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Convergece Analysis of the Gradient-Projection Method

We consider the constrained convex minimization problem:
min_x∈C f(x)
we will present gradient projection method which generates a sequence x^k
according to the formula
x^(k+1) = P_c(x^k − £\_k∇f(x^k)), k= 0, 1, ¡P ¡P ¡P ,
our ideal is rewritten the formula as a xed point algorithm:
x^(k+1) = T_(£\k)x^k, k = 0, 1, ¡P ¡P ¡P
is used to solve the minimization problem.
In this paper, we present the gradient projection method(GPM) and different choices of the stepsize to discuss the convergence of gradient projection
method which converge to a solution of the concerned problem.

Identiferoai:union.ndltd.org:NSYSU/oai:NSYSU:etd-0709112-151634
Date09 July 2012
CreatorsChow, Chung-Huo
ContributorsNgai-Ching Wong, Jen-Chih Yao, Lai-Jiu Lin, Hong-Kun Xu
PublisherNSYSU
Source SetsNSYSU Electronic Thesis and Dissertation Archive
LanguageEnglish
Detected LanguageEnglish
Typetext
Formatapplication/pdf
Sourcehttp://etd.lib.nsysu.edu.tw/ETD-db/ETD-search/view_etd?URN=etd-0709112-151634
Rightsunrestricted, Copyright information available at source archive

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