7 papers
Objective-Function Free Multi-Objective Optimization: Rate of Convergence and Performance of an Adagrad-like algorithm
Marianna De Santis, Gabriele Eichfelder, Margherita Porcelli
We propose an Adagrad-like algorithm for multi-objective unconstrained optimization that relies on the computation of a common descent direction only. Unlike classical local algori…
Quantum block encoding for one-pair semiseparable matrices
Giacomo Antonioli, Paola Boito, Gianna M. Del Corso +1
Quantum block encoding (QBE) is a crucial step in the development of most quantum algorithms, as it provides an embedding of a given matrix into a suitable larger unitary matrix. H…
An extrapolated and provably convergent algorithm for nonlinear matrix decomposition with the ReLU function
Nicolas Gillis, Margherita Porcelli, Giovanni Seraghiti
ReLU matrix decomposition (RMD) is the following problem: given a sparse, nonnegative matrix and a factorization rank , identify a rank- matrix such that $X\approx \…
Fast and Simple Multiclass Data Segmentation: An Eigendecomposition and Projection-Free Approach
Chiara Faccio, Margherita Porcelli, Francesco Rinaldi +1
Graph-based machine learning has seen an increased interest over the last decade with many connections to other fields of applied mathematics. Learning based on partial differentia…
A multilevel stochastic regularized first-order method with application to finite sum minimization
Filippo Marini, Margherita Porcelli, Elisa Riccietti
In this paper, we propose a multilevel stochastic framework for the solution of nonconvex unconstrained optimization problems. The proposed approach uses random regularized first-o…
prunAdag: an adaptive pruning-aware gradient method
Margherita Porcelli, Giovanni Seraghiti, Philippe L. Toint
A pruning-aware adaptive gradient method is proposed which classifies the variables in two sets before updating them using different strategies. This technique extends the ``releva…