Thomas Huckle
Current Organizations / Institutes
The organizations/institutes from the scholar's latest article
Research Subjects
Research Interests
Thomas Huckle has a total of 20 co-authors. Here are the top co-authors by number of shared publications:
- Senior System Analyst Stefan Schneider
Axxom Software AG
29 shared publications - Marco Donatelli
Università dell’Insubria
2 shared publications - Matous Sedlacek
Technische Universität München
2 shared publications - A. Kallischko
Fakultät für Informatik, Technische Universität München , Boltzmannstr. 3, D-85748, Garching, Germany
1 shared publication - Bruno Lang
University of Wuppertal
1 shared publication Christos D. Kravvaritis
Department of Mathematics, University of Athens, Athens, Greece
1 shared publication
Latest Publications
- 1 October 2019
- Published by Elsevier in Parallel Computing
- Vol. 88, 102542
Parallel eigenvalue computation for banded generalized eigenvalue problems
- 1 October 2019
- Published by Elsevier in Parallel Computing
- Vol. 88, 102542
Parallel eigenvalue computation for banded generalized eigenvalue problems
- Open Access
Towards a better understanding of the matrix product function approximation algorithm in application to quantum physics
We recently introduced a method to approximate functions of Hermitian Matrix Product Operators or Tensor Trains that are of the form $\mathsf{Tr} f(A)$. Functions of this type occur in several applications, most notably in quantum physics. In this work we aim at extending the theoretical understanding of our method by showing several properties of our algorithm that can be used to detect and correct errors in its results. Most importantly, we show that there exists a more computationally efficient version of our algorithm for certain inputs. To illustrate the usefulness of our finding, we prove that several classes of spin Hamiltonians in quantum physics fall into this input category. We finally support our findings with numerical results obtained for an example from quantum physics.Open AccessTowards a better understanding of the matrix product function approximation algorithm in application to quantum physics
We recently introduced a method to approximate functions of Hermitian Matrix Product Operators or Tensor Trains that are of the form $\mathsf{Tr} f(A)$. Functions of this type occur in several applications, most notably in quantum physics. In this work we aim at extending the theoretical understanding of our method by showing several properties of our algorithm that can be used to detect and correct errors in its results. Most importantly, we show that there exists a more computationally efficient version of our algorithm for certain inputs. To illustrate the usefulness of our finding, we prove that several classes of spin Hamiltonians in quantum physics fall into this input category. We finally support our findings with numerical results obtained for an example from quantum physics. - 26 October 2016
- Published by Taylor & Francis in Linear and Multilinear Algebra
- Vol. 65 (8) , 1629-1642
Properties of submatrices of Sylvester Hadamard matrices
We investigate the algebraic behaviour of leading principal submatrices of Hadamard matrices being powers of 2. We provide analytically the spectrum of general submatrices of these Hadamard matrices. Symmetry properties and relationships between the upper left and lower right corners of the matrices in this respect are demonstrated. Considering the specific construction scheme of this particular class of Hadamard matrices (called Sylvester Hadamard matrices), we utilize tensor operations to prove the respective results. An algorithmic procedure yielding the complete spectrum of leading principal submatrices of Sylvester Hadamard matrices is proposed.
- 26 October 2016
- Published by Taylor & Francis in Linear and Multilinear Algebra
- Vol. 65 (8) , 1629-1642
Properties of submatrices of Sylvester Hadamard matrices
We investigate the algebraic behaviour of leading principal submatrices of Hadamard matrices being powers of 2. We provide analytically the spectrum of general submatrices of these Hadamard matrices. Symmetry properties and relationships between the upper left and lower right corners of the matrices in this respect are demonstrated. Considering the specific construction scheme of this particular class of Hadamard matrices (called Sylvester Hadamard matrices), we utilize tensor operations to prove the respective results. An algorithmic procedure yielding the complete spectrum of leading principal submatrices of Sylvester Hadamard matrices is proposed.
- 23 September 2015
- Published by Springer Nature in Numerical Algorithms
- Vol. 72 (2) , 341-361
Image deblurring by sparsity constraint on the Fourier coefficients
- 23 September 2015
- Published by Springer Nature in Numerical Algorithms
- Vol. 72 (2) , 341-361
Image deblurring by sparsity constraint on the Fourier coefficients
- 29 July 2014
- Published by Springer Nature in BIT Numerical Mathematics
- Vol. 55 (2) , 341-366
Generalized grid transfer operators for multigrid methods applied on Toeplitz matrices
- 29 July 2014
- Published by Springer Nature in BIT Numerical Mathematics
- Vol. 55 (2) , 341-366
Generalized grid transfer operators for multigrid methods applied on Toeplitz matrices
- 25 January 2013
- Published by Springer Nature in BIT Numerical Mathematics
- Vol. 53 (2) , 459-473
Data based regularization for discrete deconvolution problems
- 25 January 2013
- Published by Springer Nature in BIT Numerical Mathematics
- Vol. 53 (2) , 459-473
Data based regularization for discrete deconvolution problems
- Open Access
Computations in Quantum Tensor Networks
The computation of the ground state (i.e. the eigenvector related to the smallest eigenvalue) is an important task in the simulation of quantum many-body systems. As the dimension of the underlying vector space grows exponentially in the number of particles, one has to consider appropriate subsets promising both convenient approximation properties and efficient computations. The variational ansatz for this numerical approach leads to the minimization of the Rayleigh quotient. The Alternating Least Squares technique is then applied to break down the eigenvector computation to problems of appropriate size, which can be solved by classical methods. Efficient computations require fast computation of the matrix-vector product and of the inner product of two decomposed vectors. To this end, both appropriate representations of vectors and efficient contraction schemes are needed. Here approaches from many-body quantum physics for one-dimensional and two-dimensional systems (Matrix Product States and Projected Entangled Pair States) are treated mathematically in terms of tensors. We give the definition of these concepts, bring some results concerning uniqueness and numerical stability and show how computations can be executed efficiently within these concepts. Based on this overview we present some modifications and generalizations of these concepts and show that they still allow efficient computations such as applicable contraction schemes. In this context we consider the minimization of the Rayleigh quotient in terms of the {\sc parafac} (CP) formalism, where we also allow different tensor partitions. This approach makes use of efficient contraction schemes for the calculation of inner products in a way that can easily be extended to the mps format but also to higher dimensional problems.
Open AccessComputations in Quantum Tensor Networks
The computation of the ground state (i.e. the eigenvector related to the smallest eigenvalue) is an important task in the simulation of quantum many-body systems. As the dimension of the underlying vector space grows exponentially in the number of particles, one has to consider appropriate subsets promising both convenient approximation properties and efficient computations. The variational ansatz for this numerical approach leads to the minimization of the Rayleigh quotient. The Alternating Least Squares technique is then applied to break down the eigenvector computation to problems of appropriate size, which can be solved by classical methods. Efficient computations require fast computation of the matrix-vector product and of the inner product of two decomposed vectors. To this end, both appropriate representations of vectors and efficient contraction schemes are needed. Here approaches from many-body quantum physics for one-dimensional and two-dimensional systems (Matrix Product States and Projected Entangled Pair States) are treated mathematically in terms of tensors. We give the definition of these concepts, bring some results concerning uniqueness and numerical stability and show how computations can be executed efficiently within these concepts. Based on this overview we present some modifications and generalizations of these concepts and show that they still allow efficient computations such as applicable contraction schemes. In this context we consider the minimization of the Rayleigh quotient in terms of the {\sc parafac} (CP) formalism, where we also allow different tensor partitions. This approach makes use of efficient contraction schemes for the calculation of inner products in a way that can easily be extended to the mps format but also to higher dimensional problems.
- Open Access
- 3 December 2012
- Published by Wiley in Proceedings in Applied Mathematics and Mechanics
- Vol. 12 (1) , 643-644
Data Based Regularization Matrices for the Tikhonov-Phillips Regularization
- 3 December 2012
- Published by Wiley in Proceedings in Applied Mathematics and Mechanics
- Vol. 12 (1) , 643-644
Open AccessData Based Regularization Matrices for the Tikhonov-Phillips Regularization
- 27 April 2012
- Published by Springer Nature in Numerical Algorithms
- Vol. 60 (2) , 339-353
Tikhonov–Phillips regularization with operator dependent seminorms
- 27 April 2012
- Published by Springer Nature in Numerical Algorithms
- Vol. 60 (2) , 339-353
Tikhonov–Phillips regularization with operator dependent seminorms
- 1 January 2010
- Published by Springer Nature
- p. 39-50
Fast 3D Block Parallelisation for the Matrix Multiplication Prefix Problem
- 1 January 2010
- Published by Springer Nature
- p. 39-50
Fast 3D Block Parallelisation for the Matrix Multiplication Prefix Problem