A Riemannian conjugate gradient method for optimization on the Stiefel manifold
In this paper we propose a new Riemannian conjugate gradient method for optimization on the Stiefel manifold. We introduce two novel vector transports associated with the retraction constructed by the Cayley transform. Both of them satisfy the Ring-Wirth nonexpansive condition, which is fundamental...
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| Veröffentlicht in: | Computational optimization and applications 2017-05, Vol.67 (1), p.73-110 |
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| creator | Zhu, Xiaojing |
| description | In this paper we propose a new Riemannian conjugate gradient method for optimization on the Stiefel manifold. We introduce two novel vector transports associated with the retraction constructed by the Cayley transform. Both of them satisfy the Ring-Wirth nonexpansive condition, which is fundamental for convergence analysis of Riemannian conjugate gradient methods, and one of them is also isometric. It is known that the Ring-Wirth nonexpansive condition does not hold for traditional vector transports as the differentiated retractions of QR and polar decompositions. Practical formulae of the new vector transports for low-rank matrices are obtained. Dai’s nonmonotone conjugate gradient method is generalized to the Riemannian case and global convergence of the new algorithm is established under standard assumptions. Numerical results on a variety of low-rank test problems demonstrate the effectiveness of the new method. |
| doi_str_mv | 10.1007/s10589-016-9883-4 |
| format | Article |
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| subjects | Algorithms Analysis Computer science Conjugate gradient method Convergence Convex and Discrete Geometry Decomposition Euclidean space Management Science Manifolds Mathematical analysis Mathematical models Mathematics Mathematics and Statistics Methods Operations Research Operations Research/Decision Theory Optimization Statistics Studies Topological manifolds Transport |
| title | A Riemannian conjugate gradient method for optimization on the Stiefel manifold |
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