New repository for Rmosek interface available!

As some Rmosek users might know, we have been experiencing technical issues with the CRAN repository that prevent us from updating the Rmosek package.

We have decided to host the package repository at  mosek.com, which will make it easy for users to install the latest release simply from the R command line. Follow us on the dedicated website at

 http://rmosek.r-forge.r-project.org

for more updates and details!

MOSEK version 7 and Rmosek

Currently, the version of the Rmosek interface on CRAN does not support MOSEK version 7. This issue may never be fixed because it seems CRAN does not like commercial software anymore and therefore  MOSEK  may be removed from CRAN.

Therefore, an updated version of Rmosek inteface is available for download here.

Rmosek installation issues: A workaround

Some customers have reported problems with installing Rmosek on R 2.15.3. The installation seems to give the same error as described at:

http://r.789695.n4.nabble.com/Error-Installing-Package-with-Dependency-on-quot-Matrix-quot-td4638544.html

We suggest the following workaround. First manually installing the Matrix package using the command

> install.packages("Matrix", dependencies=TRUE)

and then installing Rmosek as 

> install.packages("Rmosek", type="source", INSTALL_opts="--no-multiarch", configure.vars="PKG_MOSEKHOME=/home/joachim/rhome/mosek/6/tools/platform/linux64x86 PKG_MOSEKLIB=mosek64")

should solve the problem. The last command line should be adapted to the local platform and installation of course.

Portfolio optimization whitepaper in MATLAB, R and Python

We revised our whitepaper on portfolio optimization.

The whitepaper gives an introduction to portfolio optimization using the MOSEK conic optimizer from MATLAB, R and Python and includes topics such as:

• Conic formulations of standard Markowitz portfolio problems.
• Minimum risk/maximum return formulations.
• Computing the efficient frontier.
• Computing the maximum Sharpe ratio.
• Exploiting low-rank structure in the covariance matrix to reduce solution time, including factor models.
• Transaction costs with market impact, modeled as a conic quadratic problem.
• Transactions costs with a fixed term, modeled as a conic mixed-integer problem. 

Historical data from the S&P500 index is used in all examples.

The Markowitz portfolio optimization whitepaper and data can be downloaded from the MOSEK publications page.

Feature update in the R to MOSEK interface

A new version of the Rmosek package is available. The new features are:

• support for separable convex optimization,
• new methods to formulate quadratic programs on conic quadratic form,
• and a simplified installation process.

Furthermore, a change in input format regarding bounds and cones have been made to decrease the time it takes to construct large problems. Please see the NEWS file for details.

R to MOSEK interface

R is popular tool for statistical computing that has many users in financial industry for example. Therefore, MOSEK is pleased to announce that an open source R to MOSEK interface is now available at R-forge. The interface makes it possible to use the powerful MOSEK optimizers for

• linear,
• conic quadratic (aka. second order cone), 
• and  mixed-integer 

problems from R. Note any convex quadratic problem can reformulated as a conic quadratic problem and hence the interface can be employed to solve convex quadratic and quadratically constrained problems.