S
Sivaram Ambikasaran
Researcher at Indian Institute of Science
Publications - 35
Citations - 2295
Sivaram Ambikasaran is an academic researcher from Indian Institute of Science. The author has contributed to research in topics: Matrix (mathematics) & Solver. The author has an hindex of 17, co-authored 27 publications receiving 1742 citations. Previous affiliations of Sivaram Ambikasaran include Courant Institute of Mathematical Sciences & Mercer University.
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Fast and scalable Gaussian process modeling with applications to astronomical time series
TL;DR: In this paper, the covariance function is expressed as a mixture of complex exponentials, without requiring evenly spaced observations or uniform noise, which can be used for probabilistic inference of stellar rotation periods, asteroseismic oscillation spectra and transiting planet parameters.
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Fast Direct Methods for Gaussian Processes
TL;DR: In this paper, the authors show that for the most commonly used covariance functions, the matrix $C$ can be hierarchically factored into a product of block low-rank updates of the identity matrix, yielding an $\mathcal {O} (n\,\log^2, n)$ algorithm for inversion.
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Fast and scalable Gaussian process modeling with applications to astronomical time series
TL;DR: A novel method for Gaussian processes modeling in one dimension where the computational requirements scale linearly with the size of the data set, and is fast and interpretable, with a range of potential applications within astronomical data analysis and beyond.
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An $$\mathcal O (N \log N)$$O(NlogN) Fast Direct Solver for Partial Hierarchically Semi-Separable Matrices
Sivaram Ambikasaran,Eric Darve +1 more
TL;DR: The key ingredients behind this fast solver are recursion, efficient low rank factorization using Chebyshev interpolation, and the Sherman–Morrison–Woodbury formula.
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A fast block low-rank dense solver with applications to finite-element matrices
TL;DR: A fast solver for the dense "frontal" matrices that arise from the multifrontal sparse elimination process of 3D elliptic PDEs, using the HODLR direct solver as a preconditioner to the GMRES iterative scheme to reach machine accuracy much faster than a conventional LU solver.