Marie Maros - IEEE Xplore Author Profile

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We investigate sparse linear regression in high-dimension via the projected LASSO estimator from distributed datasets across a network of agents. This model enables the ambient dimension to scale exponentially with the total sample size. We develop a unified algorithmic framework that encompasses a variety of distributed algorithms, new and old, such as primal, primal-dual, and gradient tracking-b...Show More
In this article, we consider a distributed convex optimization problem over time-varying undirected networks. We propose a dual method, primarily averaged network dual ascent (PANDA), that is proven to converge R-linearly to the optimal point given that the agents' objective functions are strongly convex and have Lipschitz continuous gradients. Like dual decomposition, PANDA requires half the amou...Show More
In this paper we consider distributed convex optimization over time-varying undirected graphs. We propose a linearized version of primarily averaged network dual ascent (PANDA) that keeps the advantages of PANDA while requiring less computational costs. The proposed method, economic primarily averaged network dual ascent (Eco-PANDA), provably converges at R-linear rate to the optimal point given t...Show More
Solving optimization problems in multi-agent networks where each agent only has partial knowledge of the problem has become an increasingly important problem. In this paper, we consider the problem of minimizing the sum of n convex functions. We assume that each function is only known by one agent. We show that generalized distributed alternating direction method of multipliers (ADMM) converges Q-...Show More
In this paper we consider a distributed convex optimization problem over time-varying networks. We propose a dual method that converges R-linearly to the optimal point given that the agents' objective functions are strongly convex and have Lipschitz continuous gradients. The proposed method requires half the amount of variable exchanges per iteration than methods based on DIGing, and yields improv...Show More
Electric power distribution systems encounter fluctuations in supply due to renewable sources with high variability in generation capacity. It is therefore necessary to provide algorithms that are capable of dynamically finding approximate solutions. We propose two semi-distributed algorithms based on ADMM and discuss their advantages and disadvantages. One of the algorithms computes a feasible ap...Show More
This paper shows the capability of the alternating direction method of multipliers (ADMM) to track, in a distributed manner, the optimal down-link beam-forming solution in a multiple-input single-output multicell network given a dynamic channel. Each time the channel changes, ADMM is allowed to perform one algorithm iteration. In order to implement the proposed scheme, the base stations are not re...Show More