Cosmin G. Petra is a computational mathematician in the Center for Applied Scientific Computing at Lawrence Livermore National Laboratory. Cosmin's work focuses on high-performance computing algorithms for mathematical optimization and optimal control with emphasis on applications in complex energy systems and additive manufacturing.
Prior to joining the Center for Applied Scientific Computing, Cosmin was with Argonne National Laboratory as a computational mathematician and postdoctoral appointee and obtained an M.S. and a Ph.D. in Applied Mathematics from the University of Maryland, Baltimore County, in 2006 and 2009, respectively.
2016-present - Computational Mathematician, Center for Applied Scientific Computing, Lawrence Livermore National Laboratory.
2015-present - Adjunct Asst. Professor, School of Natural Sciencies, University of California, Merced.
2012-2016 - Asst. Computational Mathematician, MCS Division, Argonne National Laboratory.
2009-2012 - Postdoctoral Appointee, MCS Division, Argonne National Laboratory.
2006-2009 Ph.D. in Applied Mathematics - University of Maryland, Baltimore County, Baltimore, Maryland
2005-2006 M.S. in Applied Mathematics - University of Maryland, Baltimore County, Baltimore, Maryland
1998-2002 B.S. in Mathematics and Computer Science - Universitatea Babe?-Bolyai, Cluj-Napoca, Romania
Scalable algorithms for optimization, control, and design under uncertainty
High-performance computing algorithms for mathematical optimization at extreme scale
Scientific computing and extreme-scale computing
Recent and ongoing projects
Scalable algorithms for optimal control and design with applications in power grid and additive manufacturing.
HPC solver for PDE-constrained optimization - HiOp
HPC optimization solvers for stochastic optimization - PIPS
Uncertainty quantification and optimization of complex mathematical systems
Parallel modeling of large scale structured optimization problems on HPC platforms - StructJuMP
C. G. Petra, N. Chiang, M. Anitescu, A structured quasi-Newton algorithm for optimizing with incomplete Hessian information, in review, 2018
C. G. Petra, A memory-distributed quasi-Newton solver for nonlinear programming problems with a small number of general constraints, in review, 2018.
M. Schanen, F. Gilbert, C. G. Petra, M. Anitescu, Towards multiperiod AC-based contingency constrained optimal power flow at large scale, in print, “Proceedings to the 20th Power Systems Computation Conference”, 2018.
K. Kim, C. G. Petra, V. M. Zavala, An Asynchronous Bundle-Trust-Region Method for Dual Decomposition
of Stochastic Mixed-Integer Programming, in review, 2018.
C. G. Petra, F. Potra, A Homogeneous Model for Monotone Mixed Horizontal Linear Complementarity Problems, in review, 2018.
C. G. Petra, F. Qiang, M. Lubin, J. Huchette, On efficient Hessian computation using the edge pushing algorithm in Julia, accepted, Optimization Methods and Software, 2018.
N. Petra, C. G. Petra, Z. Zhang, E. M. Constantinescu, and M. Anitescu, A Bayesian Approach for Parameter Estimation with Uncertainty for Dynamic Power Systems, IEEE Transactions on Power Systems, Volume 32(4), pages 2735 - 2743, 2017. DOI.
C. G. Petra, V. M. Zavala, E. N. Ruiz, M. Anitescu, A high-performance computing framework for analyzing the economic impacts of wind correlation, Electric Power Systems Research, Volume 141, pages 372-380, 2016. DOI.
J. Huchette, M. Lubin, C. G. Petra, Parallel algebraic modeling for stochastic optimization. The First Workshop for High Performance Technical Computing in Dynamic Languages (HPTCDL), Supercomputing 2014. DOI.
C. G. Petra, O. Schenk, M. Anitescu. Real-time Stochastic Optimization of Complex Energy Systems on High Performance Computers. Computing in Science & Engineering (CiSE) 16(5), pages 32-42. DOI.
N. Chiang, C. G. Petra, V. M. Zavala, Structured Nonconvex Optimization of Large-Scale Energy Systems Using PIPS-NLP. 18th IEEE Power Systems Computations Conference, 2014.
C. G. Petra, Olaf Schenk, Miles Lubin, Klaus Gaertner. An augmented incomplete factorization approach for computing the Schur complement in stochastic optimization. SIAM J. Sci. Comput., 36-2, pages C139-C162, 2014. DOI
M. Lubin, K. Martin, C.G. Petra, and Burhaneddin Sandikic. On parallelizing dual decomposition in stochastic integer programming. Operations Research Letters 41(3), pages 252-258, 2013. DOI
M. Lubin, C. G. Petra, M. Anitescu, and V. Zavala. Scalable Stochastic Optimization of Complex Energy Systems. In Proceedings of 2011 International Conference for High Performance Computing, Networking, Storage and Analysis (SC '11), pages 64:1-64:10. DOI
M. Lubin, J. A. J. Hall, C. G. Petra, and M. Anitescu. Parallel distributed-memory simplex for large-scale stochastic LP problems., Computational Optimization and Applications 55(3), pages 571-596, 2013. Coin-OR INFORMS 2013 Cup winner + 2013 COAP Best Paper. DOI
M. Lubin, C. G. Petra, M. Anitescu. On the parallel solution of dense saddle-point linear systems arising in stochastic programming. Optimization Methods and Software, Volume 27, Number 4-5, Pages 845-864, 2012.
C. G. Petra, M. Anitescu. A preconditioning technique for Schur complement systems arising in stochastic optimization. Computational Optimization and Applications, Pages 315-344, Volume 52, Issue 2, 2012.
A. Draganescu, C. G. Petra. Multigrid preconditioning of linear systems for interior point methods applied to a class of bound-constrained control problems. SIAM Journal on Numerical Analysis, Volume 50, Number 1, Pages 328-353, 2012.
S. Al-Homidan, M. M. Alshahrani, C. G. Petra, F. A. Potra. Minimal Condition Number for Positive Definite Hankel Matrices using Semidefinite Programming. Linear Algebra and Its Applications, Volume 433, Issue 6, Pages 1101-1109, November 2010.
F. Gurtuna, C. G. Petra, F. A. Potra, O. Schevchenko, A. Vancea. Corrector-Predictor Methods for sufficient linear complementarity problems. Computational Optimization and Applications, Volume 48, Issue 3, Pages 453-485, April 2011. COAP Best Paper 2011 - Honorable mention.
C. G. Petra, B. Gavrea, M. Anitescu, F. A. Potra. A computational study on the use of an optimization based method in the simulation of large multi-body systems. Optimization Methods and Software, Volume 24, Issue 6, Pages 871-894, 2009.
C. G. Petra, "HiOp - User manual", Tech Report LLNL-SM-743591, 2018, Lawrence Livermore National Laboratory.
M. Anitescu, C. G. Petra. Higher-Order Confidence Intervals for Stochastic Programming using Bootstrapping, 2012, Tech Report ANL/MCS-P1964-1011. Argonne National Laboratory.
R. Serban, C. G. Petra. User Documentation for IDAS v1.0.0, 2008, Report UCRL-SM-234051, Lawrence Livermore National Laboratory.
Selected recent talks
Parallel Structured Quasi-Newton Methods for Material Design, SIAM Conference on Optimization (2017), SIAM Student Chapter, UC Davis (2017)
Optimization and Design of Complex Engineering Systems using High-Performance Computing, SIAM-CSE (2017).
Scalable stochastic optimization of complex energy systems on high-performance computers, Zuse Institut Berlin (2016)
Modelling and solving SC-ACOPF problems in parallel, FERC Technical Conference on Increasing Market and Planning Efficiency through Improved Software (2016)
Optimization-based parameter estimation of dynamic power grid systems, SIAM UQ (2016).
Scalable optimization of complex energy systems using high-performance computing, Joint Mathematics Meeting, SIAM Parallel Processing (2016)
Parallel algebraic modelling for stochastic optimization in Julia, Platform for Advanced Scientific Computing Conference (2015)
On the role of wind correlation in the economic dispatch of power grid systems, SIAM CSE (2015)
This document is released under LLNL IM Release Number LLNL-WEB-403310.