Routing table storage demands pose a significant obstacle for large-scale network simulation. On-demand computation of routes can alleviate those problems for models that do not r...
We consider the problem of numerical stability and model density growth when training a sparse linear model from massive data. We focus on scalable algorithms that optimize certain...
The aim of the paper is to provide a theoretical basis for approximate reduced SQP methods. In contrast to inexact reduced SQP methods, the forward and the adjoint problem accuraci...
Kazufumi Ito, Karl Kunisch, Volker Schulz, Ilia Gh...
Abstract. A new iterative algorithm for the solution of minimization problems in infinitedimensional Hilbert spaces which involve sparsity constraints in form of p-penalties is pro...
We introduce the first algorithm for off-policy temporal-difference learning that is stable with linear function approximation. Off-policy learning is of interest because it forms...