4 citations · 8 across the 4 of their papers we have counts for
6 papers · 1 filter
Second-order optimization for tensors with fixed tensor-train rank
Michael Psenka, Nicolas Boumal
There are several different notions of "low rank" for tensors, associated to different formats. Among them, the Tensor Train (TT) format is particularly well suited for tensors of…
Generalization of Quasi-Newton Methods: Application to Robust Symmetric Multisecant Updates
Damien Scieur, Lewis Liu, Thomas Pumir +1
Quasi-Newton techniques approximate the Newton step by estimating the Hessian using the so-called secant equations. Some of these methods compute the Hessian using several secant e…
Efficiently escaping saddle points on manifolds
Chris Criscitiello, Nicolas Boumal
Smooth, non-convex optimization problems on Riemannian manifolds occur in machine learning as a result of orthonormality, rank or positivity constraints. First- and second-order ne…
Simple algorithms for optimization on Riemannian manifolds with constraints
Changshuo Liu, Nicolas Boumal
We consider optimization problems on manifolds with equality and inequality constraints. A large body of work treats constrained optimization in Euclidean spaces. In this work, we…
Adaptive regularization with cubics on manifolds
Naman Agarwal, Nicolas Boumal, Brian Bullins +1
Adaptive regularization with cubics (ARC) is an algorithm for unconstrained, non-convex optimization. Akin to the popular trust-region method, its iterations can be thought of as a…
3D ab initio modeling in cryo-EM by autocorrelation analysis
Eitan Levin, Tamir Bendory, Nicolas Boumal +2
Single-Particle Reconstruction (SPR) in Cryo-Electron Microscopy (cryo-EM) is the task of estimating the 3D structure of a molecule from a set of noisy 2D projections, taken from u…