In: Journal of computational science, 2021, vol. 53, p. 13
The ℓ1-regularized Gaussian maximum likelihood method is a common approach for sparse precision matrix estimation, but one that poses a computational challenge for high-dimensional datasets. We present a novel ℓ1- regularized maximum likelihood method for performant large-scale sparse precision matrix estimation utilizing the block structures in the underlying computations. We identify the...
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In: Entropy, 2019, vol. 21, no. 2, p. 104
In view of the importance of quantum non-locality in cryptography, quantum computation, and communication complexity, it is crucial to decide whether a given correlation exhibits non-locality or not. As proved by Pitowski, this problem is NP- complete, and is thus computationally intractable unless NP is equal to P. In this paper, we first prove that the Euclidean distance of given...
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In: Natural Computing, 2009, vol. 8, no. 2, p. 239-287
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