12 citations · 52 across the 24 of their papers we have counts for
25 papers
Simulation-Based Neural Policies for Portfolio Choice: Architecture, Training, and Interpretability
Jules Viard, Alexander Michaelides, Panos Parpas
Many economic decision problems, lifecycle consumption-saving and dynamic portfolio choice, are finite-horizon stochastic control problems with continuous states and actions. When…
Adaptive Multilevel Newton: A Quadratically Convergent Optimization Method
Nick Tsipinakis, Panos Parpas, Matthias Voigt
Newton's method may exhibit slower convergence than vanilla Gradient Descent in its initial phase on strongly convex problems. Classical Newton-type multilevel methods mitigate thi…
Convergence rates of Newton's method for strongly self-concordant minimization
Nick Tsipinakis, Panos Parpas
Newton's method has been thoroughly studied for the class of self-concordant functions. However, a local analysis specific to strongly self-concordant functions (a subclass of the…
Kernel Sum of Squares for Data Adapted Kernel Learning of Dynamical Systems from Data: A global optimization approach
Daniel Lengyel, Panos Parpas, Boumediene Hamzi +1
This paper examines the application of the Kernel Sum of Squares (KSOS) method for enhancing kernel learning from data, particularly in the context of dynamical systems. Traditiona…
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…
Curvature Aligned Simplex Gradient: Principled Sample Set Construction For Numerical Differentiation
Daniel Lengyel, Panos Parpas, Nikolas Kantas +1
The simplex gradient, a popular numerical differentiation method due to its flexibility, lacks a principled method by which to construct the sample set, specifically the location o…