4 papers
In-span learning: adapting reduced-order models using their own predictions
Amirpasha Hedayat, Laura Balzano, Karthik Duraisamy
Reduced-order models compress high-dimensional dynamics into low-dimensional representations that can be evaluated rapidly, but they lose accuracy when online dynamics drift beyond…
History-aware adaptive reduced-order models via incremental singular value decomposition
Amirpasha Hedayat, Ali Mohaghegh, Laura Balzano +2
Reduced-order models (ROMs) can accelerate high-dimensional dynamical simulations, but their accuracy often deteriorates when online dynamics leave the regime represented by offlin…
Convergence and complexity of block majorization-minimization for constrained block-Riemannian optimization
Yuchen Li, Laura Balzano, Deanna Needell +1
Block majorization-minimization (BMM) is a simple iterative algorithm for nonconvex optimization that sequentially minimizes a majorizing surrogate of the objective function in eac…
Convergence and Complexity Guarantee for Inexact First-order Riemannian Optimization Algorithms
Yuchen Li, Laura Balzano, Deanna Needell +1
We analyze inexact Riemannian gradient descent (RGD) where Riemannian gradients and retractions are inexactly (and cheaply) computed. Our focus is on understanding when inexact RGD…