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- W3162853669 abstract "Optimization problem with orthogonality constraints, whose feasible region is called the Stiefel manifold, has rich applications in data sciences. The severe non-linearity of the Stiefel manifold has hindered the utilization of optimization mechanisms developed specially over a vector space for the problem. In this paper, we present a global parametrization of the Stiefel manifold entirely by a single fixed vector space with the Cayley transform, say Global Cayley Parametrization (G-CP), to solve the problem through optimization over a vector space. The G-CP has key properties for solving the problem with G-CP and for applications to orthogonality constraint stochastic/distributed optimization problems. A numerical experiment shows that G-CP strategy outperforms the standard strategy with a retraction [Absil-Mahony-Sepulchre, 08]." @default.
- W3162853669 created "2021-05-24" @default.
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- W3162853669 date "2021-06-06" @default.
- W3162853669 modified "2023-10-16" @default.
- W3162853669 title "A Global Cayley Parametrization of Stiefel Manifold for Direct Utilization of Optimization Mechanisms Over Vector Spaces" @default.
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- W3162853669 doi "https://doi.org/10.1109/icassp39728.2021.9414157" @default.
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