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Publications

Publications

Journal Publications

P. Giselsson, Nonlinear Forward-Backward Splitting with Projection Correction. SIAM Journal on Optimization. Accepted for publication. 2021.

E. Ryu, A. Taylor, C. Bergeling, and P. Giselsson, Operator Splitting Performance Estimation: Tight contraction factors and optimal parameter selection. SIAM Journal on Optimization, 30(3):2251 - 2271, 2020.

C. Grussler and P. Giselsson, Low-Rank Inducing Norms with Optimality Interpretations. SIAM Journal on Optimization, 28(4):3057 - 3078, 2018.

P. Giselsson and M. Fält, Envelope Functions: Unifications and Further Properties. Journal of Optimization Theory and Applications, 178(3):673 - 698, 2018.

C. Grussler, A. Rantzer, and P. Giselsson, Low-Rank Optimization with Convex Constraints. IEEE Transactions on Automatic Control, 63(11):4000 - 4007, 2018.

P. Giselsson, Tight Global Linear Convergence Rate Bounds for Douglas-Rachford Splitting. Journal of Fixed-Point Theory and Applications, 2017. doi:10.1007/s11784-017-0417-1.

P. Giselsson and S. Boyd, Linear Convergence and Metric Selection in Douglas Rachford Splitting and ADMM. Transactions of Automatic Control. 62(2):532 - 544, February 2017.

P. Giselsson and S. Boyd, Metric Selection in Fast Dual Forward Backward Splitting. Automatica, 62:1-10, December 2015.

P. Giselsson and A. Rantzer, On feasibility, stability and performance in distributed model predictive control. IEEE Transactions on Automatic Control, 59(4):1031-1036, April 2014.

M. D. Doan, P. Giselsson, T. Keviczky, B. De Schutter, and A. Rantzer, A distributed accelerated gradient algorithm for distributed model predictive control of a hydro power valley. Control Engineering Practice, 21(11):1594-1605, 2013.

A. Lindholm and P. Giselsson, Minimization of economical losses due to utility disturbances in the process industry. Journal of Process Control, 23(5):767-777, 2013.

P. Giselsson, M. D. Doan, T. Keviczky, B. De Schutter, and A. Rantzer, Accelerated gradient methods and dual decomposition in distributed model predictive control. Automatica, 49(3):829-833, 2013.

Preprints

M. Morin and P. Giselsson, Sampling and Update Frequencies in Proximal Variance Reduced Stochastic Gradient Methods. Submitted. 2020.

P. Giselsson and W. Moursi, On compositions of special cases of Lipschitz continuous operators. Submitted 2020.

M. Morin and P. Giselsson, SVAG: Stochastic Variance Adjusted Gradient Descent and Biased Stochastic Gradients. Submitted. 2019.

M. Fält and P. Giselsson, System Identification for Hybrid Systems using Neural Networks. Submitted. 2019.

C. Grussler and P. Giselsson, Efficient Proximal Mapping Computation for Unitarily Invariant Low-Rank Inducing Norms. Submitted. 2018.

Conference Publications

M. Fält and P. Giselsson, QPDAS: Dual Active Set Solver for Mixed Constraint Quadratic Programming. In Proceedings of the 58th IEEE Conference on Decision and Control (CDC), Nice, France, Dec 2019.

C. Grussler and P. Giselsson, Optimality interpretations for atomic norms, In Proceedings of the 18th European Control Conference (ECC), Naples, Italy, June 2019.

M. S. Darup, G. Book, and P. Giselsson, Towards real-time ADMM for linear MPC, In Proceedings of the 18th European Control Conference (ECC), Naples, Italy, June 2019.

C. Grussler and P. Giselsson, Local Convergence of Proximal Splitting Methods for Rank Constrained Problems. In Proceedings of the 56th Conference on Decision and Control, Melbourne, Australia, Dec 2017.

M. Fält and P. Giselsson, Optimal Convergence Rates for Generalized Alternating Projections. In Proceedings of  the 56th Conference on Decision and Control, Melbourne, Australia, Dec 2017.

M. Fält and P. Giselsson, Line Search for Generalized Alternating projections. In Proceedings of the 2017 American Control Conference, Seattle, USA, May 2017.

P. Giselsson, M. Fält, and S. Boyd, Line Search for Averaged Operator Iteration. In Proceedings of  the 55th Conference on Decision and Control, Las Vegas, USA, Dec 2016.

P. Giselsson, Tight Linear Convergence Rate Bounds for Douglas-Rachford Splitting and ADMM. In Proceedings of  the 54th Conference on Decision and Control, Osaka, Japan, Dec 2015.

P. Giselsson and S. Boyd, Diagonal Scaling in Douglas-Rachford Splitting and ADMM. In Proceedings of the 53rd IEEE Conference on Decision and Control, pp. 5033-5039. Los Angeles, CA, December 2014.

P. Giselsson and S. Boyd, Preconditioning in Fast Dual Gradient Methods. In Proceedings of the 53rd IEEE Conference on Decision and Control, pp. 5040-5045. Los Angeles, CA, December 2014.

P. Giselsson and S. Boyd, Monotonicity and Restart in Fast Gradient Methods. In Proceedings of the 53rd IEEE Conference on Decision and Control, pp. 5058-5063. Los Angeles, CA, December 2014.

P. Giselsson, Improved Fast Dual Gradient Methods for Embedded Model Predictive Control. In Proceedings of the 2014 IFAC World Congress, pp. 2303-2309. Cape Town, South Africa, August 2014.
Paper awarded Young Author Price.

P. Giselsson, Improved Dual Decomposition for Distributed Model Predictive Control. In Proceedings of the 2014 IFAC World Congress, pp. 1203-1209. Cape Town, South Africa, August 2014.
Finalist paper (out of five) for Young Author Price.

A. Lindholm, P. Giselsson, N-H. Quttineh, C. Johnsson, H. Lidestam, and K. Forsman, Production scheduling in the process industry. In The 22nd International Conference on Production Research, Iguassu Falls, Brazil, July 2013.

P. Giselsson, Optimal preconditioning and iteration complexity bounds for gradient-based optimization in model predictive control. In Proceedings of 2013 American Control Conference, pp. 358-364. Washington D.C., June 2013.
Finalist paper (out of five) for Best Student Paper Award.

P. Giselsson, A generalized distributed accelerated gradient method for distributed model predictive control with iteration complexity bounds. In Proceedings of 2013 American Control Conference, pp. 327-333. Washington D.C., June 2013.

P. Giselsson, Output feedback distributed model predictive control with inherent robustness properties. In Proceedings of 2013 American Control Conference, pp. 1691-1696. Washington D.C., June 2013.

P. Giselsson, Execution time certification for gradient-based optimization in model predictive control. In Proceedings of the 51st IEEE Conference on Decision and Control, pp. 3165-3170. Maui, HI, December 2012.

A. Lindholm and P. Giselsson, Formulating an optimization problem for minimization of losses due to utilities. In 8th IFAC International Symposium on Advanced Control of Chemical Processes. Singapore, July 2012.
Paper awarded Young Author Price.

P. Giselsson, Model predictive control in a pendulum system. In Proceedings of the 31st IASTED conference on Modelling, Identification and Control. Innsbruck, Austria, February 2011.

P. Giselsson and A. Rantzer, Distributed model predictive control with suboptimality and stability guarantees. In Proceedings of the 49th Conference on Decision and Control, pp. 7272–7277. Atlanta, GA, December 2010.

P. Giselsson, Adaptive nonlinear model predictive control with suboptimality and stability guarantees. In Proceedings of the 49th Conference on Decision and Control, pp. 3644–3649. Atlanta, GA, December 2010.

P. M. Torreblanca, P. Giselsson, and A. Rantzer, Distributed receding horizon Kalman filter. In Proceedings of the 49th Conference on Decision and Control, pp. 5068–5073. Atlanta, GA, December 2010.

P. Giselsson, J. Åkesson, and A. Robertsson, Optimization of a pendulum system using Optimica and Modelica. In Proceedings of the 7th International Modelica Conference 2009, pp. 480–489. Como, Italy, September 2009.

Lecture notes

Emil Bjornson and Pontus Giselsson, Two Applications of Deep Learning in the Physical Layer of Communication Systems. IEEE Signal Processing Magazine,http://www.lth.se/typo3/0 37. 2020.

PhD Thesis

P. Giselsson, Gradient-Based Distributed Model Predictive Control. Ph.D. Thesis ISRN LUTFD2/TFRT--1094--SE, Department of Automatic Control, Lund University, Sweden, November 2012.

Book Chapters

P. Giselsson and A. Rantzer, Generalized accelerated gradient methods for DMPC based on dual decomposition. In R. R. Negenborn and J. M. Maestre, editors, Distributed MPC made easy, pp. 309-325. Springer Netherlands, 2013.

Technical Reports

P. Giselsson, Gradient-based model predictive control in a pendulum system. Technical Report ISRN LUTFD2/TFRT--7624--SE, Department of Automatic Control, LTH, Lund University, Sweden, 2012.

Other reports

P. Giselsson, Improving Fast Dual Ascent for MPC - Part I: The Distributed Case.

P. Giselsson, Improving Fast Dual Ascent for MPC - Part II: The Embedded Case.

Master Thesis

P. Giselsson, Modeling and Control of a 1.45 m deformable mirror. Master's Thesis ISRN LUTFD2/TFRT--5775--SE, Department of Automatic Control, Lund University, Sweden, October 2006.