**PEN**OPT
solvers grew up from an early version of the code PENNON. While the
original PENNON was a
typical academic software, whose primal
goal was to test and verify new algorithms, the main stress in **
PEN**OPT
solvers is given to careful and efficient programming and robustness.
Hence, **
PEN**OPT solvers can solve all problems that the
original PENNON can, and
will always be equally fast or faster.
Results
of benchmark tests for the original PENNON can be
found here.

The codes are or has been used by more than 70 users from both the industry and academia.

References:

Complete theory, full algorithm and results of extensive testing now available in the thesis of Michael Stingl "On the Solution of Nonlinear Semidefinite Programs by Augmented Lagrangian Methods". The thesis, submitted in August 2005, is available here.

- M. Kocvara and M. Stingl. PENNON - A Generalized Augmented
Lagrangian Method for Semidefinite Programming. In G. Di Pillo and A.
Murli, eds.,
*High Performance Algorithms and Software for Nonlinear Optimization*, Kluwer Academic Publishers, Dordrecht, 2003, pp. 297-315 - M. Kocvara and M. Stingl. PENNON - A Code for Convex
Nonlinear
and Semidefinite
Programming.
*Optimization Methods and Software*18(3):317-333, 2003 - D. Henrion, M. Kocvara and M. Stingl. Solving simultaneous stabilization BMI problems with PENNON. LAAS-CNRS Research Report No. 03549, December 2003, LAAS Toulouse (ps).
- D. Henrion, J. Löfberg, M. Kocvara, M. Stingl. Solving polynomial static output feedback problems with PENBMI. LAAS-CNRS Research Report No. 05165, March 2005. Proceedings of the joint IEEE Conference on Decision and Control and European Control Conference, Sevilla, Spain, December 2005 (pdf).
- M. Kocvara and M. Stingl. On the solution of large-scale SDP problems by the modified barrier method using iterative solvers. Mathematical Programming Series B, 109(2-3):413-444, 2007.
- M. Kocvara and M. Stingl. PENNON: Software for Linear and Nonlinear Matrix Inequalities. In: Handbook on Semidefinite, Conic and Polynomial Optimization, Anjos, Miguel F.; Lasserre, Jean B. (Eds.), Springer, 2012, pp. 755-794, ISBN 978-1-4614-0768-3