CP decomposition and low-rank approximation of antisymmetric tensors

12/27/2022
by   Erna Begovic, et al.
0

For the antisymmetric tensors the paper examines a low-rank approximation which is represented via only three vectors. We describe a suitable low-rank format and propose an alternating least squares structure-preserving algorithm for finding such approximation. The case of partial antisymmetry is also discussed. The algorithms are implemented in Julia programming language and their numerical performance is discussed.

READ FULL TEXT

page 1

page 2

page 3

page 4

research
11/05/2021

The low-rank approximation of fourth-order partial-symmetric and conjugate partial-symmetric tensor

We present an orthogonal matrix outer product decomposition for the four...
research
05/24/2019

Learning Low-Rank Approximation for CNNs

Low-rank approximation is an effective model compression technique to no...
research
12/04/2019

Finding entries of maximum absolute value in low-rank tensors

We present an iterative method for the search of extreme entries in low-...
research
04/23/2021

Low Rank Approximation in Simulations of Quantum Algorithms

Simulating quantum algorithms on classical computers is challenging when...
research
09/20/2017

Near Optimal Sketching of Low-Rank Tensor Regression

We study the least squares regression problem _Θ∈S_ D,RAΘ-b_2, where S_...
research
09/15/2022

A new Kernel Regression approach for Robustified L_2 Boosting

We investigate L_2 boosting in the context of kernel regression. Kernel ...
research
12/26/2013

Language Modeling with Power Low Rank Ensembles

We present power low rank ensembles (PLRE), a flexible framework for n-g...

Please sign up or login with your details

Forgot password? Click here to reset