• Distributed Principal Component Analysis with Limited Communication

Foivos Alimisis*, Peter Davies, Bart Vandereycken, Dan Alistarh

NeurIPS 2021

• Communication-Efficient Distributed Optimization with Quantized Preconditioners

Foivos Alimisis*, Peter Davies, Dan Alistarh

ICML 2021

• Momentum improves optimization on Riemannian manifolds

Foivos Alimisis*, Antonio Orvieto, Gary Becigneul, Aurelien Lucchi

AISTATS 2021

• A continuous-time perspective for modeling acceleration in riemannian optimization

Foivos Alimisis*, Antonio Orvieto, Gary Becigneul, Aurelien Lucchi

AISTATS 2020

• Characterization of optimization problems that are solvable iteratively with linear convergence

Foivos Alimisis*

MTNS 2024

• Geodesic convexity of the symmetric eigenvalue problem and convergence of Riemannian steepest descent

    Foivos Alimisis*,  Bart Vandereycken

    Journal of Optimization Theory and Applications, 2024

• Gradient-type subspace iteration methods for the symmetric eigenvalue problem

    Foivos Alimisis*, Yousef Saad,  Bart Vandereycken

    SIAM Journal on Matrix Analysis and Applications, 2024

Preprints (under review):

A Nesterov-style Accelerated Gradient Descent Algorithm for the Symmetric Eigenvalue Problem 

Foivos Alimisis*, Simon Vary,  Bart Vandereycken

A Preconditioned Inverse Iteration with an Improved Convergence Guarantee 

Foivos Alimisis*, Daniel Kressner, Nian Shao, Bart Vandereycken