We start the new year 2019 with a Beta version release of MOSEK 9.

All information and instructions on this page:

## Tuesday, January 15, 2019

## Thursday, December 13, 2018

### Christmas tree denoising via SDP

In our last year's Christmas special we ran logistic regression on a sampled Christmas tree. By public demand this year we treat the tree with some semidefinite programming.

We start with a $50 \times 100$ binary image with noise.

We start with a $50 \times 100$ binary image with noise.

It is convenient to encode the image as a sequence of pixels $x_i\in\{-1,1\}$. Then we can write (see eg. here) a simple binary quadratic denoising model

$$\mathrm{maximize} \sum_{i\sim j}z_iz_j+\gamma\sum_i x_iz_i,\quad z_i\in\{-1,1\}$$

which favors similarity between neighboring ($i\sim j$) pixels in $z$ and the similarity between $z$ and the original $x$, with tradeoff between the two measures provided by $\gamma$.

Binary quadratic problems have a natural SDP relaxation, in this case

$$ \begin{array}{ll} \mathrm{mazimize} & \sum_{i\sim j} Z_{ij} +\gamma x^Tz \\ \mathrm{s.t.} & \left[ \begin{array}{cc}Z&z\\z^T&1\end{array} \right] \succeq 0, \\ & \mathrm{diag}(Z)=1.\end{array} $$

We solve the SDP for various $\gamma$ ($Z$ has dimension $5000$; this is not the most efficient image processing algorithm ever invented). To further simplify we don't round but let the raw $z$ interpolate between shades of green.

Merry Christmas from the MOSEK team.

## Tuesday, November 27, 2018

### Geometric programming preview

Geometric programs (GP) can be conveniently expressed in conic form using the exponential cone

$$ x\geq y\exp(z/y),\quad y>0$$

in the upcoming version 9 of MOSEK. Here is a preview implementation of some signal-to-interference-and-noise optimization problems in Python Fusion:

You can read more about modeling GPs in our Modeling Cookbook:

MOSEK's previous GP interface (scopt/dgopt/expopt), which is being phased out, relied on the slower and less accurate general nonlinear optimizer. You can read more about version 9 plans in

$$ x\geq y\exp(z/y),\quad y>0$$

in the upcoming version 9 of MOSEK. Here is a preview implementation of some signal-to-interference-and-noise optimization problems in Python Fusion:

The key log-sum-exp constraint is just a few lines:

## Tuesday, November 6, 2018

### Reseller in China

shanshu.ai (Cardinal Operations) is the official reseller of MOSEK in China. Customers in China interested in acquiring a MOSEK license are welcome to visit

for details.

for details.

## Friday, August 31, 2018

### Conic modeling cheatsheet

As a supplement to the MOSEK Modeling Cookbook, here is s a quick reference guide to some useful conic models (click image for PDF):

## Friday, August 17, 2018

### Solving SDP with millions of matrix variables

Is it feasible in practice to solve semidefinite optimization problems with a huge number of matrix variables?

We recently received a problem from a structural engineering application with approximately the following parameters:

We recently received a problem from a structural engineering application with approximately the following parameters:

**1 500 000**three-dimensional matrix variables,- 750 000 three-dimensional rotated quadratic cones,
- $8\cdot 10^6$ scalar variables, $15\cdot 10^6$ linear constraints, $45\cdot 10^6$ nonzeros.

On a DELL PowerEdge R730 server with 2 Xeon E5-2687W v4 3.0GHZ the optimal solution is found in about

**161 minutes**on 24 threads using the latest MOSEK 8.1.0.59 with memory peaking at about 60GB. Due to the nature of the problem we disabled the linear dependency check and otherwise used all standard parameter settings.## Friday, June 29, 2018

### .NET Core support

From release 8.1.0.56 we initialize support for .NET Core, the cross-platform implementation of .NET.

MOSEK for .NET Core is distributed as a platform-independent NuGet package, which can be downloaded directly from our website. See the documentation for .NET APIs for installation instructions.

MOSEK for .NET Core is distributed as a platform-independent NuGet package, which can be downloaded directly from our website. See the documentation for .NET APIs for installation instructions.

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