-07T17:16:51Z")

</div>

Dear Julia Optimizers,

We are happy to announce the release of Clarabel.jl v0.1.0, a new interior point code for conic optimization written entirely in Julia.

The code currently supports LP/QP/SOCP/SDP problems and can be accessed through JuMP or Convex.jl.

Documentation and examples can be found [here](https://oxfordcontrol.github.io/Clarabel.jl/stable/). It is installable via the Julia package manager, or downloadable directly from our group’s research repository here:

[https://github.com/oxfordcontrol/Clarabel.jl](https://github.com/oxfordcontrol/Clarabel.jl)

Enjoy!

---

<div class="post-metadata">

**Author:** ![ccoffrin](https://sea2.discourse-cdn.com/julialang/user_avatar/discourse.julialang.org/ccoffrin/32/400_2.png) [@ccoffrin](https://discourse.julialang.org/u/ccoffrin)\
**Post date:** [July 7, 2022, 5:35pm UTC](https://discourse.julialang.org/t/ann-clarabel-jl-new-convex-optimization-solver/83915/2 "2022-07-07T17:35:54Z")

</div>

It looks like the solver is pure Julia, not calling a C library? If so, that is super cool and a great contributions to the community!

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<div class="post-metadata">

**Author:** ![pgoulart](https://sea2.discourse-cdn.com/julialang/user_avatar/discourse.julialang.org/pgoulart/32/17514_2.png) [@pgoulart](https://discourse.julialang.org/u/pgoulart)\
**Post date:** [July 7, 2022, 5:54pm UTC](https://discourse.julialang.org/t/ann-clarabel-jl-new-convex-optimization-solver/83915/3 "2022-07-07T17:54:34Z")

</div>

Yes, it is a pure Julia language solver. It even uses our pure Julia LDL factorization package [QDLDL.jl](https://github.com/osqp/QDLDL.jl) for the linear solve step by default, although MKL and Cholmod are also supported as options.

---

<div class="post-metadata">

**Author:** ![Oscar\_Smith](https://sea2.discourse-cdn.com/julialang/user_avatar/discourse.julialang.org/oscar_smith/32/25343_2.png) [@Oscar\_Smith](https://discourse.julialang.org/u/Oscar_Smith)\
**Post date:** [July 8, 2022, 11:16pm UTC](https://discourse.julialang.org/t/ann-clarabel-jl-new-convex-optimization-solver/83915/4 "2022-07-08T23:16:09Z")

</div>

this is really cool! how does qldl stack up vs other solvers wrt performance?

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<div class="post-metadata">

**Author:** ![pgoulart](https://sea2.discourse-cdn.com/julialang/user_avatar/discourse.julialang.org/pgoulart/32/17514_2.png) [@pgoulart](https://discourse.julialang.org/u/pgoulart)\
**Post date:** [July 9, 2022, 6:14am UTC](https://discourse.julialang.org/t/ann-clarabel-jl-new-convex-optimization-solver/83915/5 "2022-07-09T06:14:19Z")

</div>

We find that QDLDL is generally faster than either Pardiso or ldlt/Cholmod, at least for problems of moderate size. At some large scale I think MKL overtakes it, but we haven’t done extensive benchmarking to find the crossover point.

The caveat though is that QDLDL should only be used for a sign definite or quasidefinite system. If you use it on a general sparse symmetric matrix it may work anyway, but it’s not really safe to do so. MKL and Cholmod also probably both do a better job with badly conditioned systems.

---

<div class="post-metadata">

**Author:** ![maxkapur](https://sea2.discourse-cdn.com/julialang/user_avatar/discourse.julialang.org/maxkapur/32/21208_2.png) [@maxkapur](https://discourse.julialang.org/u/maxkapur)\
**Post date:** [July 9, 2022, 7:46am UTC](https://discourse.julialang.org/t/ann-clarabel-jl-new-convex-optimization-solver/83915/6 "2022-07-09T07:46:31Z")

</div>

Wow, I’m noticing a nearly 6x improvement over SCS for the [minimal ellipses](https://jump.dev/JuMP.jl/v1.1.1/tutorials/conic/min_ellipse/) example from the JuMP documentation:

```julia
julia> versioninfo()
Julia Version 1.8.0-rc1
Commit 6368fdc656 (2022-05-27 18:33 UTC)
Platform Info:
  OS: Linux (x86_64-pc-linux-gnu)
  CPU: 4 × Intel(R) Core(TM) i5-3210M CPU @ 2.50GHz
  WORD_SIZE: 64
  LIBM: libopenlibm
  LLVM: libLLVM-13.0.1 (ORCJIT, ivybridge)
  Threads: 1 on 4 virtual cores

julia> minimum(solve_clarabel() for _ in 1:5)
0.008435284000000001

julia> minimum(solve_SCS() for _ in 1:5)
0.047278745000000004

```

(`solve_clarabel()` and `solve_SCS()` just wrap the code included on the linked page and then return `solve_time(model)`. I couldn’t use BenchmarkTools because this would add in the noise time of reading in the model, rather than solving it.)

I will be making lots of use of Clarabel in the future!

---

<div class="post-metadata">

**Author:** ![compleat](https://sea2.discourse-cdn.com/julialang/user_avatar/discourse.julialang.org/compleat/32/8958_2.png) [@compleat](https://discourse.julialang.org/u/compleat)\
**Post date:** [July 12, 2022, 9:52pm UTC](https://discourse.julialang.org/t/ann-clarabel-jl-new-convex-optimization-solver/83915/7 "2022-07-12T21:52:33Z")

</div>

I’m intrigued but having trouble installing (maybe this isn’t the right place)

```julia
(@v1.7) pkg> add Clarabel
    Updating registry at `~/.julia/registries/General.toml`
   Resolving package versions...
ERROR: Unsatisfiable requirements detected for package DataFrames [a93c6f00]:
 DataFrames [a93c6f00] log:
 ├─possible versions are: 0.11.7-1.3.4 or uninstalled
 ├─restricted to versions * by an explicit requirement, leaving only versions 0.11.7-1.3.4
 ├─restricted by compatibility requirements with StatsBase [2913bbd2] to versions: 0.19.1-1.3.4 or uninstalled, leaving only versions: 0.19.1-1.3.4
 │ └─StatsBase [2913bbd2] log:
 │ ├─possible versions are: 0.24.0-0.33.18 or uninstalled
 │ └─restricted to versions 0.33.1-0.33 by NaiveBayes [9bbee03b], leaving only versions 0.33.1-0.33.18
 │ └─NaiveBayes [9bbee03b] log:
 │ ├─possible versions are: 0.5.2 or uninstalled
 │ └─NaiveBayes [9bbee03b] is fixed to version 0.5.2
 ├─restricted by compatibility requirements with Compat [34da2185] to versions: 0.20.0-1.3.4 or uninstalled, leaving only versions: 0.20.0-1.3.4
 │ └─Compat [34da2185] log:
 │ ├─possible versions are: 1.0.0-4.1.0 or uninstalled
 │ ├─restricted to versions * by an explicit requirement, leaving only versions 1.0.0-4.1.0
 │ ├─restricted by compatibility requirements with BlackBoxOptim [a134a8b2] to versions: 1.0.0-3.45.0
 │ │ └─BlackBoxOptim [a134a8b2] log:
 │ │ ├─possible versions are: 0.4.0-0.6.1 or uninstalled
 │ │ ├─restricted to versions * by an explicit requirement, leaving only versions 0.4.0-0.6.1
 │ │ └─restricted by compatibility requirements with Compat [34da2185] to versions: 0.5.0-0.6.1 or uninstalled, leaving only versions: 0.5.0-0.6.1
 │ │ └─Compat [34da2185] log: see above
 │ └─restricted by compatibility requirements with GaussianMixtures [cc18c42c] to versions: 3.6.0-3.45.0
 │ └─GaussianMixtures [cc18c42c] log:
 │ ├─possible versions are: 0.3.0-0.3.6 or uninstalled
 │ ├─restricted to versions * by an explicit requirement, leaving only versions 0.3.0-0.3.6
 │ └─restricted by compatibility requirements with StatsBase [2913bbd2] to versions: 0.3.3-0.3.6 or uninstalled, leaving only versions: 0.3.3-0.3.6
 │ └─StatsBase [2913bbd2] log: see above
 ├─restricted by compatibility requirements with Lathe [38d8eb38] to versions: 0.11.7-0.22.7, leaving only versions: 0.20.0-0.22.7
 │ └─Lathe [38d8eb38] log:
 │ ├─possible versions are: 0.0.3-0.1.8 or uninstalled
 │ └─restricted to versions * by an explicit requirement, leaving only versions 0.0.3-0.1.8
 └─restricted by compatibility requirements with Clarabel [61c947e1] to versions: 1.0.0-1.3.4 — no versions left
   └─Clarabel [61c947e1] log:
     ├─possible versions are: 0.1.0-0.1.1 or uninstalled
     └─restricted to versions * by an explicit requirement, leaving only versions 0.1.0-0.1.1

julia> using Clarabel
 │ Package Clarabel not found, but a package named Clarabel is available from a registry. 
 │ Install package?
 │ (@v1.7) pkg> add Clarabel 
 └ (y/n) [y]: 
   Resolving package versions...
ERROR: Unsatisfiable requirements detected for package DataFrames [a93c6f00]:
 DataFrames [a93c6f00] log:
 ├─possible versions are: 0.11.7-1.3.4 or uninstalled
 ├─restricted to versions * by an explicit requirement, leaving only versions 0.11.7-1.3.4
 ├─restricted by compatibility requirements with StatsBase [2913bbd2] to versions: 0.19.1-1.3.4 or uninstalled, leaving only versions: 0.19.1-1.3.4
 │ └─StatsBase [2913bbd2] log:
 │ ├─possible versions are: 0.24.0-0.33.18 or uninstalled
 │ └─restricted to versions 0.33.1-0.33 by NaiveBayes [9bbee03b], leaving only versions 0.33.1-0.33.18
 │ └─NaiveBayes [9bbee03b] log:
 │ ├─possible versions are: 0.5.2 or uninstalled
 │ └─NaiveBayes [9bbee03b] is fixed to version 0.5.2
 ├─restricted by compatibility requirements with Compat [34da2185] to versions: 0.20.0-1.3.4 or uninstalled, leaving only versions: 0.20.0-1.3.4
 │ └─Compat [34da2185] log:
 │ ├─possible versions are: 1.0.0-4.1.0 or uninstalled
 │ ├─restricted to versions * by an explicit requirement, leaving only versions 1.0.0-4.1.0
 │ ├─restricted by compatibility requirements with BlackBoxOptim [a134a8b2] to versions: 1.0.0-3.45.0
 │ │ └─BlackBoxOptim [a134a8b2] log:
 │ │ ├─possible versions are: 0.4.0-0.6.1 or uninstalled
 │ │ ├─restricted to versions * by an explicit requirement, leaving only versions 0.4.0-0.6.1
 │ │ └─restricted by compatibility requirements with Compat [34da2185] to versions: 0.5.0-0.6.1 or uninstalled, leaving only versions: 0.5.0-0.6.1
 │ │ └─Compat [34da2185] log: see above
 │ └─restricted by compatibility requirements with GaussianMixtures [cc18c42c] to versions: 3.6.0-3.45.0
 │ └─GaussianMixtures [cc18c42c] log:
 │ ├─possible versions are: 0.3.0-0.3.6 or uninstalled
 │ ├─restricted to versions * by an explicit requirement, leaving only versions 0.3.0-0.3.6
 │ └─restricted by compatibility requirements with StatsBase [2913bbd2] to versions: 0.3.3-0.3.6 or uninstalled, leaving only versions: 0.3.3-0.3.6
 │ └─StatsBase [2913bbd2] log: see above
 ├─restricted by compatibility requirements with Lathe [38d8eb38] to versions: 0.11.7-0.22.7, leaving only versions: 0.20.0-0.22.7
 │ └─Lathe [38d8eb38] log:
 │ ├─possible versions are: 0.0.3-0.1.8 or uninstalled
 │ └─restricted to versions * by an explicit requirement, leaving only versions 0.0.3-0.1.8
 └─restricted by compatibility requirements with Clarabel [61c947e1] to versions: 1.0.0-1.3.4 — no versions left
   └─Clarabel [61c947e1] log:
     ├─possible versions are: 0.1.0-0.1.1 or uninstalled
     └─restricted to versions * by an explicit requirement, leaving only versions 0.1.0-0.1.1
Stacktrace:
  [1] propagate_constraints!(graph::Pkg.Resolve.Graph, sources::Set{Int64}; log_events::Bool)
    @ Pkg.Resolve /Applications/Julia-1.7.app/Contents/Resources/julia/share/julia/stdlib/v1.7/Pkg/src/Resolve/graphtype.jl:1063
  [2] propagate_constraints! (repeats 2 times)
    @ /Applications/Julia-1.7.app/Contents/Resources/julia/share/julia/stdlib/v1.7/Pkg/src/Resolve/graphtype.jl:1000 [inlined]
  [3] simplify_graph!(graph::Pkg.Resolve.Graph, sources::Set{Int64}; clean_graph::Bool)
    @ Pkg.Resolve /Applications/Julia-1.7.app/Contents/Resources/julia/share/julia/stdlib/v1.7/Pkg/src/Resolve/graphtype.jl:1519
  [4] simplify_graph! (repeats 2 times)
    @ /Applications/Julia-1.7.app/Contents/Resources/julia/share/julia/stdlib/v1.7/Pkg/src/Resolve/graphtype.jl:1519 [inlined]
  [5] resolve_versions!(env::Pkg.Types.EnvCache, registries::Vector{Pkg.Registry.RegistryInstance}, pkgs::Vector{Pkg.Types.PackageSpec}, julia_version::VersionNumber)
    @ Pkg.Operations /Applications/Julia-1.7.app/Contents/Resources/julia/share/julia/stdlib/v1.7/Pkg/src/Operations.jl:335
  [6] targeted_resolve(env::Pkg.Types.EnvCache, registries::Vector{Pkg.Registry.RegistryInstance}, pkgs::Vector{Pkg.Types.PackageSpec}, preserve::Pkg.Types.PreserveLevel, julia_version::VersionNumber)
    @ Pkg.Operations /Applications/Julia-1.7.app/Contents/Resources/julia/share/julia/stdlib/v1.7/Pkg/src/Operations.jl:1154
  [7] tiered_resolve(env::Pkg.Types.EnvCache, registries::Vector{Pkg.Registry.RegistryInstance}, pkgs::Vector{Pkg.Types.PackageSpec}, julia_version::VersionNumber)
    @ Pkg.Operations /Applications/Julia-1.7.app/Contents/Resources/julia/share/julia/stdlib/v1.7/Pkg/src/Operations.jl:1139
  [8] _resolve(io::Base.TTY, env::Pkg.Types.EnvCache, registries::Vector{Pkg.Registry.RegistryInstance}, pkgs::Vector{Pkg.Types.PackageSpec}, preserve::Pkg.Types.PreserveLevel, julia_version::VersionNumber)
    @ Pkg.Operations /Applications/Julia-1.7.app/Contents/Resources/julia/share/julia/stdlib/v1.7/Pkg/src/Operations.jl:1160
  [9] add(ctx::Pkg.Types.Context, pkgs::Vector{Pkg.Types.PackageSpec}, new_git::Set{Base.UUID}; preserve::Pkg.Types.PreserveLevel, platform::Base.BinaryPlatforms.Platform)
    @ Pkg.Operations /Applications/Julia-1.7.app/Contents/Resources/julia/share/julia/stdlib/v1.7/Pkg/src/Operations.jl:1176
 [10] add(ctx::Pkg.Types.Context, pkgs::Vector{Pkg.Types.PackageSpec}; preserve::Pkg.Types.PreserveLevel, platform::Base.BinaryPlatforms.Platform, kwargs::Base.Pairs{Symbol, Base.TTY, Tuple{Symbol}, NamedTuple{(:io,), Tuple{Base.TTY}}})
    @ Pkg.API /Applications/Julia-1.7.app/Contents/Resources/julia/share/julia/stdlib/v1.7/Pkg/src/API.jl:268
 [11] add(pkgs::Vector{Pkg.Types.PackageSpec}; io::Base.TTY, kwargs::Base.Pairs{Symbol, Union{}, Tuple{}, NamedTuple{(), Tuple{}}})
    @ Pkg.API /Applications/Julia-1.7.app/Contents/Resources/julia/share/julia/stdlib/v1.7/Pkg/src/API.jl:149
 [12] add(pkgs::Vector{Pkg.Types.PackageSpec})
    @ Pkg.API /Applications/Julia-1.7.app/Contents/Resources/julia/share/julia/stdlib/v1.7/Pkg/src/API.jl:144
 [13] #add#27
    @ /Applications/Julia-1.7.app/Contents/Resources/julia/share/julia/stdlib/v1.7/Pkg/src/API.jl:142 [inlined]
 [14] add
    @ /Applications/Julia-1.7.app/Contents/Resources/julia/share/julia/stdlib/v1.7/Pkg/src/API.jl:142 [inlined]
 [15] try_prompt_pkg_add(pkgs::Vector{Symbol})
    @ Pkg.REPLMode /Applications/Julia-1.7.app/Contents/Resources/julia/share/julia/stdlib/v1.7/Pkg/src/REPLMode/REPLMode.jl:710
 [16] #invokelatest#2
    @ ./essentials.jl:716 [inlined]
 [17] invokelatest
    @ ./essentials.jl:714 [inlined]
 [18] check_for_missing_packages_and_run_hooks(ast::Any)
    @ REPL /Applications/Julia-1.7.app/Contents/Resources/julia/share/julia/stdlib/v1.7/REPL/src/REPL.jl:175
 [19] eval_user_input(ast::Any, backend::REPL.REPLBackend)
    @ REPL /Applications/Julia-1.7.app/Contents/Resources/julia/share/julia/stdlib/v1.7/REPL/src/REPL.jl:145
 [20] repl_backend_loop(backend::REPL.REPLBackend)
    @ REPL /Applications/Julia-1.7.app/Contents/Resources/julia/share/julia/stdlib/v1.7/REPL/src/REPL.jl:244
 [21] start_repl_backend(backend::REPL.REPLBackend, consumer::Any)
    @ REPL /Applications/Julia-1.7.app/Contents/Resources/julia/share/julia/stdlib/v1.7/REPL/src/REPL.jl:229
 [22] run_repl(repl::REPL.AbstractREPL, consumer::Any; backend_on_current_task::Bool)
    @ REPL /Applications/Julia-1.7.app/Contents/Resources/julia/share/julia/stdlib/v1.7/REPL/src/REPL.jl:362
 [23] run_repl(repl::REPL.AbstractREPL, consumer::Any)
    @ REPL /Applications/Julia-1.7.app/Contents/Resources/julia/share/julia/stdlib/v1.7/REPL/src/REPL.jl:349
 [24] (::Base.var"#930#932"{Bool, Bool, Bool})(REPL::Module)
    @ Base ./client.jl:394
 [25] #invokelatest#2
    @ ./essentials.jl:716 [inlined]
 [26] invokelatest
    @ ./essentials.jl:714 [inlined]
 [27] run_main_repl(interactive::Bool, quiet::Bool, banner::Bool, history_file::Bool, color_set::Bool)
    @ Base ./client.jl:379
 [28] exec_options(opts::Base.JLOptions)
    @ Base ./client.jl:309
 [29] _start()
    @ Base ./client.jl:495

julia> 

```

---

<div class="post-metadata">

**Author:** ![pgoulart](https://sea2.discourse-cdn.com/julialang/user_avatar/discourse.julialang.org/pgoulart/32/17514_2.png) [@pgoulart](https://discourse.julialang.org/u/pgoulart)\
**Post date:** [July 12, 2022, 10:08pm UTC](https://discourse.julialang.org/t/ann-clarabel-jl-new-convex-optimization-solver/83915/8 "2022-07-12T22:08:17Z")

</div>

The problem appears to be that the solver wants the DataFrames package version 1 or higher, but this is causing a conflict with other packages that you are using (e.g. NaiveBayes, GaussianMixtures and maybe a few others).

DataFrames is only used by the solver to nicely format the list of solver options when writing to the terminal and is probably overkill. I also don’t have any particular justification for having listed the required version as 1 or higher. You could try editing the Project.toml file to accept earlier versions, and it may well work.

Failing that, you could open this as an issue [here](https://github.com/oxfordcontrol/Clarabel.jl/issues) and we can take a look.

---

<div class="post-metadata">

**Author:** ![tbeason](https://sea2.discourse-cdn.com/julialang/user_avatar/discourse.julialang.org/tbeason/32/15898_2.png) [@tbeason](https://discourse.julialang.org/u/tbeason)\
**Post date:** [July 12, 2022, 11:13pm UTC](https://discourse.julialang.org/t/ann-clarabel-jl-new-convex-optimization-solver/83915/9 "2022-07-12T23:13:09Z")

</div>

> [@pgoulart](#):
>
> DataFrames is only used by the solver to nicely format the list of solver options when writing to the terminal and is probably overkill

DataFrames uses PrettyTables.jl as the printing backend, which works with a wide variety of data structures. Probably you could drop DataFrames and only depend on PrettyTables.

---

<div class="post-metadata">

**Author:** ![compleat](https://sea2.discourse-cdn.com/julialang/user_avatar/discourse.julialang.org/compleat/32/8958_2.png) [@compleat](https://discourse.julialang.org/u/compleat)\
**Post date:** [July 13, 2022, 10:10am UTC](https://discourse.julialang.org/t/ann-clarabel-jl-new-convex-optimization-solver/83915/10 "2022-07-13T10:10:39Z")

</div>

Hi. I didn’t intend to distract the discussion here. This looks like a great contribution. I had a thought to use it in my classes (I teach large classes) but I (and my students) use all the other packages mentioned above (esp. DataFrames) and can’t afford to risk breaking everything else.  
Looks like I will have to keep an eye on this for potential inclusion in my classes next Autumn (2023) if the dependencies get ironed out by then.  
Looks like a great contribution, though!

---

<div class="post-metadata">

**Author:** ![pgoulart](https://sea2.discourse-cdn.com/julialang/user_avatar/discourse.julialang.org/pgoulart/32/17514_2.png) [@pgoulart](https://discourse.julialang.org/u/pgoulart)\
**Post date:** [July 13, 2022, 3:07pm UTC](https://discourse.julialang.org/t/ann-clarabel-jl-new-convex-optimization-solver/83915/11 "2022-07-13T15:07:05Z")

</div>

I have released a patched version v0.1.2 that removes the dependency on DataFrames as suggested by @tbeason.

---

<div class="post-metadata">

**Author:** ![compleat](https://sea2.discourse-cdn.com/julialang/user_avatar/discourse.julialang.org/compleat/32/8958_2.png) [@compleat](https://discourse.julialang.org/u/compleat)\
**Post date:** [July 13, 2022, 3:14pm UTC](https://discourse.julialang.org/t/ann-clarabel-jl-new-convex-optimization-solver/83915/12 "2022-07-13T15:14:11Z")

</div>

Thank you so much! I’ll try it! [worked!!]

PS.I benchmarked it on the sample problem on the JuMP documentation  
[https://jump.dev/JuMP.jl/stable/tutorials/getting\_started/getting\_started\_with\_JuMP/](https://jump.dev/JuMP.jl/stable/tutorials/getting_started/getting_started_with_JuMP/)  
and found similar timing (.1 to .6 s, avg. ~ .3 s) to HiGHS. Is that what you would have expected?

---

<div class="post-metadata">

**Author:** ![compleat](https://sea2.discourse-cdn.com/julialang/user_avatar/discourse.julialang.org/compleat/32/8958_2.png) [@compleat](https://discourse.julialang.org/u/compleat)\
**Post date:** [July 13, 2022, 9:36pm UTC](https://discourse.julialang.org/t/ann-clarabel-jl-new-convex-optimization-solver/83915/13 "2022-07-13T21:36:38Z")

</div>

Just a comment (after playing around - not sure this is the best forum).

The abbreviated output of HiGHS has both objective value and feasibility status (which I find very useful), but the (default) abbreviated output of Clarabel does not. If a future version could include these, I would regard this as a great improvement, from a casual user point of view.

---

<div class="post-metadata">

**Author:** ![Paul\_Soderlind](https://sea2.discourse-cdn.com/julialang/user_avatar/discourse.julialang.org/paul_soderlind/32/1753_2.png) [@Paul\_Soderlind](https://discourse.julialang.org/u/Paul_Soderlind)\
**Post date:** [July 14, 2022, 7:08pm UTC](https://discourse.julialang.org/t/ann-clarabel-jl-new-convex-optimization-solver/83915/14 "2022-07-14T19:08:27Z")

</div>

Clarabel.jl seems to work very well. Thanks a lot.

I just noticed this while testing:

1. it does not seem to like `b[i] = (-)Inf`
2. [`NonnegativeConeT(2)`] has no `collect` method, which makes it cumbersome to filter out all restrictions where `b[i] = (-)Inf`.

Background: I wrote a wrapper to convert the restrictions to OSQP style: `lower <= Ax <= upper` and that sometimes involves non-binding constraints (`(-)Inf`)

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**Author:** ![pgoulart](https://sea2.discourse-cdn.com/julialang/user_avatar/discourse.julialang.org/pgoulart/32/17514_2.png) [@pgoulart](https://discourse.julialang.org/u/pgoulart)\
**Post date:** [July 16, 2022, 9:48am UTC](https://discourse.julialang.org/t/ann-clarabel-jl-new-convex-optimization-solver/83915/15 "2022-07-16T09:48:40Z")

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@Paul_Soderlind You are right, it fails ungracefully with an infinite bound in `b`. For the moment the only option is to pre-filter those constraints before passing to the solver. I will open an issue on it though in github, since it should at least give a clear warning or error in that case.

Regarding the use of `collect` : I don’t understand what you want to do there. What call are you trying to make, and what output are you expecting? I’m happy to implement the method if it’s useful, so please do open an issue if you like.

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**Author:** ![pgoulart](https://sea2.discourse-cdn.com/julialang/user_avatar/discourse.julialang.org/pgoulart/32/17514_2.png) [@pgoulart](https://discourse.julialang.org/u/pgoulart)\
**Post date:** [July 16, 2022, 9:54am UTC](https://discourse.julialang.org/t/ann-clarabel-jl-new-convex-optimization-solver/83915/16 "2022-07-16T09:54:30Z")

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@compleat When you say “abbreviated output” do you mean with `verbose = false`? The standard output already shows the progress of the primal/dual objectives and gives the final solver status at the end. With the verbose printing off it doesn’t print anything, but you can still get both objective value and status in the solver’s `result` field.

Maybe you mean via JuMP or something here though. Please do open an issue on github with an MWE and the output you hope to see and I can take a look.

> PS.I benchmarked it on the sample problem on the JuMP documentation and found similar timing (.1 to .6 s, avg. ~ .3 s) to HiGHS. Is that what you would have expected?

I have benchmarked against that solver, but if the problem you tried was an LP then it is not that surprising I think.

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**Author:** ![compleat](https://sea2.discourse-cdn.com/julialang/user_avatar/discourse.julialang.org/compleat/32/8958_2.png) [@compleat](https://discourse.julialang.org/u/compleat)\
**Post date:** [July 16, 2022, 10:22am UTC](https://discourse.julialang.org/t/ann-clarabel-jl-new-convex-optimization-solver/83915/17 "2022-07-16T10:22:34Z")

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> [@pgoulart](#):
>
> Maybe you mean via JuMP or something here though.

Yes, but it is no big deal. Regarding the benchmark, it really was more of quick check for the example (LP) problem to make sure everything was working OK, not a criticism.  
I’m sure whatever gains have been achieved on bigger problems will carry through on JuMP.

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**Author:** ![Paul\_Soderlind](https://sea2.discourse-cdn.com/julialang/user_avatar/discourse.julialang.org/paul_soderlind/32/1753_2.png) [@Paul\_Soderlind](https://discourse.julialang.org/u/Paul_Soderlind)\
**Post date:** [July 16, 2022, 11:30am UTC](https://discourse.julialang.org/t/ann-clarabel-jl-new-convex-optimization-solver/83915/18 "2022-07-16T11:30:38Z")

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> Regarding the use of `collect`

To filter out the `b[i]==Inf` cases, I currently need to build the vector `cones` in a loop, so it has as many elements as `length(b)`. Then, I can index into it with `cones[.!isinf.(b)]`.

It would nice if I could create this vector by `collect([NonnegativeConeT(2)])` or similarly to allow the indexing.

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**Author:** ![pgoulart](https://sea2.discourse-cdn.com/julialang/user_avatar/discourse.julialang.org/pgoulart/32/17514_2.png) [@pgoulart](https://discourse.julialang.org/u/pgoulart)\
**Post date:** [July 31, 2022, 1:08pm UTC](https://discourse.julialang.org/t/ann-clarabel-jl-new-convex-optimization-solver/83915/19 "2022-07-31T13:08:07Z")

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Dear Julia Optimizers,

A further announcement about this solver. We have released a pure Rust implementation of this solver to [Github](https://github.com/oxfordcontrol/Clarabel.rs), along with a Python interface to the Rust version.

There is also now a common documentation site for all versions here [https://github.com/oxfordcontrol/ClarabelDocs](https://github.com/oxfordcontrol/ClarabelDocs).

There you will find example code showing how to solve problems through all three interfaces.

Since someone is likely to ask: no, we have not yet benchmarked the performance of the Julia version vs the Rust version. My impression is that Rust is a bit faster, but not by much.

We will continue to develop both the Julia and Rust versions in parallel for the forseeable future. We will also continue to support the Python interface and are working on adding support for it through `cvxpy`.

Enjoy!

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**Author:** ![nrontsis](https://sea2.discourse-cdn.com/julialang/user_avatar/discourse.julialang.org/nrontsis/32/38535_2.png) [@nrontsis](https://discourse.julialang.org/u/nrontsis)\
**Post date:** [August 4, 2022, 1:24pm UTC](https://discourse.julialang.org/t/ann-clarabel-jl-new-convex-optimization-solver/83915/20 "2022-08-04T13:24:27Z")

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Thanks for the solver!

Having a rust implementation really helps in easily trying the solver in some codebases I work at.

I am curious: how do plan to avoid having the two parallel implementations (rust & julia) from diverging?

[Next page](https://discourse.julialang.org/t/ann-clarabel-jl-new-convex-optimization-solver/83915.md?page=2)