A first-order multigrid method for bound-constrained convex optimization

Michal Kocvara, Sudaba Mohammed

Research output: Contribution to journalArticlepeer-review

5 Citations (Scopus)
232 Downloads (Pure)


The aim of this paper is to design an efficient multigrid method for constrained convex optimization problems arising from discretization of some underlying infinite dimensional problems. Due to problem dependency of this approach, we only consider bound constraints with (possibly) a single equality constraint. As our aim is to target large-scale problems, we want to avoid computation of second derivatives of the objective function, thus excluding Newton like methods. We propose a smoothing operator that only uses first-order information and study the computational efficiency of the resulting method.
Original languageEnglish
Pages (from-to)622-644
Number of pages23
JournalOptimization Methods and Software
Issue number3
Early online date16 Mar 2016
Publication statusPublished - 2016


Dive into the research topics of 'A first-order multigrid method for bound-constrained convex optimization'. Together they form a unique fingerprint.

Cite this