Relaxed Recentered Log-Barrier Function based Nonlinear Model Predictive Control

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Neural Networks (NNs), renowned for their universal approximation capabilities, have proven well-suited for parametrizing nonlinear control policies and modeling complex processes from available data. However, several NN architectures are physics-agnostic, ...

We present a general variational framework for the training of freeform nonlinearities in layered computational architectures subject to some slope constraints. The regularization that we add to the traditional training loss penalizes the second-order tota ...

We consider the problem of learning to play a repeated contextual game with unknown reward and unknown constraints functions. Such games arise in applications where each agent’s action needs to belong to a feasible set, but the feasible set is a priori unk ...

Omnidirectional video streaming is usually implemented based on the representations of tiles, where the tiles are obtained by splitting the video frame into several rectangular areas and each tile is converted into multiple representations with different r ...

Federated learning (FL), as an emerging edge artificial intelligence paradigm, enables many edge devices to collaboratively train a global model without sharing their private data. To enhance the training efficiency of FL, various algorithms have been prop ...

We relate discrepancy theory with the classic scheduling problems of minimizing max flow time and total flow time on unrelated machines. Specifically, we give a general reduction that allows us to transfer discrepancy bounds in the prefix Beck-Fiala (bound ...

Proximal splitting methods are standard tools for nonsmooth optimization. While primal-dual methods have become very popular in the last decade for their flexibility, primal methods may still be preferred for two reasons: acceleration schemes are more effe ...

This paper considers optimization problems over networks where agents have individual objectives to meet, or individual parameter vectors to estimate, subject to subspace constraints that require the objectives across the network to lie in low-dimensional ...

This paper studies the problem of learning under both large datasets and large-dimensional feature space scenarios. The feature information is assumed to be spread across agents in a network, where each agent observes some of the features. Through local co ...

This paper proposes a methodology for computationally-efficient estimation of the local E-field maximums within the transformer winding insulation material for design optimization purposes, where besides the estimation accuracy, the computational cost repr ...

This thesis contributes towards the design and analysis of fast and distributed optimization algorithms based on splitting techniques, such as proximal gradient methods or alternation minimization algorithms, with the application of solving model predictiv ...

Within the context of microgrids optimal voltage control, most schemes proposed in the literature either rely on (i) droop-control methods or (ii) methods involving the computation of explicit nodal power set-points as a solution to a given optimization pr ...

A central problem in signal processing and communications is to design signals that are compact both in time and frequency. Heisenberg's uncertainty principle states that a given function cannot be arbitrarily compact both in time and frequency, defining a ...

In the past decades, wire electric discharge machining (W-EDM) has undergone a number of techno-logical developments aimed at improving its process performance. Still the current practice of W-EDM is not defect-free and results in a number of surface relat ...

On-line control and optimization can improve the eﬃciency of fuel cell systems whilst simultaneously ensuring that the operation remains within a safe region. Also, fuel cells are subject to frequent variations in their power demand. This paper investigate ...

The electricity markets worldwide have distinctive particularities due to some political and historical reasons. However, principal guidelines of market design remain very similar. The main feature of deregulation is to introduce a competition into the gen ...

Over the last two decades medical image processing has become a fundamental tool in medicine, and brain imaging is a paradigm thereof. The fast development of the imaging acquisition techniques allows medical staff to dispose of a variety of data coming fr ...

In the efficient social choice problem, the goal is to assign values, subject to side constraints, to a set of variables to maximize the total utility across a population of agents, where each agent has private information about its utility function. In th ...

Relaxed Recentered Log-Barrier Function based Nonlinear Model Predictive Control

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