# YALMIP vs JuMP

**URL:** https://discourse.julialang.org/t/yalmip-vs-jump/30776
**Category:** Optimization (Mathematical)
**Created:** [November 6, 2019, 4:05am UTC](https://discourse.julialang.org/t/yalmip-vs-jump/30776 "2019-11-06T04:05:18Z")
**Posts on this page:** 1
**Showing post:** 13

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### Author: ![raktim](https://sea2.discourse-cdn.com/julialang/user_avatar/discourse.julialang.org/raktim/32/11188_2.png) [@raktim](https://discourse.julialang.org/u/raktim)
#### Post date: [November 8, 2019, 12:34am UTC](https://discourse.julialang.org/t/yalmip-vs-jump/30776/13 "2019-11-08T00:34:49Z")

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I think so too! Coming from cvx/YALMIP, I find it hard to transfer to JuMP. I am documenting these, especially for folks coming from control/estimation and are used to cvx/YALMIP. Perhaps it can be included with JuMP examples in the future.

With Dualization, things are better. However I am still running into difficulties in implementing constraints such as s\ge0 where s\in\mathcal{R}^n.

The documentation for Dualization is probably aimed at developers at this time.  
I have to spend some time on this to figure these basic things.

It may be better to dualize the problem just before calling \texttt{optimize!(...)}. But then I don’t know how to recover the primal variables from the dual solution.  
Where can I get information about this?

I am going to mark this topic as resolved.

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