A Greedy Subspace Method for Computing the ℒ∞‐Norm
PAMM, 2017•Wiley Online Library
We consider the computation of the ℒ∞‐norm for a general class of ℒ∞‐functions and focus
on the case where the function is represented in terms of large‐scale matrix‐valued factors.
We propose a subspace projection method to obtain reduced approximations of this function
by interpolation techniques. The ℒ∞‐norms are computed for the resulting reduced
functions, then the subspaces are refined by means of the optimizer of the ℒ∞‐norm of the
reduced function. In this way we obtain much better performance compared to existing …
on the case where the function is represented in terms of large‐scale matrix‐valued factors.
We propose a subspace projection method to obtain reduced approximations of this function
by interpolation techniques. The ℒ∞‐norms are computed for the resulting reduced
functions, then the subspaces are refined by means of the optimizer of the ℒ∞‐norm of the
reduced function. In this way we obtain much better performance compared to existing …
Abstract
We consider the computation of the ℒ∞‐norm for a general class of ℒ∞‐functions and focus on the case where the function is represented in terms of large‐scale matrix‐valued factors. We propose a subspace projection method to obtain reduced approximations of this function by interpolation techniques. The ℒ∞‐norms are computed for the resulting reduced functions, then the subspaces are refined by means of the optimizer of the ℒ∞‐norm of the reduced function. In this way we obtain much better performance compared to existing methods. (© 2017 Wiley‐VCH Verlag GmbH & Co. KGaA, Weinheim)
Wiley Online Library以上显示的是最相近的搜索结果。 查看全部搜索结果