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Ergodic mirror descent
KTH, Skolan för elektro- och systemteknik (EES), Reglerteknik.
2012 (engelsk)Inngår i: SIAM Journal on Optimization, ISSN 1052-6234, E-ISSN 1095-7189, Vol. 22, nr 4, s. 1549-1578Artikkel i tidsskrift (Fagfellevurdert) Published
Abstract [en]

We generalize stochastic subgradient descent methods to situations in which we do not receive independent samples from the distribution over which we optimize, instead receiving samples coupled over time. We show that as long as the source of randomness is suitably ergodic it converges quickly enough to a stationary distribution-the method enjoys strong convergence guarantees, both in expectation and with high probability. This result has implications for stochastic optimization in high-dimensional spaces, peer-to-peer distributed optimization schemes, decision problems with dependent data, and stochastic optimization problems over combinatorial spaces.

sted, utgiver, år, opplag, sider
2012. Vol. 22, nr 4, s. 1549-1578
Emneord [en]
convex programming, stochastic optimization, Markov chain, Monte Carlo sampling mixing, mirror descent algorithm
HSV kategori
Identifikatorer
URN: urn:nbn:se:kth:diva-116742DOI: 10.1137/110836043ISI: 000312734300015Scopus ID: 2-s2.0-84871567576OAI: oai:DiVA.org:kth-116742DiVA, id: diva2:600765
Merknad

QC 20130125

Tilgjengelig fra: 2013-01-25 Laget: 2013-01-25 Sist oppdatert: 2017-12-06bibliografisk kontrollert

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