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Diffusion LMS for multitask problems with overlapping hypothesis subspaces

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Convergence analysis of Padé approximations for Helmholtz frequency response problems

Fabio Nobile, Francesca Bonizzoni

The present work concerns the approximation of the solution map

S

associated to the parametric Helmholtz boundary value problem, i.e., the map which associates to each (real) wavenumber belonging to a given interval of interest the corresponding solution ...

2018

Diffusion LMS for multitask problems with overlapping hypothesis subspaces

Graph Chatbot

Chattez avec Graph Search

Stochastic pairwise preference convergence in Bayesian agents

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

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

Learning Generalized Nash Equilibria in a Class of Convex Games

Avoidance of Convex and Concave Obstacles with Convergence ensured through Contraction

Convergence analysis of Padé approximations for Helmholtz frequency response problems

Performance limits of stochastic sub-gradient learning, Part II: Multi-agent case

A coordinator-driven communication reduction scheme for distributed optimization using the projected gradient method

A coordinator-driven communication reduction scheme for distributed optimization using the projected gradient method

Convergence Of Variance-Reduced Learning Under Random Reshuffling

Stochastic pairwise preference convergence in Bayesian agents

Learning Generalized Nash Equilibria in a Class of Convex Games

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

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

A coordinator-driven communication reduction scheme for distributed optimization using the projected gradient method

Convergence analysis of Padé approximations for Helmholtz frequency response problems

Convergence Of Variance-Reduced Learning Under Random Reshuffling

A coordinator-driven communication reduction scheme for distributed optimization using the projected gradient method

Performance limits of stochastic sub-gradient learning, Part II: Multi-agent case

Avoidance of Convex and Concave Obstacles with Convergence ensured through Contraction