New release with some small fixes and additions

New release with numerous small fixes.

Minor release with some small fixes.

Minor release with some small fixes.

The previous version had some configuration issues which caused some of the fixes to get lost. Hence, this release includes the fixes announced in the previous release and some additional fixes related to scs.

This release fixes some minor issues and fixes a warning occurring after upgrading to Gurobi 6.0.

This release fixes numerous small issues and adds support for the semidefinite programming presolver frlib developed by Frank Permenter and Pablo Parrilo. It also features some internal changes which could break old(-style) code.

I have been asked several times if I would consider an Octave port. My answer has been no, based on my, as it turns out, flawed idea that it would require a lot of changes (and my general lack of interest in Octave). Well, yet another request came, and I decided to download Octave and test it. Turned out the changes required were absolutely minimal. Hence, as of now, YALMIP runs in both MATLAB and Octave.

Minor release fixing some small issues.

Minor release with new solvers, bug-fixes and performance tweaks.

Minor release with new solvers and various bug-fixes.

Minor release.

Minor release.

I have been asked frequently recently whether is is possible to have YALMIP code in Simulink simulations. The answer is yes, but there are some caveats.

Required release if you use MATLAB R2013a.

Required release if you use MATLAB R2013a.

Minor release with some small enhancements and, for the first time in 10 years probably, support for a new SDP solver.

Minor release.

A common application of integer programming is the unit commitment problem in power generation, i.e., scheduling of set of power plants in order to meet a current and forecasted power demand while minimizing costs.

To see how such a problem is modeled in YALMIP, a unit commitment example is now available.

If you would like to see how additional constraints or logic behaviour can be added to the model, let me know!

The command optimizer is used to reduce overhead when solving a large number of optimization problems which only differ through some changing parameter in the model. Previously, this feature has been limited to models where the changing parameter has entered the model affinely. Read more...

Minor release fixing some small issues.

Minor release.

In coming versions of YALMIP, the operators `>`

and `<`

will not be allowed in constraints. Hence, to prepare, you should replace occurrences of these operators with `<=`

and `>=`

.

Important release. A massive performance degradation was accidentally added to the two most recent releases. Hence, the latest release should be installed.

A common question I get is along the lines *"how can I solve a nonconvex QP using SeDuMi"*

A question on the YALMIP forum on the SeDuMi homepage essentially boiled down to *"how can I generate sum-of-squares solutions which really are feasible, i.e. true certificates"*
Read more...

I was asked by a colleague today on how to compute the worst-case ∞-norm of a matrix **A(p)** linearly parameterized in an uncertainty **p** constrained to a polytope.
Read more...

A paper describing the robust optimization module has been accepted for publication.

Philipp Rostalski has made his Convex Algebraic Geometry Wiki public. Among other things, you can find the tool Bermeja, which is partly based on YALMIP.

Ever wondered how to compute the L_{1} Chebyshev ball? Check out the new article on polytopic geometry using YALMIP and MPT

Material from the mini-course on YALMIP and MPT for Optimization and Modelling in Control at the Swedish control meeting is now available for download. Please note that you need the latest release to run some of the examples.

I am very pleased to announce that YALMIP now supports the mixed-integer linear programming solver Gurobi. The solver is interfaced via Gurobi mex developed by Wotao Yin.

A new sum-of-squares example has been made available. This example concentrates on the pre-processing capabilities of YALMIPs sum-of-squares module.

A poster and a related paper (Löfberg:2008b) presenting the robust optimization module has been uploaded to the Wiki.