4 papers
In-context learning from self-generated trajectories for adaptive model reduction
Amirpasha Hedayat, Laura Balzano, Karthik Duraisamy
High-fidelity simulations of complex physical systems are often too expensive for repeated prediction, design, and control. Reduced-order models address this computational cost by…
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 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…
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…