4 citations · 7 across the 6 of their papers we have counts for
6 papers
A penalty-interior point method combined with MADS for equality and inequality constrained optimization
Charles Audet, Andrea Brilli, Youssef Diouane +3
This work introduces MADS-PIP, an efficient framework that integrates a penalty-interior point strategy into the mesh adaptive direct search (MADS) algorithm for solving nonsmooth…
Nonlinear Derivative-free Constrained Optimization with a Penalty-Interior Point Method and Direct Search
Andrea Brilli, Ana L. Custódio, Giampaolo Liuzzi +1
In this work, we propose the joint use of a mixed penalty-interior point method and direct search, for addressing nonlinearly constrained derivative-free optimization problems. A m…
Nonlinear Derivative-free Constrained Optimization with a Penalty-Interior Point Method and Direct Search
Andrea Brilli, Ana L. Custódio, Giampaolo Liuzzi +1
In this work, the joint use of a mixed penalty-interior point method and direct search is proposed, to address {nonlinear} constrained derivative-free optimization problems. A meri…
Complexity results and active-set identification of a derivative-free method for bound-constrained problems
Andrea Brilli, Andrea Cristofari, Giampaolo Liuzzi +1
In this paper, we analyze a derivative-free line search method designed for bound-constrained problems. Our analysis demonstrates that this method exhibits a worst-case complexity…
Worst case complexity bounds for linesearch-type derivative-free algorithms
Andrea Brilli, Morteza Kimiaei, Giampaolo Liuzzi +1
This paper is devoted to the analysis of worst case complexity bounds for linesearch-type derivative-free algorithms for the minimization of general non-convex smooth functions. We…
An interior point method for nonlinear constrained derivative-free optimization
Andrea Brilli, Giampaolo Liuzzi, Stefano Lucidi
In this paper we consider constrained optimization problems where both the objective and constraint functions are of the black-box type. Furthermore, we assume that the nonlinear i…