| 1. |
- Bastianello, Nicola, et al.
(författare)
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Extrapolation-Based Prediction-Correction Methods for Time-varying Convex Optimization
- 2023
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Ingår i: Signal Processing. - : Elsevier BV. - 0165-1684 .- 1872-7557. ; 210
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Tidskriftsartikel (refereegranskat)abstract
- In this paper, we focus on the solution of online optimization problems that arise often in signal processing and machine learning, in which we have access to streaming sources of data. We discuss algorithms for online optimization based on the prediction-correction paradigm, both in the primal and dual space. In particular, we leverage the typical regularized least-squares structure appearing in many signal processing problems to propose a novel and tailored prediction strategy, which we call extrapolation-based. By using tools from operator theory, we then analyze the convergence of the proposed methods as applied both to primal and dual problems, deriving an explicit bound for the tracking error, that is, the distance from the time-varying optimal solution. We further discuss the empirical performance of the algorithm when applied to signal processing, machine learning, and robotics problems.
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| 2. |
- Bastianello, Nicola, et al.
(författare)
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Internal Model-Based Online Optimization
- 2024
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Ingår i: IEEE Transactions on Automatic Control. - : Institute of Electrical and Electronics Engineers (IEEE). - 0018-9286 .- 1558-2523. ; 69:1, s. 689-696
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Tidskriftsartikel (refereegranskat)abstract
- In this article, we propose a model-based approach to the design of online optimization algorithms, with the goal of improving the tracking of the solution trajectory (trajectories) w.r.t. state-of-the-art methods. We focus first on quadratic problems with a time-varying linear term, and use digital control tools (a robust internal model principle) to propose a novel online algorithm that can achieve zero tracking error by modeling the cost with a dynamical system. We prove the convergence of the algorithm for both strongly convex and convex problems. We further discuss the sensitivity of the proposed method to model uncertainties and quantify its performance. We discuss how the proposed algorithm can be applied to general (nonquadratic) problems using an approximate model of the cost, and analyze the convergence leveraging the small gain theorem. We present numerical results that showcase the superior performance of the proposed algorithms over previous methods for both quadratic and nonquadratic problems.
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| 3. |
- Bastianello, Nicola, et al.
(författare)
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Online Distributed Learning with Quantized Finite-Time Coordination
- 2023
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Ingår i: 2023 62nd IEEE Conference on Decision and Control, CDC 2023. - : Institute of Electrical and Electronics Engineers (IEEE). ; , s. 5026-5032
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Konferensbidrag (refereegranskat)abstract
- In this paper we consider online distributed learning problems. Online distributed learning refers to the process of training learning models on distributed data sources. In our setting a set of agents need to cooperatively train a learning model from streaming data. Differently from federated learning, the proposed approach does not rely on a central server but only on peer-to-peer communications among the agents. This approach is often used in scenarios where data cannot be moved to a centralized location due to privacy, security, or cost reasons. In order to overcome the absence of a central server, we propose a distributed algorithm that relies on a quantized, finite-time coordination protocol to aggregate the locally trained models. Furthermore, our algorithm allows for the use of stochastic gradients during local training. Stochastic gradients are computed using a randomly sampled subset of the local training data, which makes the proposed algorithm more efficient and scalable than traditional gradient descent. In our paper, we analyze the performance of the proposed algorithm in terms of the mean distance from the online solution. Finally, we present numerical results for a logistic regression task.
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| 4. |
- Carnevale, Guido, et al.
(författare)
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Distributed Consensus Optimization via ADMM-Tracking
- 2023
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Ingår i: 2023 The 62nd IEEE Conference on Decision and Control (CDC 2023), CDC. - : Institute of Electrical and Electronics Engineers (IEEE). ; , s. 290-295
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Konferensbidrag (refereegranskat)abstract
- In this paper, we propose a novel distributed algorithm for consensus optimization over networks. The key idea is to achieve dynamic consensus on the agents' average and on the global descent direction by iteratively solving an online auxiliary optimization problem through the Alternating Direction Method of Multipliers (ADMM). Such a mechanism is suitably interlaced with a local proportional action steering each agent estimate to the solution of the original consensus optimization problem. The analysis uses tools from system theory to prove the linear convergence of the scheme with strongly convex costs. Finally, some numerical simulations confirm our findings and show the robustness of the proposed scheme.
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| 5. |
- Demir, Ozlem Tugfe, et al.
(författare)
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Energy Reduction in Cell-Free Massive MIMO through Fine-Grained Resource Management
- 2024
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Ingår i: 2024 Joint European Conference on Networks and Communications and 6G Summit, EuCNC/6G Summit 2024. - : Institute of Electrical and Electronics Engineers (IEEE). ; , s. 547-552
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Konferensbidrag (refereegranskat)abstract
- The physical layer foundations of cell-free massive MIMO (CF-mMIMO) have been well-established. As a next step, researchers are investigating practical and energy-efficient network implementations. This paper focuses on multiple sets of access points (APs) where user equipments (UEs) are served in each set, termed a federation, without inter-federation interference. The combination of federations and CF-mMIMO shows promise for highly-loaded scenarios. Our aim is to minimize the total energy consumption while adhering to UE downlink data rate constraints. The energy expenditure of the full system is modelled using a detailed hardware model of the APs. We jointly design the AP-UE association variables, determine active APs, and assign APs and UEs to federations. To solve this highly combinatorial problem, we develop a novel alternating optimization algorithm. Simulation results for an indoor factory demonstrate the advantages of considering multiple federations, particularly when facing large data rate requirements. Furthermore, we show that adopting a more distributed CF-mMIMO architecture is necessary to meet the data rate requirements. Conversely, if feasible, using a less distributed system with more antennas at each AP is more advantageous from an energy savings perspective.
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| 6. |
- Deplano, Diego, et al.
(författare)
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A Unified Approach to Solve the Dynamic Consensus on the Average, Maximum, and Median Values with Linear Convergence
- 2023
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Ingår i: 2023 62nd IEEE Conference on Decision and Control, CDC 2023. - : Institute of Electrical and Electronics Engineers (IEEE). ; , s. 6442-6448
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Konferensbidrag (refereegranskat)abstract
- This manuscript proposes novel distributed algorithms for solving the dynamic consensus problem in discrete-time multi-agent systems on three different objective functions: the average, the maximum, and the median. In this problem, each agent has access to an external time-varying scalar signal and aims to estimate and track a function of all the signals by exploiting only local communications with other agents. By recasting the problem as an online distributed optimization problem, the proposed algorithms are derived based on the distributed implementation of the alternating direction method of multipliers (ADMM) and are thus amenable to a unified analysis technique. A major contribution is that of proving linear convergence of these ADMM-based algorithms for the specific dynamic consensus problems of interest, for which current results could only guarantee sub-linear convergence. In particular, the tracking error is shown to converge within a bound, whereas the steady-state error is zero. Numerical simulations corroborate the theoretical findings, empirically show the robustness of the proposed algorithms to re-initialization errors, and compare their performance with that of state-of-the-art algorithms.
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| 7. |
- Riveiros, Alejandro Penacho, et al.
(författare)
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Real-Time Anomaly Detection and Categorization for Satellite Reaction Wheels
- 2024
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Ingår i: 2024 European Control Conference, ECC 2024. - : Institute of Electrical and Electronics Engineers (IEEE). ; , s. 253-260
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Konferensbidrag (refereegranskat)abstract
- In this paper, we address the problem of detecting anomalies in the reaction wheel assemblies (RWAs) of a satellite. These anomalies can alert of an impending failure in a RWA, and effective detection would allow to take preventive action. To this end, we propose a novel algorithm that detects and categorizes anomalies in the friction profile of an RWA, where the profile relates spin rate to measured friction torque. The algorithm, developed in a probabilistic framework, runs in real-time and has a tunable false positive rate as a parameter. The performance of the proposed method is thoroughly tested in a number of numerical experiments, with different anomalies of varying severity.
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