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.
| Identifer | oai:union.ndltd.org:NSYSU/oai:NSYSU:etd-0709112-151634 |
| Date | 09 July 2012 |
| Creators | Chow, Chung-Huo |
| Contributors | Ngai-Ching Wong, Jen-Chih Yao, Lai-Jiu Lin, Hong-Kun Xu |
| Publisher | NSYSU |
| Source Sets | NSYSU Electronic Thesis and Dissertation Archive |
| Language | English |
| Detected Language | English |
| Type | text |
| Format | application/pdf |
| Source | http://etd.lib.nsysu.edu.tw/ETD-db/ETD-search/view_etd?URN=etd-0709112-151634 |
| Rights | unrestricted, Copyright information available at source archive |
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