SIAM Journal on Numerical Analysis, Vol. 43, No. 3 (2006), pp. 1321-1347 (27 pages) This paper develops and investigates iterative tensor methods for solving large-scale systems of nonlinear equations ...
All the codes used to implement the different algorithms and produce the results found in the Tensor Methods in Statistics Essay may be found in this repository. For examples 1 and 2, we used R and ...
Robust tensor CP decomposition involves decomposing a tensor into low rank and sparse components. We propose a novel non-convex iterative algorithm with guaranteed recovery. It alternates between ...
TensorLy makes it easy to handle tensor decomposition, tensor learning and tensor algebra. It’s robust backend system enables users to perform computations with NumPy, TensorFlow, PyTorch, MXNet, CuPy ...
Abstract: The majority of current dimension reduction methods are restricted to the use of spectral information, when the spatial information is left out. In order to overcome this defect, two ...
In this project we implement two tensor methods for the minimization of p-th order smooth convex functions. We compare the performance of the accelerated tensor method proposed in (Nesterov,2018), and ...
Abstract: Tensor methods have become a promising tool to solve high-dimensional problems in the big data era. By exploiting possible low-rank tensor factorization, many high-dimensional model-based or ...
1 Department of Electrical and Computer Engineering, University of California, Santa Barbara, Santa Barbara, CA, United States 2 Department of Mathematics, University of California, Santa Barbara, ...
Represents a tensor t∈R n 0 ×n 1 ×…×n r-1 of dimensions n 0 ×n 1 ×…×n r-1 and rank r. If you intend to use mutable arithmetic elements, note the discussion of mutations per reference vs. explicit ...
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