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Moreover, we propose an efficient implementation, whereby the shape matrix of the lifted ellipsoid is stored in sparse format, and every atom operation corresponds to a sparse update in that matrix.
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In particular, we are able to vastly speed up the solution time through efficient variable reordering and incremental sparse updates, which we believe will greatly increase the practicality of Gaussian process methods for robot mapping and localization.
We present an adaptive method which compresses the problem very efficiently by representing the solution in a sparse wavelet basis that is updated in each step.
First, an existing batch-based structural modeling is applied to a sparse point cloud that are incrementally updated on SLAM.
The main idea is that, in the next step, Y is fixed, and the inner product A T A, rather than A itself, is updated; in the next step, Y is updated while fixing A T A (a sparse coding procedure).
Most of the existing methods for solving Equation 1 can be essentially interpreted as different generalizations of the K-means clustering algorithm because they usually have two-step iterative approaches consisting of a sparse coding step where sparse approximations X is found with A fixed and a dictionary update step where A is optimized based on the current X[1].
Another notable approach of tracking a sparse signal in an underdetermined measurement scenario is viewing sparsity as a pseudo-measurement and implementing a parallel state and covariance update scheme for this extra measurement [18].
A sparse classic.
It's a sparse but intriguing catalog.
Together they evoke a sparse domestic interior.
Everything is heightened on such a sparse canvas.
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Since I tried Ludwig back in 2017, I have been constantly using it in both editing and translation. Ever since, I suggest it to my translators at ProSciEditing.

Justyna Jupowicz-Kozak
CEO of Professional Science Editing for Scientists @ prosciediting.com