Nuit Blanche: Convex Optimization in Julia

Everybody that has read this blog a few times knows our collective reliance on CVX as a means of figuring out, in small problems, if things work out. While CVX is a great tool it relies on Matlab which is not free. As nice as Matlab is, some people want to have CVX capabilities outside of that framework. Today and tomorrow, we will feature two instances of that. The first one is an implementation in Julia.

Convex Optimization in Julia by Madeleine Udell, Karanveer Mohan, David Zeng, Jenny Hong, Steven Diamond, Stephen Boyd

This paper describes Convex.jl, a convex optimization modeling framework in Julia. Convex.jl translates problems from a user-friendly functional language into an abstract syntax tree describing the problem. This concise representation of the global structure of the problem allows Convex.jl to infer whether the problem complies with the rules of disciplined convex programming (DCP), and to pass the problem to a suitable solver. These operations are carried out in Julia using multiple dispatch, which dramatically reduces the time required to verify DCP compliance and to parse a problem into conic form.

From the GitHub page ( https://github.com/cvxgrp/Convex.jl )

Convex.jl

Convex.jl is a julia package for Disciplined Convex Programming. Note that Convex.jl was previously called CVX.jl. This package is under active development; interfaces are not guaranteed to be stable, and bugs should be expected. Nevertheless, we try to fix problems that come up as swiftly as we can. We'd love bug reports and feature requests!

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Credits
Currently, Convex.jl is developed and maintained by:

Jenny Hong

Karanveer Mohan

Madeleine Udell

David Zeng

The Convex.jl developers also thank:

Stephen Boyd: Professor of Electrical Engineering, Stanford University. He is also the co-author of the book Convex Optimization. We thank Professor Boyd for his continuous input and support.

Steven Diamond: Convex.jl started out as a wrapper around Steven Diamond's CVXPY and its design has been inspired from CVXPY. We greatly appreciate Steven Diamond's experienced and continual guidance. In addition, Steven Diamond also wrote http://dcp.stanford.edu/ to teach disciplined convex programming, a useful resource for Convex.jl users.