Zobrazeno 1 - 10
of 1 224
pro vyhledávání: '"Saibaba, A."'
Autor:
Ipsen, Ilse C. F., Saibaba, Arvind K.
We compare the properties of the stable rank and intrinsic dimension of real and complex matrices to those of the classical rank. Basic proofs and examples illustrate that the stable rank does not satisfy any of the fundamental rank properties, while
Externí odkaz:
http://arxiv.org/abs/2407.21594
Autor:
Khan, Abraham, Saibaba, Arvind K.
Computing low-rank approximations of kernel matrices is an important problem with many applications in scientific computing and data science. We propose methods to efficiently approximate and store low-rank approximations to kernel matrices that depe
Externí odkaz:
http://arxiv.org/abs/2406.06344
This paper tackles optimal sensor placement for Bayesian linear inverse problems, a popular version of the more general Optimal Experiment Design (OED) problem, using the D-optimality criterion. This is done by establishing connections between sensor
Externí odkaz:
http://arxiv.org/abs/2402.16000
Strong Constraint 4D Variational (SC-4DVAR) is a data assimilation method that is widely used in climate and weather applications. SC-4DVAR involves solving a minimization problem to compute the maximum a posteriori estimate, which we tackle using th
Externí odkaz:
http://arxiv.org/abs/2401.15758
We study Bayesian methods for large-scale linear inverse problems, focusing on the challenging task of hyperparameter estimation. Typical hierarchical Bayesian formulations that follow a Markov Chain Monte Carlo approach are possible for small proble
Externí odkaz:
http://arxiv.org/abs/2311.15827
Autor:
Saibaba, Arvind K., Międlar, Agnieszka
This paper expands the analysis of randomized low-rank approximation beyond the Gaussian distribution to four classes of random matrices: (1) independent sub-Gaussian entries, (2) independent sub-Gaussian columns, (3) independent bounded columns, and
Externí odkaz:
http://arxiv.org/abs/2308.05814
Autor:
Antil, Harbir, Saibaba, Arvind K.
This paper is interested in developing reduced order models (ROMs) for repeated simulation of fractional elliptic partial differential equations (PDEs) for multiple values of the parameters (e.g., diffusion coefficients or fractional exponent) govern
Externí odkaz:
http://arxiv.org/abs/2306.16148
Autor:
Jayaram Saibaba, Gopinath Karuppiah
Publikováno v:
National Board of Examinations Journal of Medical Sciences, Vol Volume 2, Iss Issue 9, Pp 933-935 (2024)
A 60-year-old woman from Cuddalore presented with restricted neck movements, progressive difficulty in walking, and lower limb weakness, leading to bed confinement. Clinical examination revealed spastic paraparesis, brisk reflexes, and sensory defici
Externí odkaz:
https://doaj.org/article/f18c119007be45288bc7ae3b093bd330
Autor:
Reese, William, Hart, Joseph, Waanders, Bart van Bloemen, Perego, Mauro, Jakeman, John, Saibaba, Arvind
Inverse problems constrained by partial differential equations (PDEs) play a critical role in model development and calibration. In many applications, there are multiple uncertain parameters in a model which must be estimated. Although the Bayesian f
Externí odkaz:
http://arxiv.org/abs/2212.12386
Reconstructing high-resolution flow fields from sparse measurements is a major challenge in fluid dynamics. Existing methods often vectorize the flow by stacking different spatial directions on top of each other, hence confounding the information enc
Externí odkaz:
http://arxiv.org/abs/2208.09875