185 research outputs found
Symmetric tensor decomposition
We present an algorithm for decomposing a symmetric tensor, of dimension n
and order d as a sum of rank-1 symmetric tensors, extending the algorithm of
Sylvester devised in 1886 for binary forms. We recall the correspondence
between the decomposition of a homogeneous polynomial in n variables of total
degree d as a sum of powers of linear forms (Waring's problem), incidence
properties on secant varieties of the Veronese Variety and the representation
of linear forms as a linear combination of evaluations at distinct points. Then
we reformulate Sylvester's approach from the dual point of view. Exploiting
this duality, we propose necessary and sufficient conditions for the existence
of such a decomposition of a given rank, using the properties of Hankel (and
quasi-Hankel) matrices, derived from multivariate polynomials and normal form
computations. This leads to the resolution of polynomial equations of small
degree in non-generic cases. We propose a new algorithm for symmetric tensor
decomposition, based on this characterization and on linear algebra
computations with these Hankel matrices. The impact of this contribution is
two-fold. First it permits an efficient computation of the decomposition of any
tensor of sub-generic rank, as opposed to widely used iterative algorithms with
unproved global convergence (e.g. Alternate Least Squares or gradient
descents). Second, it gives tools for understanding uniqueness conditions, and
for detecting the rank
Symmetric Tensor Decomposition by an Iterative Eigendecomposition Algorithm
We present an iterative algorithm, called the symmetric tensor eigen-rank-one
iterative decomposition (STEROID), for decomposing a symmetric tensor into a
real linear combination of symmetric rank-1 unit-norm outer factors using only
eigendecompositions and least-squares fitting. Originally designed for a
symmetric tensor with an order being a power of two, STEROID is shown to be
applicable to any order through an innovative tensor embedding technique.
Numerical examples demonstrate the high efficiency and accuracy of the proposed
scheme even for large scale problems. Furthermore, we show how STEROID readily
solves a problem in nonlinear block-structured system identification and
nonlinear state-space identification
Generating Polynomials and Symmetric Tensor Decompositions
This paper studies symmetric tensor decompositions. For symmetric tensors,
there exist linear relations of recursive patterns among their entries. Such a
relation can be represented by a polynomial, which is called a generating
polynomial. The homogenization of a generating polynomial belongs to the apolar
ideal of the tensor. A symmetric tensor decomposition can be determined by a
set of generating polynomials, which can be represented by a matrix. We call it
a generating matrix. Generally, a symmetric tensor decomposition can be
determined by a generating matrix satisfying certain conditions. We
characterize the sets of such generating matrices and investigate their
properties (e.g., the existence, dimensions, nondefectiveness). Using these
properties, we propose methods for computing symmetric tensor decompositions.
Extensive examples are shown to demonstrate the efficiency of proposed methods.Comment: 35 page
Symmetric Tensor Decomposition Description of Fermionic Many-Body Wavefunctions
The configuration interaction (CI) is a versatile wavefunction theory for
interacting fermions but it involves an extremely long CI series. Using a
symmetric tensor decomposition (STD) method, we convert the CI series into a
compact and numerically tractable form. The converted series encompasses the
Hartree-Fock state in the first term and rapidly converges to the full-CI
state, as numerically tested using small molecules. Provided that the length of
the STD-CI series grows only moderately with the increasing complexity of the
system, the new method will serve as one of the alternative variational methods
to achieve full-CI with enhanced practicability.Comment: 10 pages, 6 figure
Symmetric tensor decomposition
International audienceWe present an algorithm for decomposing a symmetric tensor of dimension n and order d as a sum of of rank-1 symmetric tensors, extending the algorithm of Sylvester devised in 1886 for symmetric tensors of dimension 2. We exploit the known fact that every symmetric tensor is equivalently represented by a homogeneous polynomial in n variables of total degree d. Thus the decomposition corresponds to a sum of powers of linear forms. The impact of this contribution is two-fold. First it permits an efficient computation of the decomposition of any tensor of sub-generic rank, as opposed to widely used iterative algorithms with unproved convergence (e.g. Alternate Least Squares or gradient descents). Second, it gives tools for understanding uniqueness conditions, and for detecting the tensor rank
Numerical Optimization for Symmetric Tensor Decomposition
We consider the problem of decomposing a real-valued symmetric tensor as the
sum of outer products of real-valued vectors. Algebraic methods exist for
computing complex-valued decompositions of symmetric tensors, but here we focus
on real-valued decompositions, both unconstrained and nonnegative, for problems
with low-rank structure. We discuss when solutions exist and how to formulate
the mathematical program. Numerical results show the properties of the proposed
formulations (including one that ignores symmetry) on a set of test problems
and illustrate that these straightforward formulations can be effective even
though the problem is nonconvex
Skew-Symmetric Tensor Decomposition
International audienceWe introduce the ``skew apolarity lemma" and we use it to give algorithms for the skew-symmetric rank and the decomposition of tensors in { with and }. New algorithms to compute the rank and a minimal decomposition of a tri-tensor are also presented
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