Superlinearly convergent asynchronous distributed network newton method

Fatemeh Mansoori, Ermin Wei

Research output: Contribution to journalArticlepeer-review


The problem of minimizing a sum of local convex objective functions over a networked system captures many important applications and has received much attention in the distributed optimization field. Most of existing work focuses on development of fast distributed algorithms under the presence of a central clock. The only known algorithms with convergence guarantees for this problem in asynchronous setup could achieve either sublinear rate under totally asynchronous setting or linear rate under partially asynchronous setting (with bounded delay). In this work, we built upon existing literature to develop and analyze an asynchronous Newton based approach for solving a penalized version of the problem. We show that this algorithm converges almost surely with global linear rate and local superlinear rate in expectation. Numerical studies confirm superior performance against other existing asynchronous methods.

Original languageEnglish (US)
JournalUnknown Journal
StatePublished - May 10 2017

ASJC Scopus subject areas

  • General

Fingerprint Dive into the research topics of 'Superlinearly convergent asynchronous distributed network newton method'. Together they form a unique fingerprint.

Cite this