Convex.jl
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jump-dev
A Julia package for disciplined convex programming
Hi Team I read about [the project](https://github.com/jump-dev/GSOC2020/blob/master/ideas-list.md#optimization-problem-differentiation) to enable `JuMP.jl` differentiate solution of problem w.r.t. its parameters. CVXPY implemented this ability and [the article](http://web.stanford.edu/~boyd/papers/pdf/diff_cvxpy.pdf) accompanying this feature describes a new...
I think support for disciplined quasiconvex programming would fit nicely into this package. Analogous to disciplined convex programming, there also is the language of disciplined quasiconvex programming [1]. Similar to...
Now that Convex.jl is backed by MOI, we can take advantage of its ability to write to file formats (https://jump.dev/MathOptInterface.jl/stable/apimanual/#File-formats-1). It's actually pretty easy to do this already, e.g. ```julia...
The purpose of this issue is to collate suggestions for documentation improvements into a single issue. (We did thes quite successfully in JuMP https://github.com/jump-dev/JuMP.jl/issues/2348). If you are looking to contribute...
Is it possible to provide an initial guess of the unknowns when using Convex.jl to solve a convex optimization problem, without using a warmstart from a previous solve? For example,...
I was playing around with implementing a basic linear SVM in both Convex.jl and JuMP and noticed a pretty significant penalty for model generation in Convex.jl: ``` using JuMP using...
Convex.jl lacks support for a lot of broadcasting operations. This causes a bunch of problems like this: ```Julia julia> using Convex julia> x = Variable(2) Variable size: (2, 1) sign:...
Probably using * https://gist.github.com/pfitzseb/4b910449e21b51fed4b4a2a7c20912ff * https://github.com/JuliaDocs/Documenter.jl/pull/1577 to avoid issues like https://discourse.julialang.org/t/is-convex-jl-still-working/68624/5
I am trying to use Convex.jl to interface with the OSQP solver. However, for some reason I am unable to use quadform in the objective and still have it link...
I have an example of a moderately sized problem where problem formulation is consuming massive memory. I have no idea why this happens. Is there something inherently difficult to this...
