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PENNON: Software for Linear and Nonlinear Matrix Inequalities

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Handbook on Semidefinite, Conic and Polynomial Optimization

Part of the book series: International Series in Operations Research & Management Science ((ISOR,volume 166))

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Abstract

We present a collection of computer programs for the solution of linear and nonlinear semidefinite optimization problems. After briefly discussing the underlying algorithm, the generalized augmented Lagrangian method, we describe details of the specific programs for linear, bilinear and general nonlinear semidefinite optimization problems. For each of the programs we present typical application areas and examples.

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Notes

  1. 1.

    plato.la.asu.edu/bench.html.

  2. 2.

    plato.asu.edu/ftp/sparse_sdp.html.

  3. 3.

    plato.asu.edu/ftp/sparse_sdp.html.

  4. 4.

    See http://www.mathematik.uni-trier.de/ ∼ leibfritz/Proj_TestSet/NSDPTestSet.htm.

References

  1. Alizadeh, F., Eckstein, J., Noyan, N., Rudolf, G.: Arrival rate approximation by nonnegative cubic splines. Operations Research 56, 140–156 (2008)

    Article  Google Scholar 

  2. Blondel, V.D.: Simultaneous stabilization of linear systems. MacMillan, New York (1994)

    Book  Google Scholar 

  3. Blondel, V.D., Tsitsiklis, J.N.: A survey of computational complexity results in systems and control. Automatica 36(9), 1249–1274 (2000)

    Article  Google Scholar 

  4. Bonnans, F.J., Shapiro, A.: Perturbation Analysis of Optimization Problems. Springer-Verlag New-York (2000)

    Google Scholar 

  5. de Boor, C., Daniel, J.W.: Splines with nonnegative b-spline coefficients. Math. Comp. 28(4-5), 565–568 (1974)

    Google Scholar 

  6. Duff, I.S., Reid, J.K.: MA 27—A set of Fortran subroutines for solving sparse symmetric sets of linear equations. Tech. Report R.10533, AERE, Harwell, Oxfordshire, UK (1982)

    Google Scholar 

  7. Fourer, R., Gay, D.M., Kerningham, B.W.: AMPL: A Modeling Language for Mathematical Programming. The Scientific Press (1993)

    Google Scholar 

  8. Fujisawa, K., Kojima, M., Nakata, K.: Exploiting sparsity in primal-dual interior-point method for semidefinite programming. Mathematical Programming 79, 235–253 (1997)

    Article  Google Scholar 

  9. Fujisawa, K., Kojima, M., Nakata, K., Yamashita, M.: SDPA User’s Manual—Version 6.00. Technical report, Department of Mathematical and Computing Science, Tokyo University of Technology (2002)

    Google Scholar 

  10. Fukuda, M., Kojima, M., Shida, M.: Lagrangian dual interior-point methods for semidefinite programs. SIAM J. Optimization 12, 1007–1031 (2002)

    Article  Google Scholar 

  11. Geiger C., Kanzow, C.: Numerische Verfahren zur Lösung unrestringierter Optimierungsaufgaben. Springer-Verlag (1999). In German.

    Google Scholar 

  12. Goh, K.C., Turan, L., Safonov, M.G., Papavassilopoulos, G.P., Ly, J.H.: Biaffine matrix inequality properties and computational methods. In: Proceedings of the American Control Conference, Baltimore, MD (1994)

    Google Scholar 

  13. Henrion, D., Kočvara, M., Stingl, M.: Solving simultaneous stabilization bmi problems with pennon. LAAS-CNRS research report no. 04508, LAAS, Toulouse (2003)

    Google Scholar 

  14. Henrion, D., Tarbouriech, S., Šebek, M.: Rank-one LMI approach to simultaneous stabilization of linear systems. Systems and control letters 38(2), 79–89 (1999)

    Article  Google Scholar 

  15. Higham, N.J.: Computing the nearest correlation matrix—A problem from finance. IMA J. Numer. Anal 22(3), 329–343 (2002)

    Article  Google Scholar 

  16. Kočvara, M., Stingl, M.: PENNON—a code for convex nonlinear and semidefinite programming. Optimization Methods and Software 18(3), 317–333 (2003)

    Article  Google Scholar 

  17. Kočvara, M., Stingl, M.: Free material optimization: Towards the stress constraints. Structural and Multidisciplinary Optimization 33(4-5), 323–335 (2007)

    Article  Google Scholar 

  18. Kočvara, M., Stingl, M.: 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)

    Article  Google Scholar 

  19. Kočvara, M., Stingl, M.: On the solution of large-scale SDP problems by the modified barrier method using iterative solvers: Erratum. Mathematical Programming (Series B) 120(1), 285–287 (2009)

    Article  Google Scholar 

  20. Leibfritz, F.: COMPle​ib: COnstrained Matrix–optimization Problem library – a collection of test examples for nonlinear semidefinite programs, control system design and related problems. Technical report, University of Trier, Department of Mathematics, D–54286 Trier, Germany (2003)

    Google Scholar 

  21. Li, Y., Zhang, L.: A new nonlinear lagrangian method for nonconvex semidefinite programming. Journal of Applied Analysis 15(2), 149–172 (2009)

    Article  Google Scholar 

  22. Löfberg, J.: YALMIP : A toolbox for modeling and optimization in MATLAB. In: Proceedings of the CACSD Conference, Taipei, Taiwan (2004)

    Google Scholar 

  23. Malick, J.: A dual approach to semidefinite least-squares problems. SIAM J. Matrix Analysis and Applications 26(1), 272–284 (2005)

    Article  Google Scholar 

  24. Mittelmann, H.D.: An independent benchmarking of SDP and SOCP solvers. Math. Prog. 95, 407–430 (2003)

    Article  Google Scholar 

  25. Morales, J.L., Nocedal, J.: Automatic preconditioning by limited memory quasi-Newton updating. SIAM Journal on Optimization 10, 1079–1096 (2000)

    Article  Google Scholar 

  26. Nesterov, Y.: Squared functional systems and optimization problems. In: Frenk, H., Roos, K., Terlaky, T. (eds.) High performance optimization (Chapter 17), pp. 405–440. Kluwer Academic Publishers, Dordrecht (2000)

    Chapter  Google Scholar 

  27. Ng, E., Peyton, B.W.: Block sparse cholesky algorithms on advanced uniprocessor computers. SIAM Journal on Scientific Computing 14, 1034–1056 (1993)

    Article  Google Scholar 

  28. Nocedal, J., Wright, S.: Numerical Optimization. Springer Series in Operations Research. Springer, New York (1999)

    Book  Google Scholar 

  29. Noll, D.: Local convergence of an augmented lagrangian method for matrix inequality constrained programming. Optimization Methods and Software 22(5), 777–802 (2007)

    Article  Google Scholar 

  30. Polyak, R.: Modified barrier functions: Theory and methods. Mathematical Programming 54, 177–222 (1992)

    Article  Google Scholar 

  31. Stingl, M.: On the Solution of Nonlinear Semidefinite Programs by Augmented Lagrangian Methods. PhD thesis, Institute of Applied Mathematics II, Friedrich-Alexander University of Erlangen-Nuremberg (2006)

    Google Scholar 

  32. Wächter, A., Biegler, L.T.: On the implementation of a primal-dual interior point filter line search algorithm for large-scale nonlinear programming. Math. Prog. 106, 25–57 (2006)

    Article  Google Scholar 

  33. Werner, R., Schöttle, K.: Calibration of correlation matrices—SDP or not SDP. Preprint available from www.gloriamundi.org (2007)

  34. Wright, S.: Primal-Dual Interior-Point Methods. SIAM, Philadelphia, PA (1997)

    Book  Google Scholar 

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Acknowledgements

The authors would like to thank Didier Henrion and Johan Löfbeg for their constant help during the code development. The work has been partly supported by grant A100750802 of the Czech Academy of Sciences (MK) and by DFG cluster of excellence 315 (MS). The manuscript was finished while the first author was visiting the Institute for Pure and Applied Mathematics, UCLA. The support and friendly atmosphere of the Institute are acknowledged with gratitude.

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Correspondence to Michal Kocvara .

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Kocvara, M., Stingl, M. (2012). PENNON: Software for Linear and Nonlinear Matrix Inequalities. In: Anjos, M.F., Lasserre, J.B. (eds) Handbook on Semidefinite, Conic and Polynomial Optimization. International Series in Operations Research & Management Science, vol 166. Springer, New York, NY. https://doi.org/10.1007/978-1-4614-0769-0_26

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