113 citations · 1k across the 84 of their papers we have counts for
6 papers · 1 filter
Approximating Hessian matrices using Bayesian inference: a new approach for quasi-Newton methods in stochastic optimization
Andre Carlon, Luis Espath, Raul Tempone
Using quasi-Newton methods in stochastic optimization is not a trivial task given the difficulty of extracting curvature information from the noisy gradients. Moreover, pre-conditi…
On the equivalence of different adaptive batch size selection strategies for stochastic gradient descent methods
Luis Espath, Sebastian Krumscheid, Raúl Tempone +1
In this study, we demonstrate that the norm test and inner product/orthogonality test presented in \cite{Bol18} are equivalent in terms of the convergence rates associated with Sto…
Multi-Iteration Stochastic Optimizers
Andre Carlon, Luis Espath, Rafael Lopez +1
We introduce Multi-Iteration Stochastic Optimizers, a novel class of first-order stochastic methods that control the relative error using successive control variates along th…
An a posteriori error estimate for Symplectic Euler approximation of optimal control problems
Jesper Karlsson, Stig Larsson, Mattias Sandberg +2
This work focuses on numerical solutions of optimal control problems. A time discretization error representation is derived for the approximation of the associated value function.…
Mean-Field Games for Marriage
Dario Bauso, Ben Mansour Dia, Boualem Djehiche +2
This article examines mean-field games for marriage. The results support the argument that optimizing the long-term well-being through effort and social feeling state distribution…
A Stochastic Maximum Principle for Risk-Sensitive Mean-Field Type Control
Boualem Djehiche, Hamidou Tembine, Raul Tempone
In this paper we study mean-field type control problems with risk-sensitive performance functionals. We establish a stochastic maximum principle (SMP) for optimal control of stocha…