Alberto Bemporad

Learning low-dimensional embeddings for black-box optimization

When gradient-based methods are impractical, black-box optimization (BBO) provides a valuable alternative. However, BBO often struggles with high-dimensional problems and limited trial budgets. In this work, we propose a novel approach based on meta-learning to pre-compute a redu...

A proximal augmented Lagrangian method for nonconvex optimization with equality and inequality constraints

Abstract We propose an inexact proximal augmented Lagrangian method (P-ALM) for solving nonconvex structured optimization problems. The proposed method features an easily implementable rule not only for updating the penalty parameters, but also for adaptively tuning the proximal...