Randomized algorithms for computation of Tucker decomposition and higher order SVD (HOSVD)
Big data analysis has become a crucial part of new emerging technologies such as the
internet of things, cyber-physical analysis, deep learning, anomaly detection, etc. Among …
internet of things, cyber-physical analysis, deep learning, anomaly detection, etc. Among …
Randomized algorithms for low-rank tensor decompositions in the Tucker format
R Minster, AK Saibaba, ME Kilmer - SIAM journal on mathematics of data …, 2020 - SIAM
Many applications in data science and scientific computing involve large-scale datasets that
are expensive to store and manipulate. However, these datasets possess inherent …
are expensive to store and manipulate. However, these datasets possess inherent …
Efficient quantum circuits for accurate state preparation of smooth, differentiable functions
A Holmes, AY Matsuura - 2020 IEEE International Conference …, 2020 - ieeexplore.ieee.org
Effective quantum computation relies upon making good use of the exponential information
capacity of a quantum machine. A large barrier to designing quantum algorithms for …
capacity of a quantum machine. A large barrier to designing quantum algorithms for …
Randomized algorithms for rounding in the tensor-train format
The tensor-train (TT) format is a highly compact low-rank representation for high-
dimensional tensors. TT is particularly useful when representing approximations to the …
dimensional tensors. TT is particularly useful when representing approximations to the …
Randomized algorithms for fast computation of low rank tensor ring model
Randomized algorithms are efficient techniques for big data tensor analysis. In this tutorial
paper, we review and extend a variety of randomized algorithms for decomposing large …
paper, we review and extend a variety of randomized algorithms for decomposing large …
Faster tensor train decomposition for sparse data
In recent years, the application of tensors has become more widespread in fields that involve
data analytics and numerical computation. Due to the explosive growth of data, low-rank …
data analytics and numerical computation. Due to the explosive growth of data, low-rank …
Cost-efficient Gaussian tensor network embeddings for tensor-structured inputs
L Ma, E Solomonik - Advances in Neural Information …, 2022 - proceedings.neurips.cc
This work discusses tensor network embeddings, which are random matrices ($ S $) with
tensor network structure. These embeddings have been used to perform dimensionality …
tensor network structure. These embeddings have been used to perform dimensionality …
An object-oriented optimization framework for large-scale inverse problems
We present an object-oriented optimization framework that can be employed to solve small-
and large-scale problems based on the concept of vectors and operators. By using such a …
and large-scale problems based on the concept of vectors and operators. By using such a …
Low-rank tensor train decomposition using tensorsketch
Tensor train decomposition is one of the most powerful approaches for processing high-
dimensional data. For low-rank tensor train decomposition of large tensors, the alternating …
dimensional data. For low-rank tensor train decomposition of large tensors, the alternating …
Comment on" Controlled Bond Expansion for Density Matrix Renormalization Group Ground State Search at Single-Site Costs"(Extended Version)
IP McCulloch, JJ Osborne - arXiv preprint arXiv:2403.00562, 2024 - arxiv.org
In a recent Letter [Phys. Rev. Lett. 130, 246402 (2023)], Gleis, Li, and von Delft present an
algorithm for expanding the bond dimension of a Matrix Product State wave function, giving …
algorithm for expanding the bond dimension of a Matrix Product State wave function, giving …