2 citations · 5 across the 17 of their papers we have counts for
21 papers
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…
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…
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…
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 \m…
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…
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…
Regularized methods via cubic model subspace minimization for nonconvex optimization
Stefania Bellavia, Davide Palitta, Margherita Porcelli +1
Adaptive cubic regularization methods for solving nonconvex problems need the efficient computation of the trial step, involving the minimization of a cubic model. We propose a new…
Accelerated Algorithms for Nonlinear Matrix Decomposition with the ReLU function
Giovanni Seraghiti, Atharva Awari, Arnaud Vandaele +2
In this paper, we study the following nonlinear matrix decomposition (NMD) problem: given a sparse nonnegative matrix , find a low-rank matrix such that , wh…
Numerical solution of a class of quasi-linear matrix equations
Margherita Porcelli, Valeria Simoncini
Given the matrix equation in the unknown matrix , we analyze existence and uniqueness conditions, toget…
A semidefinite programming approach for the projection onto the cone of negative semidefinite symmetric tensors with applications to solid mechanics
Cristina Padovani, Margherita Porcelli
We propose an algorithm for computing the projection of a symmetric second-order tensor onto the cone of negative semidefinite symmetric tensors with respect to the inner product d…
An Improved Penalty Algorithm using Model Order Reduction for MIPDECO problems with partial observations
Dominik Garmatter, Margherita Porcelli, Francesco Rinaldi +1
This work addresses optimal control problems governed by a linear time-dependent partial differential equation (PDE) as well as integer constraints on the control. Moreover, partia…
A spectral PALM algorithm for matrix and tensor-train based Dictionary Learning
Domitilla Brandoni, Margherita Porcelli, Valeria Simoncini
Dictionary Learning (DL) is one of the leading sparsity promoting techniques in the context of image classification, where the "dictionary" matrix D of images and the sparse matrix…
Solving nonlinear systems of equations via spectral residual methods: stepsize selection and applications
Enrico Meli, Benedetta Morini, Margherita Porcelli +1
Spectral residual methods are derivative-free and low-cost per iteration procedures for solving nonlinear systems of equations. They are generally coupled with a nonmonotone linese…
Exploiting problem structure in derivative free optimization
Margherita Porcelli, Philippe L. Toint
A structured version of derivative-free random pattern search optimization algorithms is introduced which is able to exploit coordinate partially separable structure (typically ass…
A relaxed interior point method for low-rank semidefinite programming problems with applications to matrix completion
Stefania Bellavia, Jacek Gondzio, Margherita Porcelli
A new relaxed variant of interior point method for low-rank semidefinite programming problems is proposed in this paper. The method is a step outside of the usual interior point fr…
Improved penalty algorithm for Mixed Integer PDE Constrained Optimization Problems
Dominik Garmatter, Margherita Porcelli, Francesco Rinaldi +1
Optimal control problems including partial differential equation (PDE) as well as integer constraints merge the combinatorial difficulties of integer programming and the challenges…
Interior Point Methods and Preconditioning for PDE-Constrained Optimization Problems Involving Sparsity Terms
John W. Pearson, Margherita Porcelli, Martin Stoll
PDE-constrained optimization problems with control or state constraints are challenging from an analytical as well as numerical perspective. The combination of these constraints wi…
Finite element model updating for structural applications
Maria Girardi, Cristina Padovani, Daniele Pellegrini +2
A novel method for performing model updating on finite element models is presented. The approach is particularly tailored to modal analyses of buildings, by which the lowest freque…
A note on using performance and data profilesfor training algorithms
Margherita Porcelli, Philippe L. Toint
It is shown how to use the performance and data profile benchmarking tools to improve algorithms' performance. An illustration for the BFO derivative-free optimizer suggests that t…
Preconditioning PDE-constrained optimization with -sparsity and control constraints
Margherita Porcelli, Valeria Simoncini, Martin Stoll
PDE-constrained optimization aims at finding optimal setups for partial differential equations so that relevant quantities are minimized. Including sparsity promoting terms in the…
Preconditioning of Active-Set Newton Methods for PDE-constrained Optimal Control Problems
Margherita Porcelli, Valeria Simoncini, Mattia Tani
We address the problem of preconditioning a sequence of saddle point linear systems arising in the solution of PDE-constrained optimal control problems via active-set Newton method…