a simple sufficient descent method for unconstrained optimization
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ID: 204014
2010
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Abstract
We develop a sufficient descent method for solving large-scale unconstrained optimization problems. At each iteration, the search direction is a linear combination of the gradient
at the current and the previous steps. An attractive property of this method is that the generated directions are always descent. Under some appropriate conditions, we show that the proposed
method converges globally. Numerical experiments on some unconstrained minimization problems
from CUTEr library are reported, which illustrate that the proposed method is promising.
| Reference Key |
zhang2010mathematicala
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|---|---|
| Authors | ;Ming-Liang Zhang;Yun-Hai Xiao;Dangzhen Zhou |
| Journal | journal of power sources |
| Year | 2010 |
| DOI |
10.1155/2010/684705
|
| URL | |
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