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<title>Panos Patrinos – Publications</title>
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<div class="menu-item"><a href="https://github.com/JuliaFirstOrder/StructuredOptimization.jl">StructuredOptimization.jl</a></div>
<div class="menu-item"><a href="https://github.com/JuliaFirstOrder/ProximalOperators.jl">ProximalOperators.jl</a></div>
<div class="menu-item"><a href="https://github.com/JuliaFirstOrder/ProximalAlgorithms.jl">ProximalAlgorithms.jl</a></div>
<div class="menu-item"><a href="https://alphaville.github.io/optimization-engine/">OpEn</a></div>
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<h1>Panos Patrinos – Publications</h1>
<div id="subtitle"><a href="https://www.esat.kuleuven.be/english/overview">Department of Electrical Engineering (ESAT)</a>, <a href="https://www.kuleuven.be/english/">KU Leuven</a></div>
</div>
<h3>2022</h3>
<ul>
<li><p><a href="https://openreview.net/forum?id=2_vhkAMARk">Escaping limit cycles: Global convergence for constrained nonconvex-nonconcave minimax problems</a><br />
10th International Conference on Learning Representations (ICLR 2022), <b>spotlight</b></p>
</li>
<li><p><a href="https://arxiv.org/abs/2107.04395">Block Alternating Bregman Majorization Minimization with Extrapolation</a><br />
SIAM Journal on Mathematics of Data Science, vol. 4, no. 1, pp. 1-25<br />
L.T.K. Hien, D.N. Phan, N. Gillis, M. Ahookhosh, P. Patrinos </p>
</li>
</ul>
<h3>2021</h3>
<ul>
<li><p><a href="https://arxiv.org/abs/2112.08886">Conjugate dualities for relative smoothness and strong convexity under the light of generalized convexity</a><br />
E. Laude, A. Themelis, P. Patrinos</p>
</li>
<li><p><a href="https://arxiv.org/abs/2112.02370">Alpaqa: A matrix-free solver for nonlinear MPC and large-scale nonconvex optimization</a><br />
P. Pas, M. Schuurmans, P. Patrinos</p>
</li>
</ul>
<ul>
<li><p><a href="https://arxiv.org/pdf/2011.12659.pdf">Unsupervised learning of disentangled representations in deep restricted kernel machines with orthogonality constraints</a><br />
Neural Networks<br />
F. Tonin, P. Patrinos, J.A.K. Suykens</p>
</li>
<li><p><a href="https://arxiv.org/abs/2103.03006">Data-driven distributionally robust MPC for constrained stochastic systems</a><br />
IEEE Control Systems Letters<br />
P. Coppens, P. Patrinos</p>
</li>
<li><p><a href="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</a><br />
Journal of Optimization Theory and Applications<br />
M. Ahookhosh, L.T.K. Hien, N. Gillis and P. Patrinos</p>
</li>
<li><p><a href="https://arxiv.org/pdf/1908.01402.pdf">Multi-block Bregman proximal alternating linearized minimization and its application to sparse orthogonal nonnegative matrix factorization</a><br />
Computational Optimization and Applications<br />
M. Ahookhosh, L.T.K. Hien, N. Gillis, P. Patrinos</p>
</li>
<li><p><a href="https://arxiv.org/pdf/1906.10053.pdf">Block-coordinate and incremental aggregated nonconvex proximal gradient methods: a unified view</a><br />
Mathematical Programming, pp. 1-30<br />
P. Latafat, A. Themelis, P. Patrinos</p>
</li>
<li><p><a href="https://arxiv.org/pdf/1905.11904.pdf">Bregman forward-backward splitting for nonconvex composite optimization: superlinear convergence to nonisolated critical points</a><br />
SIAM Journal on Optimization, vol. 31, no. 1, pp. 653-685<br />
M. Ahookhosh, A. Themelis, P. Patrinos</p>
</li>
<li><p><a href="https://www.sciencedirect.com/science/article/pii/S0005109821000200">A penalty method for nonlinear programs with set exclusion constraints</a><br />
Automatica, vol. 127, no. 109500<br />
B. Hermans, G. Pipeleers, P. Patrinos</p>
</li>
<li><p><a href="https://arxiv.org/abs/2102.10312">Bregman Finito/MISO for nonconvex regularized finite sum minimization without Lipschitz gradient continuity</a><br />
P. Latafat, A. Themelis, M. Ahookhosh, P. Patrinos </p>
</li>
<li><p><a href="https://arxiv.org/abs/2106.00561">A General Framework for Learning-Based Distributionally Robust MPC of Markov Jump Systems</a><br />
M. Schuurmans, P. Patrinos</p>
</li>
<li><p><a href="https://arxiv.org/abs/2105.02511">Data-driven distributionally robust control of partially observable jump linear systems</a><br />
60th IEEE Conference on Decision and Control (CDC), 2021<br />
M. Schuurmans, P. Patrinos </p>
</li>
<li><p><a href="https://arxiv.org/abs/2103.14343">Neural network training as an optimal control problem: An augmented Lagrangian approach</a><br />
60th IEEE Conference on Decision and Control (CDC), 2021<br />
B. Evens, P. Latafat, A. Themelis, J. Suykens, P. Patrinos </p>
</li>
<li><p><a href="https://arxiv.org/abs/2103.08533">Lasry-Lions envelopes and nonconvex optimization: A homotopy approach</a><br />
29th European Signal Processing Conference (EUSIPCO), 2021<br />
M. Simoes, A. Themelis, P. Patrinos</p>
</li>
<li><p><a href="https://arxiv.org/abs/2102.08443">Unsupervised energy-based out-of-distribution detection using Stiefel-restricted kernel machine</a><br />
International Joint Conference on Neural Networks (IJCNN), 2021<br />
F. Tonin, A. Pandey, P. Patrinos, J. Suykens</p>
</li>
</ul>
<h3>2020</h3>
<ul>
<li><p><a href="https://arxiv.org/pdf/2010.02653.pdf">QPALM: A Proximal Augmented Lagrangian Method for Nonconvex Quadratic Programs</a><br />
B. Hermans, A. Themelis, P. Patrinos</p>
</li>
<li><p><a href="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</a><br />
IEEE in Transactions on Neural Systems & Rehabilitation Engineering<br />
A.M. Narayanan, P. Patrinos, A. Bertrand</p>
</li>
<li><p><a href="https://arxiv.org/pdf/2005.10230.pdf">Douglas-Rachford splitting and ADMM for nonconvex optimization: Accelerated and Newton-type algorithms</a><br />
A. Themelis, L. Stella, P. Patrinos</p>
</li>
<li><p><a href="https://arxiv.org/pdf/2009.04422">Learning-Based Distributionally Robust Model Predictive Control of Markovian Switching Systems with Guaranteed Stability and Recursive Feasibility</a><br />
59th IEEE Conference on Decision and Control (CDC), 2020<br />
M. Schuurmans, P. Patrinos</p>
</li>
<li><p><a href="https://arxiv.org/abs/1903.01818">Inertial Block Mirror Descent Method for Non-Convex Non-Smooth Optimization</a><br />
37th International Conference on Machine Learning (ICML)<br />
L.T.K Hien, N. Gillis, P. Patrinos</p>
</li>
<li><p><a href="https://arxiv.org/pdf/2004.00083.pdf">A new envelope function for nonsmooth DC optimization</a><br />
59th IEEE Conference on Decision and Control (CDC), 2020<br />
A. Themelis, B. Hermans and P. Patrinos</p>
</li>
<li><p><a href="ftp://ftp.esat.kuleuven.be/pub/SISTA/pcoppens/CDC_2020/cdc2020pcoppens.pdf">Sample complexity of data-driven stochastic LQR with multiplicative uncertainty</a><br />
59th IEEE Conference on Decision and Control (CDC), 2020<br />
P. Coppens and P. Patrinos</p>
</li>
<li><p><a href="https://arxiv.org/pdf/2003.03502.pdf">A quadratically convergent proximal algorithm for nonnegative tensor decomposition</a><br />
28th European Signal Processing Conference (EUSIPCO)<br />
N. Vervliet, A. Themelis, P. Patrinos, and L. De Lathauwer</p>
</li>
<li><p><a href="https://ieeexplore.ieee.org/document/9242265">Multi-pattern recognition through maximization of signal-to-peak-interference ratio with application to neural spike sorting</a><br />
IEEE Transactions on Signal Processing, vol. 68, pp. 6240-6254, 2020<br />
J. Wouters, P. Patrinos, F. Kloosterman and A. Bertrand</p>
</li>
<li><p><a href="https://arxiv.org/pdf/1912.09990.pdf">Data-driven distributionally robust LQR with multiplicative noise</a><br />
2nd Learning for Decision & Control (L4DC) Conference, UC Berkeley, CA<br />
P. Coppens, M. Schuurmans, P. Patrinos</p>
</li>
<li><p><a href="https://arxiv.org/abs/1709.05747">Douglas-Rachford splitting and ADMM for nonconvex optimization: tight convergence results</a><br />
SIAM Journal on Optimization,vol. 30, no. 1, pp. 149–181, 2020<br />
A. Themelis, P. Patrinos</p>
</li>
<li><p><a href="https://ieeexplore.ieee.org/abstract/document/8962239">On the convexity of bit depth allocation for linear MMSE estimation in wireless sensor networks</a><br />
IEEE Signal Processing Letters, vol. 27, pp. 291-295, 2020<br />
F. de la Hucha Arce, P. Patrinos, M. Verhelst, A. Bertrand</p>
</li>
<li><p><a href="https://onlinelibrary.wiley.com/doi/abs/10.1002/cta.2737">Microsecond nonlinear model predictive control for DC-DC converters</a><br />
International Journal Of Circuit Theory And Applications, vol. 48, no. 3, pp. 406-419, 2020<br />
A. Lekic, B. Hermans, N. Jovicic, P. Patrinos</p>
</li>
<li><p><a href="https://arxiv.org/pdf/2003.00292.pdf">OpEn: Code generation for embedded nonconvex optimization</a><br />
21st IFAC World Congress, 2020<br />
P. Sopasakis, E. Fresk, and P. Patrinos</p>
</li>
<li><p><a href="https://arxiv.org/pdf/2005.02646.pdf">Learning-based distributionally robust model predictive control for adaptive cruise control with stochastic driver models</a><br />
21st IFAC World Congress, 2020<br />
M. Schuurmans, A. Katriniok, H. Tseng, P. Patrinos</p>
</li>
<li><p><a href="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</a><br />
21st IFAC World Congress, 2020<br />
P. Coppens, B. Hermans, J. Vandersteen, G. Pipeleers, and P. Patrinos</p>
</li>
</ul>
<h3>2019</h3>
<ul>
<li><p><a href="https://arxiv.org/pdf/1911.02934.pdf">QPALM: A Newton-type proximal augmented Lagrangian method for quadratic programs</a><br />
in 58th IEEE Conference on Decision and Control (CDC), Nice, France, 2019<br />
B. Hermans, A. Themelis, P. Patrinos</p>
</li>
<li><p><a href="https://arxiv.org/abs/1809.06062">Risk-averse model predictive operation control of islanded microgrids</a><br />
IEEE Transactions on Control Systems Technology<br />
C.A. Hans, P. Sopasakis, J. Raisch, C. Reincke-Collon, P. Patrinos</p>
</li>
<li><p><a href="https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=8756205">Optimal dynamic spectrum management algorithms for multi-user full-duplex DSL</a><br />
IEEE Access, vol. 7, pp. 106600-106616, 2019<br />
J Verdyck, W Lanneer, P Tsiaflakis, W Coomans, P Patrinos, M Moonen</p>
</li>
<li><p><a href="https://arxiv.org/abs/1903.12091">Nonlinear model predictive control for distributed motion planning in road intersections using PANOC</a><br />
in 58th IEEE Conference on Decision and Control (CDC), Nice, France, 2019<br />
A. Katriniok, M. Schuurmans, P. Sopasakis, P. Patrinos</p>
</li>
<li><p><a href="https://arxiv.org/abs/1903.10040">Safe Learning-Based Control of Stochastic Jump Linear Systems: a Distributionally Robust Approach</a><br />
in 58th IEEE Conference on Decision and Control (CDC), Nice, France, 2019<br />
M. Schuurmans, P. Sopasakis, P. Patrinos</p>
</li>
<li><p><a href="https://arxiv.org/abs/1609.06955">SuperMann: a superlinearly convergent algorithm for finding fixed points of nonexpansive operators</a><br />
IEEE Transactions on Automatic Control<br />
A. Themelis, P. Patrinos</p>
</li>
<li><p><a href="https://arxiv.org/abs/1706.02882">A new randomized block-coordinate primal-dual algorithm for distributed optimization</a><br />
IEEE Transactions on Automatic Control, vol. 64, no. 10, pp. 4050–4065, 2019<br />
P. Latafat, N. Freris, and P. Patrinos</p>
</li>
<li><p><a href="https://arxiv.org/abs/1803.05256">Newton-type alternating minimization algorithm for convex optimization</a><br />
IEEE Transactions on Automatic Control, vol. 64, no. 2, pp. 697-711, 2019<br />
L. Stella, A. Themelis, P. Patrinos</p>
</li>
<li><p><a href="https://arxiv.org/abs/1704.00342">Risk-averse model predictive control</a><br />
Automatica, vol. 100, pp. 281-289, 2019<br />
P. Sopasakis, D. Herceg, A. Bemporad, P. Patrinos</p>
</li>
<li><p><a href="https://arxiv.org/abs/1812.04755">Aerial navigation in obstructed environments with embedded nonlinear model predictive control</a><br />
in European Control Conference (ECC), Naples, Italy, 2019<br />
E. Small, P. Sopasakis, E. Fresk, P. Patrinos, G. Nikolakopoulos</p>
</li>
<li><p><a href="https://arxiv.org/abs/1903.06477">SuperSCS: fast and accurate large-scale conic optimization</a><br />
in European Control Conference (ECC), Naples, Italy, 2019<br />
P. Sopasakis, K. Menounou, and P. Patrinos</p>
</li>
<li><p><a href="https://arxiv.org/abs/1903.06749">Risk-averse risk-constrained optimal control</a><br />
in European Control Conference (ECC), Naples, Italy, 2019<br />
P. Sopasakis, M. Schuurmans, and P. Patrinos</p>
</li>
</ul>
<h3>2018</h3>
<ul>
<li><p><a href="https://arxiv.org/abs/1809.07199">Multi-agent structured optimization over message-passing architectures with bounded communication delays</a><br />
in 57th IEEE Conference on Decision and Control (CDC), Miami Beach, FL, USA, 2018<br />
P. Latafat, P. Patrinos</p>
</li>
<li><p><a href="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</a><br />
SIAM Journal on Optimization, vol. 28, no.3, pp. 2274–2303<br />
A. Themelis, L. Stella, and P. Patrinos</p>
</li>
<li><p><a href="https://arxiv.org/abs/1803.01621">Proximal Gradient Algorithms: Applications in Signal Processing</a><br />
N. Antonello, L. Stella, P. Patrinos, T. van Waterschoot</p>
</li>
<li><p><a href="https://arxiv.org/abs/1805.02524">A penalty method based approach for autonomous navigation using nonlinear model predictive control</a><br />
6th IFAC Conference on Nonlinear Model Predictive Control, 2018, pp. 234 - 240<br />
B. Hermans, P. Patrinos, G. Pipeleers</p>
</li>
<li><p><a href="https://lirias.kuleuven.be/bitstream/123456789/617689/1/main.pdf">Embedded nonlinear model predictive control for obstacle avoidance using PANOC</a><br />
European Control Conference, 2018, pp. 1523 - 1528<br />
A. Sathya, P. Sopasakis, A. Themelis, R. Van Parys, G. Pipeleers, P. Patrinos</p>
</li>
<li><p><a href="http://ieeexplore.ieee.org/document/7883824/">GPU-accelerated stochastic predictive control of drinking water networks</a><br />
IEEE Control Systems Technology, vol. 26, no. 3, pp. 551-562, 2018<br />
A. Sampathirao, P. Sopasakis, A. Bemporad, P. Patrinos</p>
</li>
<li><p><a href="https://www.sciencedirect.com/science/article/pii/S136481521730539X">Uncertainty-aware demand management of water distribution networks in deregulated energy markets</a><br />
Environmental Modelling & Software, vol. 101, pp. 10–22, 2018<br />
P. Sopasakis, A. Sampathirao, A. Bemporad, P. Patrinos</p>
</li>
<li><p><a href="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</a><br />
ECC 2018<br />
P. Latafat, A. Bemporad, P. Patrinos</p>
</li>
<li><p><a href="https://link.springer.com/article/10.1007/s10614-016-9628-6">A semi-parametric non-linear neural network filter: Theory and empirical evidence</a><br />
Computational Economics, vol. 51(3), pp. 637-675, 2018<br />
P. Michaelides, E. Tsionas, A. Vouldis, K. Konstantakis, P. Patrinos</p>
</li>
<li><p><a href="https://www.springer.com/us/book/9783319974774">Primal-Dual Proximal Algorithms for Structured Convex Optimization: A Unifying Framework</a><br />
in Large-Scale and Distributed Optimization, Eds. P. Giselsson, A. Rantzer<br />
P. Latafat, P. Patrinos</p>
</li>
<li><p><a href="https://arxiv.org/pdf/1811.02935.pdf">On the Acceleration of Forward-Backward Splitting via an Inexact Newton Method</a><br />
A. Themelis, M. Ahookhosh, P. Patrinos</p>
</li>
</ul>
<h3>2017</h3>
<ul>
<li><p><a href="https://link.springer.com/article/10.1007/s10589-017-9909-6">Asymmetric forward-backward-adjoint splitting for solving monotone inclusions involving three operators</a><br />
Computational Optimization & Applications, vol. 68, no. 1, pp. 57–93, 2017<br />
P. Latafat and P. Patrinos</p>
</li>
<li><p><a href="https://link.springer.com/article/10.1007/s10589-017-9912-y">Forward-backward quasi-Newton methods for nonsmooth optimization problems</a><br />
Computational Optimization & Applications, vol. 67, no. 3, pp. 443–487, 2017<br />
L. Stella, A. Themelis, and P. Patrinos</p>
</li>
<li><p><a href="http://ieeexplore.ieee.org/abstract/document/7926929/">Multidisciplinary learning through implementation of the DVB-S2 standard</a><br />
IEEE Communications Magazine, vol. 55, no. 5, pp. 124–130, 2017<br />
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</p>
</li>
<li><p><a href="https://arxiv.org/abs/1709.06487">A simple and efficient optimization algorithm for nonlinear MPC</a><br />
2017 IEEE 56th Annual Conference on Decision and Control (CDC), pp. 1939-1944, 2017<br />
L. Stella, A. Themelis, P. Sopasakis, P. Patrinos</p>
</li>
<li><p><a href="http://ieeexplore.ieee.org/abstract/document/8081371/">A primal-dual line search method and applications in image processing</a><br />
25th European Signal Processing Conference (EUSIPCO), pp. 1100–1104, 2017<br />
P. Sopasakis, A. Themelis, J. Suykens, and P. Patrinos</p>
</li>
<li><p><a href="http://ieeexplore.ieee.org/abstract/document/7984308/">Data-driven modelling, learning and stochastic predictive control for the steel industry</a><br />
25th Mediterranean Conference on Control and Automation, pp. 1361–1366, 2017<br />
D. Herceg, G. Georgoulas, P. Sopasakis, M. Castano, P. Patrinos, A. Bemporad, J. Niemi, G. Nikolakopoulos</p>
</li>
<li><p><a href="https://www.sciencedirect.com/science/article/pii/S2405896317319146">Proximal limited-memory quasi-Newton methods for scenario-based stochastic optimal control</a><br />
20th IFAC World Congress, Toulouse, France, pp. 11865–11870, 2017<br />
A. Sampathirao, P. Sopasakis, A. Bemporad, P. Patrinos</p>
</li>
<li><p><a href="https://www.sciencedirect.com/science/article/pii/S2405896317300733">Stochastic economic model predictive control for Markovian switching systems</a><br />
20th IFAC World Congress, Toulouse, France, pp. 524–530, 2017<br />
P. Sopasakis, D. Herceg, P. Patrinos, A. Bemporad</p>
</li>
<li><p><a href="https://www.sciencedirect.com/science/article/pii/S2405896317328471">Modeling and administration scheduling of fractional-order pharmacokinetic systems</a><br />
20th IFAC World Congress, Toulouse, France, pp. 9742–9747, 2017<br />
D. Herceg, S. Ntouskas, P. Sopasakis, A. Dokoumetzidis, P. Macheras, H. Sarimveis, P. Patrinos</p>
</li>
</ul>
<h3>2016</h3>
<ul>
<li><p><a href="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</a><br />
International Journal of Robust and Nonlinear Control, vol. 26, no. 15, pp. 3292–3310, 2016<br />
M. Rubagotti, P. Patrinos, A. Guiggiani, A. Bemporad</p>
</li>
<li><p><a href="http://ieeexplore.ieee.org/abstract/document/7798551/">New primal-dual proximal algorithm for distributed optimization</a><br />
IEEE 55th Conference on Decision and Control (CDC), Las Vegas, USA, pp. 1959-1964, 2016<br />
P. Latafat, L. Stella, P. Patrinos</p>
</li>
<li><p><a href="http://ieeexplore.ieee.org/abstract/document/7852360/">Distributed computing over encrypted data</a><br />
54th Annual Allerton Conference on Communication, Control, and Computing, pp. 1116–1122, 2016<br />
N. Freris, P. Patrinos</p>
</li>
<li><p><a href="http://ieeexplore.ieee.org/abstract/document/7810279/">Stochastic gradient methods for stochastic model predictive control</a><br />
European Control Conference (ECC), pp. 154–159, 2016<br />
A. Themelis, V. Silvia, P. Patrinos, A. Bemporad</p>
</li>
<li><p><a href="http://ieeexplore.ieee.org/abstract/document/7760414/">Accelerated reconstruction of a compressively sampled data stream</a><br />
24th European Signal Processing Conference (EUSIPCO), pp. 1078–1082, 2016<br />
P. Sopasakis, N. Freris, P. Patrinos</p>
</li>
</ul>
<h3>2015</h3>
<ul>
<li><p><a href="http://ieeexplore.ieee.org/abstract/document/7403352/">Distributed solution of stochastic optimal control problems on GPUs</a><br />
IEEE 54th Conference on Decision and Control, pp. 7183–7188, 2015<br />
A. K. Sampathirao, P. Sopasakis, A. Bemporad, P. Patrinos</p>
</li>
<li><p><a href="http://ieeexplore.ieee.org/abstract/document/6985596/">Model predictive control for linear impulsive systems</a><br />
IEEE Transactions on Automatic Control, vol. 60, no. 8, pp. 2277–2282, 2015<br />
P. Sopasakis, P. Patrinos, H. Sarimveis, A. Bemporad</p>
</li>
<li><p><a href="http://ieeexplore.ieee.org/abstract/document/7330731/">Constrained model predictive control of spacecraft attitude with reaction wheels desaturation</a><br />
European Control Conference (ECC), pp. 1382–1387, 2015<br />
A. Guiggiani, I. Kolmanovsky, P. Patrinos, A. Bemporad</p>
</li>
<li><p><a href="http://ieeexplore.ieee.org/abstract/document/7171078/">Fixed-point constrained model predictive control of spacecraft attitude</a><br />
American Control Conference (ACC), 2015, pp. 2317– 2322, 2015<br />
A. Guiggiani, I. Kolmanovsky, P. Patrinos, A. Bemporad</p>
</li>
<li><p><a href="https://www.sciencedirect.com/science/article/pii/S0005109815001065">A dual gradient-projection algorithm for model predictive control in fixed-point arithmetic</a><br />
Automatica, vol. 55, pp. 226–235, 2015<br />
P. Patrinos, A. Guiggiani, A. Bemporad</p>
</li>
<li><p><a href="https://arxiv.org/abs/1502.07974">A convex feasibility approach to anytime model predictive control</a><br />
A. Bemporad, D. Bernardini, P. Patrinos<br /></p>
</li>
</ul>
<h3>2014</h3>
<ul>
<li><p><a href="http://ieeexplore.ieee.org/abstract/document/7040049/">Douglas-Rachford splitting: Complexity estimates and accelerated variants</a><br />
IEEE 53rd Annual Conference on Decision and Control (CDC), pp. 4234– 4239, 2014<br />
P. Patrinos, L. Stella, A. Bemporad</p>
</li>
<li><p><a href="http://ieeexplore.ieee.org/abstract/document/7040052/">A proximal alternating minimization method for <img class="eq" src="eqs/2349274112114421518-130.png" alt="ell_0" style="vertical-align: -4px" />-regularized nonlinear optimization problems: application to state estimation</a><br />
53rd IEEE Conference on Decision and Control (CDC), pp. 4254–4259, 2014<br />
A. Patrascu, I. Necoara, P. Patrinos</p>
</li>
<li><p><a href="https://www.sciencedirect.com/science/article/pii/S0169260714002107">Robust model predictive control for optimal continuous drug administration</a><br />
Computer methods and programs in biomedicine, vol. 116, no. 3, pp. 193–204, 2014<br />
P. Sopasakis, P. Patrinos, H. Sarimveis</p>
</li>
<li><p><a href="https://www.sciencedirect.com/science/article/pii/S0005109814003471">Stochastic model predictive control for constrained discrete-time Markovian switching systems</a><br />
Automatica, vol. 50, no. 10, pp. 2504–2514, 2014<br />
P. Patrinos, P. Sopasakis, H. Sarimveis, and A. Bemporad</p>
</li>
<li><p><a href="http://ieeexplore.ieee.org/abstract/document/6725631/">Stabilizing linear model predictive control under inexact numerical optimization</a><br />
IEEE Transactions on Automatic Control, vol. 59, no. 6, pp. 1660–1666, 2014<br />
M. Rubagotti, P. Patrinos, and A. Bemporad</p>
</li>
<li><p><a href="http://ieeexplore.ieee.org/abstract/document/6632877/">MPC for sampled-data linear systems: Guaranteeing constraint satisfaction in continuous-time</a><br />
IEEE Transactions on Automatic Control, vol. 59, no. 4, pp. 1088–1093, 2014<br />
P. Sopasakis, P. Patrinos, and H. Sarimveis</p>
</li>
<li><p><a href="https://arxiv.org/abs/1402.6655">Forward-backward truncated Newton methods for convex composite optimization</a><br />
P. Patrinos, L. Stella, A. Bemporad.</p>
</li>
<li><p><a href="https://www.sciencedirect.com/science/article/pii/S1474667016433872">MPC for power systems dispatch based on stochastic optimization</a><br />
19th IFAC World Congress, pp. 11147–11152, 2014<br />
I. Necoara, D. N. Clipici, P. Patrinos, A. Bemporad</p>
</li>
<li><p><a href="https://www.sciencedirect.com/science/article/pii/S1474667016420549">Fixed-point implementation of a proximal Newton method for embedded model predictive control</a><br />
19th IFAC World Congress, pp. 2921–2926, 2014<br />
A. Guiggiani, P. Patrinos, A. Bemporad</p>
</li>
<li><p><a href="http://ieeexplore.ieee.org/abstract/document/6571216/">An accelerated dual gradient-projection algorithm for embedded linear model predictive control</a><br />
IEEE Transactions on Automatic Control, vol. 59, no. 1, pp. 18–33, 2014<br />
P. Patrinos, A. Bemporad</p>
</li>
</ul>
<h3>2013</h3>
<ul>
<li><p><a href="http://ieeexplore.ieee.org/abstract/document/6760233/">Proximal Newton methods for convex composite optimization</a><br />
IEEE 52nd Conference on Decision and Control, pp. 2358–2363, 2013<br />
P. Patrinos, A. Bemporad<br /></p>
</li>
<li><p><a href="http://ieeexplore.ieee.org/abstract/document/6669412/">Fixed-point dual gradient projection for embedded model predictive control</a><br />
European Control Conference (ECC), pp. 3602–3607, 2013<br />
P. Patrinos, A. Guiggiani, A. Bemporad</p>
</li>
<li><p><a href="http://ieeexplore.ieee.org/abstract/document/6669435/">Stabilizing embedded MPC with computational complexity guarantees</a><br />
European Control Conference (ECC), pp. 3065–3070, 2013<br />
M. Rubagotti, P. Patrinos, A. Bemporad</p>
</li>
<li><p><a href="http://ieeexplore.ieee.org/abstract/document/6607416/">Reliability and efficiency for market parties in power systems</a><br />
10th International Conference on the European Energy Market (EEM), 2013<br />
L. Puglia, P. Patrinos, D. Bernardini, A. Bemporad</p>
</li>
</ul>
<h3>2012</h3>
<ul>
<li><p><a href="http://ieeexplore.ieee.org/abstract/document/6426458/">An accelerated dual gradient-projection algorithm for linear model predictive control</a><br />
51st Annual Conference on Decision and Control, pp. 662–667, 2012<br />
Patrinos, A. Bemporad</p>
</li>
<li><p><a href="https://www.sciencedirect.com/science/article/pii/S1474667016314215">Simple and certifiable quadratic programming algorithms for embedded linear model predictive control</a><br />
IFAC 4th Nonlinear Model Predictive Control Conference, 2012<br />
A. Bemporad, P. Patrinos</p>
</li>
<li><p><a href="https://www.sciencedirect.com/science/article/pii/S1474667016320614">Two-time-scale MPC for Economically Optimal Real-time Operation of Balance Responsible Parties</a><br />
IFAC 8th Power Plant and Power Systems Control Symposium, (Toulouse, France), pp. 741–746, 2012<br />
P. Patrinos, D. Bernardini, A. Maffei, A. Jokic, and A. Bemporad</p>
</li>
<li><p><a href="http://ieeexplore.ieee.org/document/6426243/">Model predictive control for linear impulsive systems</a><br />
51st IEEE Conference on Decision and Control, pp. 5164–5169, 2012<br />
P. Sopasakis, P. Patrinos, H. Sarimveis, A. Bemporad</p>
</li>
</ul>
<h3>2011</h3>
<ul>
<li><p><a href="https://www.sciencedirect.com/science/article/pii/S0005109811002974">A global piecewise smooth Newton method for fast large-scale model predictive control</a><br />
Automatica, vol. 47, no. 9, pp. 2016–2022, 2011<br />
P. Patrinos, P. Sopasakis, H. Sarimveis</p>
</li>
<li><p><a href="https://www.sciencedirect.com/science/article/pii/S0005109811002238">Convex parametric piecewise quadratic optimization: Theory and algorithms</a><br />
Automatica, vol. 47, no. 8, pp. 1770–1777, 2011<br />
P. Patrinos H. Sarimveis</p>
</li>
<li><p><a href="http://ieeexplore.ieee.org/abstract/document/6160798/">Stochastic MPC for real-time market-based optimal power dispatch</a><br />
Proc. 50th IEEE Conference on Decision and Control, pp. 7111–7116, 2011<br />
P. Patrinos, S. Trimboli, and A. Bemporad</p>
</li>
<li><p><a href="https://www.sciencedirect.com/science/article/pii/B9780444542984500775">Physiologically based pharmacokinetic modeling and predictive control: an integrated approach for optimal drug administration</a><br />
21st European Symposium on Computer Aided Chemical Engineering, pp. 1490–1494, 2011<br />
P. Sopasakis, S. Patrinos, P. Giannikou, H. Sarimveis</p>
</li>
<li><p><a href="https://www.sciencedirect.com/science/article/pii/S1474667016456466">Stochastic model predictive control for constrained networked control systems with random time delay</a><br />
18th IFAC World Congress, pp. 12626–12631, 2011<br />
P. Patrinos, P. Sopasakis, H. Sarimveis</p>
</li>
</ul>
<h3>2010</h3>
<ul>
<li><p><a href="https://www.sciencedirect.com/science/article/pii/S000510981000258X">A new algorithm for solving convex parametric quadratic programs based on graphical derivatives of solution mappings</a><br />
Automatica, vol. 46, no. 9, pp. 1405–1418, 2010<br />
P. Patrinos, H. Sarimveis</p>
</li>
<li><p><a href="http://www.worldscientific.com/doi/abs/10.1142/S0129065710002474">Variable selection in nonlinear modeling based on RBF networks and evolutionary computation</a><br />
International Journal of Neural Systems, vol. 20, no. 5, pp. 365–379, 2010<br />
P. Patrinos, A. Alexandridis, K. Ninos, and H. Sarimveis</p>
</li>
</ul>
<h3>Prior 2010</h3>
<ul>
<li><p><a href="https://www.sciencedirect.com/science/article/pii/S0305054807000366">Dynamic modeling and control of supply chain systems: A review</a><br />
Computers and Operations Research, vol. 35, no. 11, pp. 3530– 3561, 2008<br />
H. Sarimveis, P. Patrinos, C. Tarantilis, C. Kiranoudis</p>
</li>
<li><p><a href="http://ieeexplore.ieee.org/abstract/document/7068826/">An explicit optimal control approach for mean-risk dynamic portfolio allocation</a><br />
9th European Control Conference (ECC), 2007<br />
P. Patrinos, H. Sarimveis</p>
</li>
<li><p><a href="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</a><br />
Journal of Food Engineering, vol. 75, no. 2, pp. 196–204, 2006<br />
F. Doganis, A. Alexandridis, P. Patrinos, H. Sarimveis</p>
</li>
<li><p><a href="https://www.sciencedirect.com/science/article/pii/S0169743904001716">A two-stage evolutionary algorithm for variable selection in the development of RBF neural network models</a><br />
Chemometrics and Intelligent Laboratory Systems, vol. 75, no. 2, pp. 149–162, 2005<br />
A. Alexandridis, P. Patrinos, H. Sarimveis</p>
</li>
<li><p><a href="https://www.sciencedirect.com/science/article/pii/S1474667016370999">An RBF based neuro-dynamic approach for the control of stochastic dynamic systems</a><br />
16th IFAC World Congress, (Prague, Czech Republic), pp. 1086–1086, 2005<br />
P. Patrinos and H. Sarimveis</p>
</li>
<li><p><a href="https://www.sciencedirect.com/science/article/pii/S157079460480110X">Development of nonlinear quantitative structure-activity relationships using RBF networks and evolutionary computing</a><br />
14th European Symposium on Computer Aided Process Engineering-ESCAPE 14, vol. 18, pp. 265–270, 2004<br />
P. Patrinos, A. Alexandridis, A. Afantitis, H. Sarimveis, and O. Igglesi-Markopoulou</p>
</li>
</ul>
</td>
</tr>
</table>
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