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However, this increases a risk of increasing the mutual coherence among the columns of the dictionary matrix to an extent where sparse reconstruction becomes impractical or infeasible.
In this paper, we focus on the question of how to securely outsource the characteristic polynomial and eigenvalues of a matrix to an untrusted server in the cloud.
We can apply Laplace's formula for column 1, then for column 2,... up to column m - 1, to reduce the matrix to an m-diagonal matrix of size N - m.
The term "pattern recognition" can be defined as the transformation of an input data array (for example, responses from the sensor matrix) to an output data array (for example, the type of adsorbates, their concentration).
Generally, an LS code can be denoted as LS (N,P,W 0), which denotes the family of LS codes generated by applying a (P×P -dimensional Walsh-Hadamard (WH) matrix to an orthogonal comP×P -dimensionalset of length N and inserting W 0 number of zeros between two orthogonal complementary sets [6, 9, 10].
Our contributions include: (1) a checkpointing and recovery mechanism for the left-factor Q whose performance is scalable on hybrid systems; (2) optimized Givens rotation utilities on GPGPUs to efficiently reduce an upper Hessenberg matrix to an upper triangular form for the protection of the right factor R; and (3) a recovery algorithm based on QR update on GPGPUs.
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Fig. 1 Transforming a similarity matrix to a similarity network.
In this scenario, the hit-matrix reduces from a matrix to a array.
Inconsistency is based on the distance of the matrix to a special matrix measured by a particular metric.
Basically, these methods try to fit the covariance matrix to a certain model.
The algorithm aims at partitioning the process' occurrence matrix to a specific block lower-triangular form.
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Justyna Jupowicz-Kozak
CEO of Professional Science Editing for Scientists @ prosciediting.com