5 citations · 9 across the 8 of their papers we have counts for
6 papers · 1 filter
Multilevel Regularized Newton Methods with Fast Convergence Rates
Nick Tsipinakis, Panos Parpas
We introduce new multilevel methods for solving large-scale unconstrained optimization problems. Specifically, the philosophy of multilevel methods is applied to Newton-type method…
Simba: A Scalable Bilevel Preconditioned Gradient Method for Fast Evasion of Flat Areas and Saddle Points
Nick Tsipinakis, Panos Parpas
The convergence behaviour of first-order methods can be severely slowed down when applied to high-dimensional non-convex functions due to the presence of saddle points. If, additio…
Data-driven initialization of deep learning solvers for Hamilton-Jacobi-Bellman PDEs
Anastasia Borovykh, Dante Kalise, Alexis Laignelet +1
A deep learning approach for the approximation of the Hamilton-Jacobi-Bellman partial differential equation (HJB PDE) associated to the Nonlinear Quadratic Regulator (NLQR) problem…
Uncertainty quantification for subgradient descent, with applications to relaxations of discrete problems
Conor McMeel, Panos Parpas
We consider the problem of minimizing a convex function that depends on an uncertain parameter . The uncertainty in the objective function means that the optimum, , is a…
Fast Multilevel Algorithms for Compressive Principle Component Pursuit
Vahan Hovhannisyan, Yannis Panagakis, Panos Parpas +1
Recovering a low-rank matrix from highly corrupted measurements arises in compressed sensing of structured high-dimensional signals (e.g., videos and hyperspectral images among oth…
Error Bounds for Control Constrained Singularly Perturbed Linear-Quadratic Optimal Control Problems
Sei Howe, Panos Parpas
We present a methodology for bounding the error term of an asymptotic solution to a singularly perturbed optimal control (SPOC) problem whose exact solution is known to be computat…