Global Convergence of the Heavy-ball Method for Convex Optimization
2015 (English)In: European Control Conference (ECC15), IEEE , 2015Conference paper (Refereed)
This paper establishes global convergence and provides global bounds of the rate of convergence for the Heavy-ball method for convex optimization. When the objective function has Lipschitz-continuous gradient, we show that the Cesáro average of the iterates converges to the optimum at a rate of O(1/k) where k is the number of iterations. When the objective function is also strongly convex, we prove that the Heavy-ball iterates converge linearly to the unique optimum. Numerical examples validate our theoretical findings.
Place, publisher, year, edition, pages
IEEE , 2015.
Optimization, Convex, Heavy ball, Gradient iteration
Engineering and Technology
IdentifiersURN: urn:nbn:se:kth:diva-183460DOI: 10.1109/ECC.2015.7330562ScopusID: 2-s2.0-84963894675OAI: oai:DiVA.org:kth-183460DiVA: diva2:911488
QC 201603142016-03-122016-03-122016-03-14Bibliographically approved