Avoidance of Convex and Concave Obstacles with Convergence ensured through Contraction

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Adaptation in Stochastic Algorithms: From Nonsmooth Optimization to Min-Max Problems and Beyond

Stochastic gradient descent (SGD) and randomized coordinate descent (RCD) are two of the workhorses for training modern automated decision systems. Intriguingly, convergence properties of these methods are not well-established as we move away from the spec ...

EPFL2021

Coupling-dependent convergence behavior of phase oscillators with tegotae-control

Auke Ijspeert, Jonathan Patrick Arreguit O'Neill, Simon Lukas Hauser, Matthieu Guillaume Marie Dujany

A bio-inspired way to model locomotion is using a network of coupled phase oscillators to create a Central Pattern Generator (CPG). The recently developed feedback control method tegotae includes exteroceptive force feedback into the governing phase update ...

IEEE2021

Data-Driven Convergence Prediction of Astrobots Swarms

Denis Gillet, Jean-Paul Richard Kneib, Matin Macktoobian, Francesco Basciani

Astrobots are robotic artifacts whose swarms are used in astrophysical studies to generate the map of the observable universe. These swarms have to be coordinated with respect to various desired observations. Such coordination\footnote{\z{A coordination sa ...

2022

On the Almost Sure Convergence of Stochastic Gradient Descent in Non-Convex Problems

Volkan Cevher, Ali Kavis, Shaul Nadav Hallak

This paper analyzes the trajectories of stochastic gradient descent (SGD) to help understand the algorithm’s convergence properties in non-convex problems. We first show that the sequence of iterates generated by SGD remains bounded and converges with prob ...

2020

Walkman: A Communication-Efficient Random-Walk Algorithm for Decentralized Optimization

Ali H. Sayed, Kun Yuan

This paper introduces a new algorithm for consensus optimization in a multi-agent network, where all agents collaboratively find a minimizer for the sum of their private functions. All decentralized algorithms rely on communications between adjacent nodes. ...

IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC2020

From High-Level to Low-Level Robot Learning of Complex Tasks: Leveraging Priors, Metrics and Dynamical Systems

Nadia Barbara Figueroa Fernandez

Humans have a remarkable way of learning, adapting and mastering new manipulation tasks. With the current advances in Machine Learning (ML), the promise of having robots with such capabilities seems to be on the cusp of reality. Transferring human-level sk ...

EPFL2019

Exact Diffusion for Distributed Optimization and Learning-Part II: Convergence Analysis

Ali H. Sayed, Bicheng Ying, Kun Yuan

Part I of this paper developed the exact diffusion algorithm to remove the bias that is characteristic of distributed solutions for deterministic optimization problems. The algorithm was shown to be applicable to the larger set of locally balanced left-sto ...

IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC2019

Stability Analysis of Primary Plug-and-Play and Secondary Leader-Based Controllers for DC Microgrid Clusters

Giancarlo Ferrari Trecate, Michele Tucci, Andrea Martinelli, Renke Han

In this paper, we propose a new hierarchical control scheme for microgrid (MG) clusters, given by the interconnection of atomic dc MGs with ZIP loads, each composed by both grid-forming and grid-feeding converters. In the primary level, we develop a new Pl ...

IEEE Institute of Electrical and Electronics Engineers2019

Sparse Inverse Problems Over Measures: Equivalence Of The Conditional Gradient And Exchange Methods

Armin Eftekhari

We study an optimization program over nonnegative Borel measures that encourages sparsity in its solution. Efficient solvers for this program are in increasing demand, as it arises when learning from data generated by a "continuum-of-subspaces" model, a re ...

SIAM PUBLICATIONS2019

Distributed Fault Detection for Interconnected Large-Scale Systems: A Scalable Plug & Play Approach

Giancarlo Ferrari Trecate, Marcello Farina

In this paper, we propose a novel distributed fault detection method to monitor the state of a-possibly large scale-linear system, partitioned into interconnected subsystems. The approach hinges on the definition of a partition-based distributed Luenberger ...

2019

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