Journal
COMPUTATIONAL OPTIMIZATION AND APPLICATIONS
Volume 67, Issue 1, Pages 73-110Publisher
SPRINGER
DOI: 10.1007/s10589-016-9883-4
Keywords
Riemannian optimization; Stiefelmanifold; Conjugate gradient method; Retraction; Vector transport; Cayley transform
Funding
- National Natural Science Foundation of China [11601317, 11526135]
- University Young Teachers' Training Scheme of Shanghai [ZZsdl15124]
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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.
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