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Your search keyword '"Patrinos, Panagiotis"' showing total 82 results
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82 results on '"Patrinos, Panagiotis"'

26. Optimal Versus Approximate Channel Selection Methods for EEG Decoding With Application to Topology-Constrained Neuro-Sensor Networks.

Author

Narayanan, Abhijith Mundanad, Patrinos, Panagiotis, and Bertrand, Alexander

Subjects
AUDITORY selective attention, ELECTROENCEPHALOGRAPHY, WIRELESS sensor networks, ITERATIVE decoding, HEURISTIC, BRAIN-computer interfaces, GREEDY algorithms, SENSOR networks
Abstract

Channel selection or electrode placement for neural decoding is a commonly encountered problem in electroencephalography (EEG). Since evaluating all possible channel combinations is usually infeasible, one usually has to settle for heuristic methods or convex approximations without optimality guarantees. To date, it remains unclear how large the gap is between the selection made by these approximate methods and the truly optimal selection. The goal of this paper is to quantify this optimality gap for several state-of-the-art channel selection methods in the context of least-squares based neural decoding. To this end, we reformulate the channel selection problem as a mixed-integer quadratic program (MIQP), which allows the use of efficient MIQP solvers to find the optimal channel combination in a feasible computation time for up to 100 candidate channels. As this reveals the exact solution to the combinatorial problem, it allows to quantify the performance losses when using state-of-the-art sub-optimal (yet faster) channel selection methods. In a context of auditory attention decoding, we find that a greedy channel selection based on the utility metric does not show a significant optimality gap compared to optimal channel selection, whereas other state-of-the-art greedy or ${l}_{{1}}$ -norm penalized methods do show a significant loss in performance. Furthermore, we demonstrate that the MIQP formulation also provides a natural way to incorporate topology constraints in the selection, e.g., for electrode placement in neuro-sensor networks with galvanic separation constraints. Furthermore, a combination of this utility-based greedy selection with an MIQP solver allows to perform a topology constrained electrode placement, even in large scale problems with more than 100 candidate positions. [ABSTRACT FROM AUTHOR]

Published
2021
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27. Risk-Averse Model Predictive Operation Control of Islanded Microgrids.

Author

Hans, Christian A., Sopasakis, Pantelis, Raisch, Jorg, Reincke-Collon, Carsten, and Patrinos, Panagiotis

Subjects
PREDICTION models, RENEWABLE energy sources, MICROGRIDS, ERROR probability, FORECASTING, SENSITIVITY analysis
Abstract

In this paper, we present a risk-averse model predictive control (MPC) scheme for the operation of islanded microgrids with very high share of renewable energy sources. The proposed scheme mitigates the effect of errors in the determination of the probability distribution of renewable infeed and load. This allows to use less complex and less accurate forecasting methods and to formulate low-dimensional scenario-based optimization problems, which are suitable for control applications. Additionally, the designer may trade performance for safety by interpolating between the conventional stochastic and worst case MPC formulations. The presented risk-averse MPC problem is formulated as a mixed-integer quadratically constrained quadratic problem and its favorable characteristics are demonstrated in a case study. This includes a sensitivity analysis that illustrates the robustness to load and renewable power prediction errors. [ABSTRACT FROM AUTHOR]

Published
2020
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34. SuperMann: A Superlinearly Convergent Algorithm for Finding Fixed Points of Nonexpansive Operators.

Author

Themelis, Andreas and Patrinos, Panagiotis

Subjects
*NONEXPANSIVE mappings, *ALGORITHMS, *JACOBIAN matrices, *CONVEX functions
Abstract

Operator splitting techniques have recently gained popularity in convex optimization problems arising in various control fields. Being fixed-point iterations of nonexpansive (NE) operators, such methods suffer many well known downsides, which include high sensitivity to ill conditioning and parameter selection, and consequent low accuracy and robustness. As universal solution we propose SuperMann, a Newton-type algorithm for finding fixed points of NE operators. It generalizes the classical Krasnosel'skii–Mann scheme, enjoys its favorable global convergence properties and requires exactly the same oracle. It is based on a novel separating hyperplane projection tailored for nonexpansive mappings which makes it possible to include steps along any direction. In particular, when the directions satisfy a Dennis–Moré condition we show that SuperMann converges superlinearly under mild assumptions, which, surprisingly, do not entail nonsingularity of the Jacobian at the solution but merely metric subregularity. As a result, SuperMann enhances and robustifies all operator splitting schemes for structured convex optimization, overcoming their well known sensitivity to ill conditioning. [ABSTRACT FROM AUTHOR]

Published
2019
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39. GPU-Accelerated Stochastic Predictive Control of Drinking Water Networks.

Author

Sampathirao, Ajay Kumar, Sopasakis, Pantelis, Bemporad, Alberto, and Panos Patrinos, Panagiotis

Subjects
GRAPHICS processing units, STOCHASTIC models, PREDICTIVE control systems
Abstract

Despite the proven advantages of scenario-based stochastic model predictive control for the operational control of water networks, its applicability is limited by its considerable computational footprint. In this paper, we fully exploit the structure of these problems and solve them using a proximal gradient algorithm parallelizing the involved operations. The proposed methodology is applied and validated on a case study: the water network of the city of Barcelona. [ABSTRACT FROM PUBLISHER]

Published
2018
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40. A New Randomized Block-Coordinate Primal-Dual Proximal Algorithm for Distributed Optimization.

Author

Latafat, Puya, Freris, Nikolaos M., and Patrinos, Panagiotis

Subjects
DISTRIBUTED algorithms, PROCESS optimization, CONVEX functions, LINEAR operators, SYMMETRIC matrices
Abstract

This paper proposes Triangularly Preconditioned Primal- Dual algorithm, a new primal-dual algorithm for minimizing the sum of a Lipschitz-differentiable convex function and two possibly nonsmooth convex functions, one of which is composed with a linear mapping. We devise a randomized block-coordinate (BC) version of the algorithm which converges under the same stepsize conditions as the full algorithm. It is shown that both the original as well as the BC scheme feature linear convergence rate when the functions involved are either piecewise linear-quadratic, or when they satisfy a certain quadratic growth condition (which is weaker than strong convexity). Moreover, we apply the developed algorithms to the problem of multiagent optimization on a graph, thus obtaining novel synchronous and asynchronous distributed methods. The proposed algorithms are fully distributed in the sense that the updates and the stepsizes of each agent only depend on local information. In fact, no prior global coordination is required. Finally, we showcase an application of our algorithm in distributed formation control. [ABSTRACT FROM AUTHOR]

Published
2019
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41. Newton-Type Alternating Minimization Algorithm for Convex Optimization.

Author

Stella, Lorenzo, Themelis, Andreas, and Patrinos, Panagiotis

Subjects
LAGRANGIAN functions, DYNAMICAL systems, NONLINEAR systems, APPLIED mathematics, SYSTEMS theory
Abstract

We propose a Newton-type alternating minimization algorithm (NAMA) for solving structured nonsmooth convex optimization problems where the sum of two functions is to be minimized, one being strongly convex and the other composed with a linear mapping. The proposed algorithm is a line-search method over a continuous, real-valued, exact penalty function for the corresponding dual problem, which is computed by evaluating the augmented Lagrangian at the primal points obtained by alternating minimizations. As a consequence, NAMA relies on exactly the same computations as the classical alternating minimization algorithm (AMA), also known as the dual-proximal gradient method. Under standard assumptions, the proposed algorithm converges with global sublinear and local linear rates, while under mild additional assumptions, the asymptotic convergence is superlinear, provided that the search directions are chosen according to quasi-Newton formulas. Due to its simplicity, the proposed method is well suited for embedded applications and large-scale problems. Experiments show that using limited-memory directions in NAMA greatly improves the convergence speed over AMA and its accelerated variant. [ABSTRACT FROM AUTHOR]

Published
2019
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46. Multidisciplinary Learning through Implementation of the DVB-S2 Standard.

Author

Murillo, Yuri, Bergh, Bertold Van den, Beysens, Jona, Bertrand, Alexander, Dehaene, Wim, Patrinos, Panagiotis, Tuytelaars, Tinne, Sabariego, Ruth Vazquez, Verhelst, Marian, Wambacq, Patrick, and Pollin, Sofie

Subjects
DIGITAL video broadcasting, TELECOMMUNICATIONS standards, INFORMATION theory, CONTROL theory (Engineering), VHDL (Computer hardware description language)
Abstract

Telecommunication standards are documents that contain consolidated knowledge about communication systems and implementation best practices. They are created based on long consensus processes in order to meet practical constraints. This article describes how the DVB-S2 standard is used in the electrical engineering curricula at KU Leuven within a design and implementation course called EAGLE. The goal of the course is to teach third-year Bachelor students how to apply the abstract knowledge gathered in the theoretical courses (control theory, communication theory, software, and hardware) to the design of a complex and real engineering project. Based on their progress during the academic year, we illustrate how standards can be used to teach a broad skill set and act as an accelerator to abstract system implementation complexity. We give an overview of the objectives of the EAGLE project and the relevance of the DVB-S2 standard, the design and implementation task that the students need to accomplish, and how they finally learn to master the complexity of a real communication standard, VHDL coding, and system verification and integration. [ABSTRACT FROM PUBLISHER]

Published
2017
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