publications.jemdoc

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== Panos Patrinos -- Publications
[https://www.esat.kuleuven.be/english/overview Department of Electrical Engineering (ESAT)], [https://www.kuleuven.be/english/ KU Leuven]

=== 2022
- [https://arxiv.org/abs/1906.10053 Block-coordinate and incremental aggregated nonconvex proximal gradient methods: a unified view]\n
	Mathematical Programming, vol. 193, pp. 195–224, 2022\n
	P. Latafat, A. Themelis, P. Patrinos
- [hhttps://arxiv.org/abs/2010.02653 QPALM: A Proximal Augmented Lagrangian Method for Nonconvex Quadratic Programs]\n 
	Mathematical Programming Computation, 2022\n
	B. Hermans, A. Themelis, P. Patrinos
- [https://arxiv.org/abs/2005.10230 Douglas-Rachford splitting and ADMM for nonconvex optimization: Accelerated and Newton-type algorithms]\n
	Computational Optimization and Applications, vol. 82, pp. 395–440, 2022\n
	A. Themelis, L. Stella, P. Patrinos
- [https://arxiv.org/abs/1809.07199 Primal-dual algorithms for multi-agent structured optimization over message-passing architectures with bounded communication delays]\n
	Optimization Methods & Software, 2022\n
	P. Latafat, P. Patrinos
- [https://arxiv.org/abs/2203.00775 Tight convergence rates of the gradient method on hypoconvex functions]\n
	T. Rotaru, F. Glineur, and P. Patrinos
- [https://ieeexplore.ieee.org/abstract/document/9746019 Learning-based resource allocation with dynamic data rate constraints]\n
	47th International Conference on Acoustics, Speech, & Signal Processing (ICASSP), 2022\n
	P. Behmandpoor, P. Patrinos, M. Moonen
- [https://arxiv.org/abs/2112.02370 Alpaqa: A matrix-free solver for nonlinear MPC and large-scale nonconvex optimization]\n
	European Control Conference (ECC), 2022\n
	P. Pas, M. Schuurmans, P. Patrinos
- [https://arxiv.org/abs/2111.08331 Learning MPC for interaction-aware autonomous driving: A game-theoretic approach]\n
	B. Evens, M. Schuurmans, P. Patrinos,
- [https://openreview.net/forum?id=2_vhkAMARk Escaping limit cycles: Global convergence for constrained nonconvex-nonconcave minimax problems]\n
	10th International Conference on Learning Representations (ICLR 2022), *spotlight*\n
	T. Pethick, P. Latafat, P. Patrinos, O. Fercoq, V. Cevher
- [https://arxiv.org/abs/2107.04395 Block Alternating Bregman Majorization Minimization with Extrapolation]\n
	SIAM Journal on Mathematics of Data Science, vol. 4, no. 1, pp. 1-25\n
	L.T.K. Hien, D.N. Phan, N. Gillis, M. Ahookhosh, P. Patrinos 

=== 2021
- [https://arxiv.org/abs/2112.08886 Conjugate dualities for relative smoothness and strong convexity under the light of generalized convexity]\n
	E. Laude, A. Themelis, P. Patrinos
- [https://arxiv.org/pdf/2011.12659.pdf Unsupervised learning of disentangled representations in deep restricted kernel machines with orthogonality constraints]\n
	Neural Networks\n
	F. Tonin, P. Patrinos, J.A.K. Suykens
- [https://arxiv.org/abs/2103.03006 Data-driven distributionally robust MPC for constrained stochastic systems]\n
	IEEE Control Systems Letters\n
	P. Coppens, P. Patrinos
- [https://arxiv.org/pdf/2003.03963.pdf A block inertial Bregman proximal algorithm for nonsmooth nonconvex problems with application to symmetric nonnegative matrix tri-factorization]\n
	Journal of Optimization Theory and Applications\n
	M. Ahookhosh, L.T.K. Hien, N. Gillis and P. Patrinos
- [https://arxiv.org/pdf/1908.01402.pdf Multi-block Bregman proximal alternating linearized minimization and its application to sparse orthogonal nonnegative matrix factorization]\n
	Computational Optimization and Applications\n
	M. Ahookhosh, L.T.K. Hien, N. Gillis, P. Patrinos
- [https://arxiv.org/pdf/1905.11904.pdf Bregman forward-backward splitting for nonconvex composite optimization: superlinear convergence to nonisolated critical points]\n
	SIAM Journal on Optimization, vol. 31, no. 1, pp. 653-685\n
	M. Ahookhosh, A. Themelis, P. Patrinos
- [https://www.sciencedirect.com/science/article/pii/S0005109821000200 A penalty method for nonlinear programs with set exclusion constraints]\n
	Automatica, vol. 127, no. 109500\n
	B. Hermans, G. Pipeleers, P. Patrinos
- [https://arxiv.org/abs/2102.10312 Bregman Finito/MISO for nonconvex regularized finite sum minimization without Lipschitz gradient continuity]\n
	P. Latafat, A. Themelis, M. Ahookhosh, P. Patrinos 
- [https://arxiv.org/abs/2106.00561 A General Framework for Learning-Based Distributionally Robust MPC of Markov Jump Systems]\n
	M. Schuurmans, P. Patrinos
- [https://arxiv.org/abs/2105.02511 Data-driven distributionally robust control of partially observable jump linear systems]\n
	60th IEEE Conference on Decision and Control (CDC), 2021\n
	M. Schuurmans, P. Patrinos	
- [https://arxiv.org/abs/2103.14343 Neural network training as an optimal control problem: An augmented Lagrangian approach]\n
	60th IEEE Conference on Decision and Control (CDC), 2021\n
	B. Evens, P. Latafat, A. Themelis, J. Suykens, P. Patrinos 
- [https://arxiv.org/abs/2103.08533 Lasry-Lions envelopes and nonconvex optimization: A homotopy approach]\n
	29th European Signal Processing Conference (EUSIPCO), 2021\n
	M. Simoes, A. Themelis, P. Patrinos
- [https://arxiv.org/abs/2102.08443 Unsupervised energy-based out-of-distribution detection using Stiefel-restricted kernel machine]\n
	International Joint Conference on Neural Networks (IJCNN), 2021\n
	F. Tonin, A. Pandey, P. Patrinos, J. Suykens

=== 2020
- [https://www.biorxiv.org/content/10.1101/2020.10.02.323501v1.full.pdf Optimal versus approximate channel selection methods for EEG decoding with application to topology-constrained neuro-sensor networks]\n
	IEEE in Transactions on Neural Systems & Rehabilitation Engineering\n
	A.M. Narayanan, P. Patrinos, A. Bertrand
- [https://arxiv.org/pdf/2009.04422 Learning-Based Distributionally Robust Model Predictive Control of Markovian Switching Systems with Guaranteed Stability and Recursive Feasibility]\n
	59th IEEE Conference on Decision and Control (CDC), 2020\n
	M. Schuurmans, P. Patrinos
- [https://arxiv.org/abs/1903.01818 Inertial Block Mirror Descent Method for Non-Convex Non-Smooth Optimization]\n
	37th International Conference on Machine Learning (ICML)\n
	L.T.K Hien, N. Gillis, P. Patrinos
- [https://arxiv.org/pdf/2004.00083.pdf A new envelope function for nonsmooth DC optimization]\n
	59th IEEE Conference on Decision and Control (CDC), 2020\n
	A. Themelis, B. Hermans and P. Patrinos
- [ftp://ftp.esat.kuleuven.be/pub/SISTA/pcoppens/CDC_2020/cdc2020pcoppens.pdf Sample complexity of data-driven stochastic LQR with multiplicative uncertainty]\n
	59th IEEE Conference on Decision and Control (CDC), 2020\n
	P. Coppens and P. Patrinos
- [https://arxiv.org/pdf/2003.03502.pdf A quadratically convergent proximal algorithm for nonnegative tensor decomposition]\n
	28th European Signal Processing Conference (EUSIPCO)\n
	N. Vervliet, A. Themelis, P. Patrinos, and L. De Lathauwer
- [https://ieeexplore.ieee.org/document/9242265 Multi-pattern recognition through maximization of signal-to-peak-interference ratio with application to neural spike sorting]\n
	IEEE Transactions on Signal Processing, vol. 68, pp. 6240-6254, 2020\n
	J. Wouters, P. Patrinos, F. Kloosterman and A. Bertrand
- [https://arxiv.org/pdf/1912.09990.pdf Data-driven distributionally robust LQR with multiplicative noise]\n
	2nd Learning for Decision & Control (L4DC) Conference, UC Berkeley, CA\n
	P. Coppens, M. Schuurmans, P. Patrinos
- [https://arxiv.org/abs/1709.05747 Douglas-Rachford splitting and ADMM for nonconvex optimization: tight convergence results]\n
	SIAM Journal on Optimization,vol. 30, no. 1, pp. 149–181, 2020\n
	A. Themelis, P. Patrinos
- [https://ieeexplore.ieee.org/abstract/document/8962239 On the convexity of bit depth allocation for linear MMSE estimation in wireless sensor networks]\n
	IEEE Signal Processing Letters, vol. 27, pp. 291-295, 2020\n
	F. de la Hucha Arce, P. Patrinos, M. Verhelst, A. Bertrand
- [https://onlinelibrary.wiley.com/doi/abs/10.1002/cta.2737 Microsecond nonlinear model predictive control for DC-DC converters]\n
	International Journal Of Circuit Theory And Applications, vol. 48, no. 3, pp. 406-419, 2020\n
	A. Lekic, B. Hermans, N. Jovicic, P. Patrinos
- [https://arxiv.org/pdf/2003.00292.pdf OpEn: Code generation for embedded nonconvex optimization]\n
	21st IFAC World Congress, 2020\n
	P. Sopasakis, E. Fresk, and P. Patrinos
- [https://arxiv.org/pdf/2005.02646.pdf Learning-based distributionally robust model predictive control for adaptive cruise control with stochastic driver models]\n
	21st IFAC World Congress, 2020\n
	M. Schuurmans, A. Katriniok, H. Tseng, P. Patrinos
- [https://limo.libis.be/primo-explore/fulldisplay?docid=LIRIAS3004985&context=L&vid=Lirias&search_scope=Lirias&tab=default_tab&lang=en_US&fromSitemap=1 A new heuristic approach for low-thrust spacecraft trajectory optimization]\n
	21st IFAC World Congress, 2020\n
	P. Coppens, B. Hermans, J. Vandersteen, G. Pipeleers, and P. Patrinos

=== 2019
- [https://arxiv.org/pdf/1911.02934.pdf QPALM: A Newton-type proximal augmented Lagrangian method for quadratic programs]\n
	in 58th IEEE Conference on Decision and Control (CDC), Nice, France, 2019\n
	B. Hermans, A. Themelis, P. Patrinos
- [https://arxiv.org/abs/1809.06062 Risk-averse model predictive operation control of islanded microgrids]\n
	IEEE Transactions on Control Systems Technology\n
	C.A. Hans, P. Sopasakis, J. Raisch, C. Reincke-Collon, P. Patrinos
- [https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=8756205 Optimal dynamic spectrum management algorithms for multi-user full-duplex DSL]\n
	IEEE Access, vol. 7, pp. 106600-106616, 2019\n
	J Verdyck, W Lanneer, P Tsiaflakis, W Coomans, P Patrinos, M Moonen
- [https://arxiv.org/abs/1903.12091 Nonlinear model predictive control for distributed motion planning in road intersections using PANOC]\n
 	in 58th IEEE Conference on Decision and Control (CDC), Nice, France, 2019\n
	A. Katriniok, M. Schuurmans, P. Sopasakis, P. Patrinos
- [https://arxiv.org/abs/1903.10040 Safe Learning-Based Control of Stochastic Jump Linear Systems: a Distributionally Robust Approach]\n
 	in 58th IEEE Conference on Decision and Control (CDC), Nice, France, 2019\n
	M. Schuurmans, P. Sopasakis, P. Patrinos
- [https://arxiv.org/abs/1609.06955 SuperMann: a superlinearly convergent algorithm for finding fixed points of nonexpansive operators]\n
	IEEE Transactions on Automatic Control\n
	A. Themelis, P. Patrinos
- [https://arxiv.org/abs/1706.02882 A new randomized block-coordinate primal-dual algorithm for distributed optimization]\n
	IEEE Transactions on Automatic Control, vol. 64, no. 10, pp. 4050–4065, 2019\n
	P. Latafat, N. Freris, and P. Patrinos
- [https://arxiv.org/abs/1803.05256 Newton-type alternating minimization algorithm for convex optimization]\n
	IEEE Transactions on Automatic Control, vol. 64, no. 2, pp. 697-711, 2019\n
	L. Stella, A. Themelis, P. Patrinos
- [https://arxiv.org/abs/1704.00342 Risk-averse model predictive control]\n
	Automatica, vol. 100, pp. 281-289, 2019\n
	P. Sopasakis, D. Herceg, A. Bemporad, P. Patrinos
- [https://arxiv.org/abs/1812.04755 Aerial navigation in obstructed environments with embedded nonlinear model predictive control]\n
	in European Control Conference (ECC), Naples, Italy, 2019\n
	E. Small, P. Sopasakis, E. Fresk, P. Patrinos, G. Nikolakopoulos
- [https://arxiv.org/abs/1903.06477 SuperSCS: fast and accurate large-scale conic optimization]\n
 	in European Control Conference (ECC), Naples, Italy, 2019\n
	P. Sopasakis, K. Menounou, and P. Patrinos
- [https://arxiv.org/abs/1903.06749 Risk-averse risk-constrained optimal control]\n 
	in European Control Conference (ECC), Naples, Italy, 2019\n
	P. Sopasakis, M. Schuurmans, and P. Patrinos


=== 2018
- [https://arxiv.org/abs/1809.07199 Multi-agent structured optimization over message-passing architectures with bounded communication delays]\n
	in 57th IEEE Conference on Decision and Control (CDC), Miami Beach, FL, USA, 2018\n
	P. Latafat, P. Patrinos
- [https://epubs.siam.org/doi/abs/10.1137/16M1080240 Forward-backward envelope for the sum of two nonconvex functions: Further properties and nonmonotone line-search algorithms]\n
	SIAM Journal on Optimization, vol. 28, no.3, pp. 2274–2303\n
	A. Themelis, L. Stella, and P. Patrinos
- [https://arxiv.org/abs/1803.01621 Proximal Gradient Algorithms: Applications in Signal Processing]\n
	N. Antonello, L. Stella, P. Patrinos, T. van Waterschoot
- [https://arxiv.org/abs/1805.02524 A penalty method based approach for autonomous navigation using nonlinear model predictive control]\n
	6th IFAC Conference on Nonlinear Model Predictive Control, 2018, pp. 234 - 240\n
	B. Hermans, P. Patrinos, G. Pipeleers
- [https://lirias.kuleuven.be/bitstream/123456789/617689/1/main.pdf Embedded nonlinear model predictive control for obstacle avoidance using PANOC]\n
	European Control Conference, 2018, pp. 1523 - 1528\n
	A. Sathya, P. Sopasakis, A. Themelis, R. Van Parys, G. Pipeleers, P. Patrinos
- [http://ieeexplore.ieee.org/document/7883824/ GPU-accelerated stochastic predictive control of drinking water networks]\n
	IEEE Control Systems Technology, vol. 26, no. 3, pp. 551-562, 2018\n
	A. Sampathirao, P. Sopasakis, A. Bemporad, P. Patrinos
- [https://www.sciencedirect.com/science/article/pii/S136481521730539X Uncertainty-aware demand management of water distribution networks in deregulated energy markets]\n
	Environmental Modelling & Software, vol. 101, pp. 10–22, 2018\n
	P. Sopasakis, A. Sampathirao, A. Bemporad, P. Patrinos
- [ftp://ftp.esat.kuleuven.ac.be/pub/pub/stadius/platafat/18-28.pdf Plug and Play Distributed Model Predictive Control with Dynamic Coupling: A Randomized Primal-dual Proximal Algorithm]\n
	ECC 2018\n
	P. Latafat, A. Bemporad, P. Patrinos
- [https://link.springer.com/article/10.1007/s10614-016-9628-6 A semi-parametric non-linear neural network filter: Theory and empirical evidence]\n
	Computational Economics, vol. 51(3), pp. 637-675, 2018\n 
	P. Michaelides, E. Tsionas, A. Vouldis, K. Konstantakis, P. Patrinos
- [https://www.springer.com/us/book/9783319974774 Primal-Dual Proximal Algorithms for Structured Convex Optimization: A Unifying Framework]\n
	in Large-Scale and Distributed Optimization, Eds. P. Giselsson, A. Rantzer\n
	P. Latafat, P. Patrinos
- [https://arxiv.org/pdf/1811.02935.pdf On the Acceleration of Forward-Backward Splitting via an Inexact Newton Method]\n
	A. Themelis, M. Ahookhosh, P. Patrinos

=== 2017
- [https://link.springer.com/article/10.1007/s10589-017-9909-6 Asymmetric forward-backward-adjoint splitting for solving monotone inclusions involving three operators]\n
	Computational Optimization & Applications, vol. 68, no. 1, pp. 57–93, 2017\n
	P. Latafat and P. Patrinos
- [https://link.springer.com/article/10.1007/s10589-017-9912-y Forward-backward quasi-Newton methods for nonsmooth optimization problems]\n
	Computational Optimization & Applications, vol. 67, no. 3, pp. 443–487, 2017\n
	L. Stella, A. Themelis, and P. Patrinos
- [http://ieeexplore.ieee.org/abstract/document/7926929/ Multidisciplinary learning through implementation of the DVB-S2 standard]\n
	IEEE Communications Magazine, vol. 55, no. 5, pp. 124–130, 2017\n
	Y. Murillo, B. Van den Bergh, J. Beysens, A. Bertrand, W. Dehaene, P. Patrinos, T. Tuytelaars, R. V. Sabariego, M. Verhelst, P. Wambacq, S. Pollin
- [https://arxiv.org/abs/1709.06487 A simple and efficient optimization algorithm for nonlinear MPC]\n
	2017 IEEE 56th Annual Conference on Decision and Control (CDC), pp. 1939-1944, 2017\n
	L. Stella, A. Themelis, P. Sopasakis, P. Patrinos
- [http://ieeexplore.ieee.org/abstract/document/8081371/ A primal-dual line search method and applications in image processing]\n
	25th European Signal Processing Conference (EUSIPCO), pp. 1100–1104, 2017\n
	P. Sopasakis, A. Themelis, J. Suykens, and P. Patrinos
- [http://ieeexplore.ieee.org/abstract/document/7984308/ Data-driven modelling, learning and stochastic predictive control for the steel industry]\n
	25th Mediterranean Conference on Control and Automation, pp. 1361–1366, 2017\n
	D. Herceg, G. Georgoulas, P. Sopasakis, M. Castano, P. Patrinos, A. Bemporad, J. Niemi, G. Nikolakopoulos
- [https://www.sciencedirect.com/science/article/pii/S2405896317319146 Proximal limited-memory quasi-Newton methods for scenario-based stochastic optimal control]\n
	20th IFAC World Congress, Toulouse, France, pp. 11865–11870, 2017\n
	A. Sampathirao, P. Sopasakis, A. Bemporad, P. Patrinos
- [https://www.sciencedirect.com/science/article/pii/S2405896317300733 Stochastic economic model predictive control for Markovian switching systems]\n
	20th IFAC World Congress, Toulouse, France, pp. 524–530, 2017\n
	P. Sopasakis, D. Herceg, P. Patrinos, A. Bemporad
- [https://www.sciencedirect.com/science/article/pii/S2405896317328471 Modeling and administration scheduling of fractional-order pharmacokinetic systems]\n
	20th IFAC World Congress, Toulouse, France, pp. 9742–9747, 2017\n
	D. Herceg, S. Ntouskas, P. Sopasakis, A. Dokoumetzidis, P. Macheras, H. Sarimveis, P. Patrinos

=== 2016
- [http://onlinelibrary.wiley.com/doi/10.1002/rnc.3507/full Real‐time model predictive control based on dual gradient projection: Theory and fixed‐point FPGA implementation]\n
International Journal of Robust and Nonlinear Control, vol. 26, no. 15, pp. 3292–3310, 2016\n
M. Rubagotti, P. Patrinos, A. Guiggiani, A. Bemporad
- [http://ieeexplore.ieee.org/abstract/document/7798551/ New primal-dual proximal algorithm for distributed optimization]\n
IEEE 55th Conference on Decision and Control (CDC), Las Vegas, USA, pp. 1959-1964, 2016\n
P. Latafat, L. Stella, P. Patrinos
- [http://ieeexplore.ieee.org/abstract/document/7852360/ Distributed computing over encrypted data]\n
54th Annual Allerton Conference on Communication, Control, and Computing, pp. 1116–1122, 2016\n
N. Freris, P. Patrinos
- [http://ieeexplore.ieee.org/abstract/document/7810279/ Stochastic gradient methods for stochastic model predictive control]\n
European Control Conference (ECC), pp. 154–159, 2016\n
A. Themelis, V. Silvia, P. Patrinos, A. Bemporad
- [http://ieeexplore.ieee.org/abstract/document/7760414/ Accelerated reconstruction of a compressively sampled data stream]\n
24th European Signal Processing Conference (EUSIPCO), pp. 1078–1082, 2016\n
P. Sopasakis, N. Freris, P. Patrinos

=== 2015
- [http://ieeexplore.ieee.org/abstract/document/7403352/ Distributed solution of stochastic optimal control problems on GPUs]\n
IEEE 54th Conference on Decision and Control, pp. 7183–7188, 2015\n
A. K. Sampathirao, P. Sopasakis, A. Bemporad, P. Patrinos
- [http://ieeexplore.ieee.org/abstract/document/6985596/ Model predictive control for linear impulsive systems]\n
IEEE Transactions on Automatic Control, vol. 60, no. 8, pp. 2277–2282, 2015\n
P. Sopasakis, P. Patrinos, H. Sarimveis, A. Bemporad
- [http://ieeexplore.ieee.org/abstract/document/7330731/ Constrained model predictive control of spacecraft attitude with reaction wheels desaturation]\n
European Control Conference (ECC), pp. 1382–1387, 2015\n
A. Guiggiani, I. Kolmanovsky, P. Patrinos, A. Bemporad
- [http://ieeexplore.ieee.org/abstract/document/7171078/ Fixed-point constrained model predictive control of spacecraft attitude]\n
American Control Conference (ACC), 2015, pp. 2317– 2322, 2015\n
A. Guiggiani, I. Kolmanovsky, P. Patrinos, A. Bemporad
- [https://www.sciencedirect.com/science/article/pii/S0005109815001065 A dual gradient-projection algorithm for model predictive control in fixed-point arithmetic]\n
Automatica, vol. 55, pp. 226–235, 2015\n
P. Patrinos, A. Guiggiani, A. Bemporad
- [https://arxiv.org/abs/1502.07974 A convex feasibility approach to anytime model predictive control]\n
A. Bemporad, D. Bernardini, P. Patrinos\n

=== 2014
- [http://ieeexplore.ieee.org/abstract/document/7040049/ Douglas-Rachford splitting: Complexity estimates and accelerated variants]\n
IEEE 53rd Annual Conference on Decision and Control (CDC), pp. 4234– 4239, 2014\n
P. Patrinos, L. Stella, A. Bemporad
- [http://ieeexplore.ieee.org/abstract/document/7040052/ A proximal alternating minimization method for $\ell_0$-regularized nonlinear optimization problems: application to state estimation]\n
53rd IEEE Conference on Decision and Control (CDC), pp. 4254–4259, 2014\n
A. Patrascu, I. Necoara, P. Patrinos
- [https://www.sciencedirect.com/science/article/pii/S0169260714002107 Robust model predictive control for optimal continuous drug administration]\n
Computer methods and programs in biomedicine, vol. 116, no. 3, pp. 193–204, 2014\n
P. Sopasakis, P. Patrinos, H. Sarimveis
- [https://www.sciencedirect.com/science/article/pii/S0005109814003471 Stochastic model predictive control for constrained discrete-time Markovian switching systems]\n
Automatica, vol. 50, no. 10, pp. 2504–2514, 2014\n
P. Patrinos, P. Sopasakis, H. Sarimveis, and A. Bemporad
- [http://ieeexplore.ieee.org/abstract/document/6725631/ Stabilizing linear model predictive control under inexact numerical optimization]\n
IEEE Transactions on Automatic Control, vol. 59, no. 6, pp. 1660–1666, 2014\n
M. Rubagotti, P. Patrinos, and A. Bemporad
- [http://ieeexplore.ieee.org/abstract/document/6632877/ MPC for sampled-data linear systems: Guaranteeing constraint satisfaction in continuous-time]\n
IEEE Transactions on Automatic Control, vol. 59, no. 4, pp. 1088–1093, 2014\n
P. Sopasakis, P. Patrinos, and H. Sarimveis
- [https://arxiv.org/abs/1402.6655 Forward-backward truncated Newton methods for convex composite optimization]\n
P. Patrinos, L. Stella, A. Bemporad.
- [https://www.sciencedirect.com/science/article/pii/S1474667016433872 MPC for power systems dispatch based on stochastic optimization]\n
19th IFAC World Congress, pp. 11147–11152, 2014\n
I. Necoara, D. N. Clipici, P. Patrinos, A. Bemporad
- [https://www.sciencedirect.com/science/article/pii/S1474667016420549 Fixed-point implementation of a proximal Newton method for embedded model predictive control]\n
19th IFAC World Congress, pp. 2921–2926, 2014\n
A. Guiggiani, P. Patrinos, A. Bemporad
- [http://ieeexplore.ieee.org/abstract/document/6571216/ An accelerated dual gradient-projection algorithm for embedded linear model predictive control]\n
IEEE Transactions on Automatic Control, vol. 59, no. 1, pp. 18–33, 2014\n
P. Patrinos, A. Bemporad

=== 2013
- [http://ieeexplore.ieee.org/abstract/document/6760233/ Proximal Newton methods for convex composite optimization]\n
IEEE 52nd Conference on Decision and Control, pp. 2358–2363, 2013\n	
P. Patrinos, A. Bemporad\n
- [http://ieeexplore.ieee.org/abstract/document/6669412/ Fixed-point dual gradient projection for embedded model predictive control]\n
European Control Conference (ECC), pp. 3602–3607, 2013\n
P. Patrinos, A. Guiggiani, A. Bemporad
- [http://ieeexplore.ieee.org/abstract/document/6669435/ Stabilizing embedded MPC with computational complexity guarantees]\n
European Control Conference (ECC), pp. 3065–3070, 2013\n
M. Rubagotti, P. Patrinos, A. Bemporad
- [http://ieeexplore.ieee.org/abstract/document/6607416/ Reliability and efficiency for market parties in power systems]\n
10th International Conference on the European Energy Market (EEM), 2013\n
L. Puglia, P. Patrinos, D. Bernardini, A. Bemporad

=== 2012
- [http://ieeexplore.ieee.org/abstract/document/6426458/ An accelerated dual gradient-projection algorithm for linear model predictive control]\n
51st Annual Conference on Decision and Control, pp. 662–667, 2012\n
Patrinos, A. Bemporad
- [https://www.sciencedirect.com/science/article/pii/S1474667016314215 Simple and certifiable quadratic programming algorithms for embedded linear model predictive control]\n
IFAC 4th Nonlinear Model Predictive Control Conference, 2012\n
A. Bemporad, P. Patrinos
- [https://www.sciencedirect.com/science/article/pii/S1474667016320614 Two-time-scale MPC for Economically Optimal Real-time Operation of Balance Responsible Parties]\n 
IFAC 8th Power Plant and Power Systems Control Symposium, (Toulouse, France), pp. 741–746, 2012\n
P. Patrinos, D. Bernardini, A. Maffei, A. Jokic, and A. Bemporad
- [http://ieeexplore.ieee.org/document/6426243/ Model predictive control for linear impulsive systems]\n
51st IEEE Conference on Decision and Control, pp. 5164–5169, 2012\n
P. Sopasakis, P. Patrinos, H. Sarimveis, A. Bemporad


=== 2011
- [https://www.sciencedirect.com/science/article/pii/S0005109811002974 A global piecewise smooth Newton method for fast large-scale model predictive control]\n
Automatica, vol. 47, no. 9, pp. 2016–2022, 2011\n
P. Patrinos, P. Sopasakis, H. Sarimveis
- [https://www.sciencedirect.com/science/article/pii/S0005109811002238 Convex parametric piecewise quadratic optimization: Theory and algorithms]\n
Automatica, vol. 47, no. 8, pp. 1770–1777, 2011\n
P. Patrinos H. Sarimveis
- [http://ieeexplore.ieee.org/abstract/document/6160798/ Stochastic MPC for real-time market-based optimal power dispatch]\n
Proc. 50th IEEE Conference on Decision and Control, pp. 7111–7116, 2011\n
P. Patrinos, S. Trimboli, and A. Bemporad
- [https://www.sciencedirect.com/science/article/pii/B9780444542984500775 Physiologically based pharmacokinetic modeling and predictive control: an integrated approach for optimal drug administration]\n
21st European Symposium on Computer Aided Chemical Engineering, pp. 1490–1494, 2011\n
P. Sopasakis, S. Patrinos, P. Giannikou, H. Sarimveis
- [https://www.sciencedirect.com/science/article/pii/S1474667016456466 Stochastic model predictive control for constrained networked control systems with random time delay]\n
18th IFAC World Congress, pp. 12626–12631, 2011\n
P. Patrinos, P. Sopasakis, H. Sarimveis

=== 2010
- [https://www.sciencedirect.com/science/article/pii/S000510981000258X A new algorithm for solving convex parametric quadratic programs based on graphical derivatives of solution mappings]\n
Automatica, vol. 46, no. 9, pp. 1405–1418, 2010\n
P. Patrinos, H. Sarimveis
- [http://www.worldscientific.com/doi/abs/10.1142/S0129065710002474 Variable selection in nonlinear modeling based on RBF networks and evolutionary computation]\n
International Journal of Neural Systems, vol. 20, no. 5, pp. 365–379, 2010\n
P. Patrinos, A. Alexandridis, K. Ninos, and H. Sarimveis

=== Prior 2010
- [https://www.sciencedirect.com/science/article/pii/S0305054807000366 Dynamic modeling and control of supply chain systems: A review]\n
Computers and Operations Research, vol. 35, no. 11, pp. 3530– 3561, 2008\n
H. Sarimveis, P. Patrinos, C. Tarantilis, C. Kiranoudis
- [http://ieeexplore.ieee.org/abstract/document/7068826/ An explicit optimal control approach for mean-risk dynamic portfolio allocation]\n
9th European Control Conference (ECC), 2007\n
P. Patrinos, H. Sarimveis
- [https://www.sciencedirect.com/science/article/pii/S0260877405002402 Time series sales forecasting for short shelf-life food products based on artificial neural networks and evolutionary computing]\n
Journal of Food Engineering, vol. 75, no. 2, pp. 196–204, 2006\n
F. Doganis, A. Alexandridis, P. Patrinos, H. Sarimveis
- [https://www.sciencedirect.com/science/article/pii/S0169743904001716 A two-stage evolutionary algorithm for variable selection in the development of RBF neural network models]\n
Chemometrics and Intelligent Laboratory Systems, vol. 75, no. 2, pp. 149–162, 2005\n
A. Alexandridis, P. Patrinos, H. Sarimveis
- [https://www.sciencedirect.com/science/article/pii/S1474667016370999 An RBF based neuro-dynamic approach for the control of stochastic dynamic systems]\n
16th IFAC World Congress, (Prague, Czech Republic), pp. 1086–1086, 2005\n
P. Patrinos and H. Sarimveis
- [https://www.sciencedirect.com/science/article/pii/S157079460480110X Development of nonlinear quantitative structure-activity relationships using RBF networks and evolutionary computing]\n
14th European Symposium on Computer Aided Process Engineering-ESCAPE 14, vol. 18, pp. 265–270, 2004\n
P. Patrinos, A. Alexandridis, A. Afantitis, H. Sarimveis, and O. Igglesi-Markopoulou