12 citations · 52 across the 24 of their papers we have counts for
4 papers · 2 filters
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…
Stochastic Mirror Descent for Convex Optimization with Consensus Constraints
Anastasia Borovykh, Nikolas Kantas, Panos Parpas +1
The mirror descent algorithm is known to be effective in situations where it is beneficial to adapt the mirror map to the underlying geometry of the optimization model. However, th…