Subgradient algorithm on Riemannian manifolds

被引:81
作者
Ferreira, OP [1 ]
Oliveira, PR
机构
[1] Univ Fed Goias, Inst Matemat & Estat, Goiania, Go, Brazil
[2] Univ Fed Rio de Janeiro, COPPE, Programa Engn Sist & Comp, BR-21945 Rio De Janeiro, Brazil
关键词
nondifferentiable optimization; convex programming; subgradient methods; Riemannian manifolds;
D O I
10.1023/A:1022675100677
中图分类号
C93 [管理学]; O22 [运筹学];
学科分类号
070105 ; 12 ; 1201 ; 1202 ; 120202 ;
摘要
The subgradient method is generalized to the context of Riemannian manifolds. The motivation can be seen in non-Euclidean metrics that occur in interior-point methods. In that frame, the natural curves for local steps are the geodesics relative to the specific Riemannian manifold. In this paper, the influence of the sectional curvature of the manifold on the convergence of the method is discussed, as well as the proof of convergence if the sectional curvature is nonnegative.
引用
收藏
页码:93 / 104
页数:12
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