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Convergence rates of training deep neural networks via alternating minimization methods
Training deep neural networks (DNNs) is an important and challenging optimization problem in machine learning due to its non-convexity and non-separable structure. The alternating minimization (AM) approaches ...
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A semismooth Newton based augmented Lagrangian method for nonsmooth optimization on matrix manifolds
This paper is devoted to studying an augmented Lagrangian method for solving a class of manifold optimization problems, which have nonsmooth objective functions and nonlinear constraints. Under the constant po...
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Duality for \(\alpha \) -Möbius invariant Besov spaces
For \(1\le p\le \infty \) 1 ≤ p ...
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Carleson measures and the range of a Cesàro-like operator acting on \(H^\infty \)
In this paper, we determine the range of a Cesàro-like operator acting on \(H^\infty \) ...
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\({{\cal Q}_K}\) Spaces: A Brief and Selective Survey
This article traces several prominent trends in the development of Mobius invariant function spaces \({{\cal Q}_K}\) ...
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Closure in the Logarithmic Bloch Norm of Dirichlet Type Spaces
In this paper, for every \(\alpha \in \mathbb {R}\) α ∈ ...
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Hankel Measures for Hardy Spaces
In this paper, we study the so-called Hankel measures on the open unit disk. We obtain several new characterizations for such measures and answer a question raised by J. **ao in 2000.
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On the Closures of Dirichlet Type Spaces in the Bloch Space
In this paper, via high order derivatives and via embedding derivatives of Bloch type functions into Lebesgue spaces, we characterize the closures of Dirichlet type spaces in the Bloch space. We obtain the inc...
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On Multipliers of Dirichlet Type Spaces
Let \(M(\mathcal {D}_K)\) M ...
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Improving multipliers and zero sets in \(\mathcal {Q}_K\) spaces
Let \(X\) X and...