Invariant Matrix Research Articles

Invariant correlation under marginal transforms

A useful property of independent samples is that their correlation remains the same after applying marginal transforms. This invariance property plays a fundamental role in statistical inference, but does not hold in general for dependent samples. In this paper, we study this invariance property on the Pearson correlation coefficient and its applications. A multivariate random vector is said to have an invariant correlation if its pairwise correlation coefficients remain unchanged under any common marginal transforms. For a bivariate case, we characterize all models of such a random vector via a certain combination of comonotonicity—the strongest form of positive dependence—and independence. In particular, we show that the class of exchangeable copulas with invariant correlation is precisely described by what we call positive Fréchet copulas. In the general multivariate case, we characterize the set of all invariant correlation matrices via the clique partition polytope. We also propose a positive regression dependent model that admits any prescribed invariant correlation matrix. Finally, we show that all our characterization results of invariant correlation, except one special case, remain the same if the common marginal transforms are confined to the set of increasing ones.

Gauged permutation invariant matrix quantum mechanics: partition functions

The Hilbert spaces of matrix quantum mechanical systems with N × N matrix degrees of freedom X have been analysed recently in terms of SN symmetric group elements U acting as X → UXUT. Solvable models have been constructed uncovering partition algebras as hidden symmetries of these systems. The solvable models include an 11-dimensional space of matrix harmonic oscillators, the simplest of which is the standard matrix harmonic oscillator with U(N) symmetry. The permutation symmetry is realised as gauge symmetry in a path integral formulation in a companion paper. With the simplest matrix oscillator Hamiltonian subject to gauge permutation symmetry, we use the known result for the micro-canonical partition function to derive the canonical partition function. It is expressed as a sum over partitions of N of products of factors which depend on elementary number-theoretic properties of the partitions, notably the least common multiples and greatest common divisors of pairs of parts appearing in the partition. This formula is recovered using the Molien-Weyl formula, which we review for convenience. The Molien-Weyl formula is then used to generalise the formula for the canonical partition function to the 11-parameter permutation invariant matrix harmonic oscillator.

Out-of-time ordered correlators in kicked coupled tops: Information scrambling in mixed phase space and the role of conserved quantities

We study operator growth in a bipartite kicked coupled tops (KCTs) system using out-of-time ordered correlators (OTOCs), which quantify “information scrambling” due to chaotic dynamics and serve as a quantum analog of classical Lyapunov exponents. In the KCT system, chaos arises from the hyper-fine coupling between the spins. Due to a conservation law, the system’s dynamics decompose into distinct invariant subspaces. Focusing initially on the largest subspace, we numerically verify that the OTOC growth rate aligns well with the classical Lyapunov exponent for fully chaotic dynamics. While previous studies have largely focused on scrambling in fully chaotic dynamics, works on mixed-phase space scrambling are sparse. We explore scrambling behavior in both mixed-phase space and globally chaotic dynamics. In the mixed-phase space, we use Percival’s conjecture to partition the eigenstates of the Floquet map into “regular” and “chaotic.” Using these states as the initial states, we examine how their mean phase space locations affect the growth and saturation of the OTOCs. Beyond the largest subspace, we study the OTOCs across the entire system, including all other smaller subspaces. For certain initial operators, we analytically derive the OTOC saturation using random matrix theory (RMT). When the initial operators are chosen randomly from the unitarily invariant random matrix ensembles, the averaged OTOC relates to the linear entanglement entropy of the Floquet operator, as found in earlier works. For the diagonal Gaussian initial operators, we provide a simple expression for the OTOC.

Convergence analysis of a synchronous gradient estimation scheme for time-varying parameter systems

For the problem of time-varying parameter estimation and its convergence, this paper proposes an invariant matrix to represent the changing laws of time-varying parameters, which suggests a novel expression to describe the parameters by employing finite terms. A joint recursive least squares algorithm is derived by minimizing the criterion function associated with the unknown invariant matrix. For a class of time-varying systems, a joint stochastic gradient algorithm with the computational efficiency is obtained by analogy to achieve the estimation of invariant matrix and system parameters. The convergence analysis is explored to demonstrate that the time-varying parameter estimation error is bounded under the persistent excitation conditions. The simulation results also validate the effectiveness of the proposed synchronous parameter estimation approach.

Hofer's metric in compact Lie groups

In this article, we study the Hofer geometry of a compact Lie group K which acts by Hamiltonian diffeomorphisms on a symplectic manifold M . Generalized Hofer norms on the Lie algebra of K are introduced and analyzed with tools from group invariant convex geometry, functional and matrix analysis. Several global results on the existence of geodesics and their characterization in finite-dimensional Lie groups K endowed with bi-invariant Finsler metrics are proved. We relate the conditions for being a geodesic in the group K and in the group of Hamiltonian diffeomorphisms. These results are applied to obtain necessary and sufficient conditions on the moment polytope of the momentum map, for the commutativity of the Hamiltonians of geodesics. Particular cases are studied, where a generalized non-crossing of eigenvalues property of the Hamiltonians hold.

Admissible transformation approach to Roesser state‐space model realization of singular multidimensional systems

AbstractThe problem of the singular Roesser (state‐space) model realization of non‐causal multivariate transfer (function) matrices is investigated. Specifically, the notion of so‐called admissible transformation is introduced, which allows to introduce and investigate a novel invariant matrix relationship on polynomial matrices. The singular multidimensional Roesser model (SNDRM) realization is transformed to the problem of how to reduce an initial polynomial matrix associated with the given transfer matrix into the objective polynomial matrix with a specific structure. Construction procedures are developed to obtain SNDRMs by applying admissible transformations on the initial polynomial matrix. Moreover, a separated standard form is introduced for an SNDRM so that the proposed approach can effectively treat both the left and right matrix fractional descriptions of a non‐causal multivariate transfer matrix in a unified manner. Non‐trivial examples are provided to illustrate the details and effectiveness of the proposed approach.

Solvable random-matrix ensemble with a logarithmic weakly confining potential.

This work identifies a solvable (in the sense that spectral correlation functions can be expressed in terms of orthogonal polynomials), rotationally invariant random matrix ensemble with a logarithmic weakly confining potential. The ensemble, which can be interpreted as a transformed Jacobi ensemble, is in the thermodynamic limit characterized by a Lorentzian eigenvalue density. It is shown that spectral correlation functions can be expressed in terms of the nonclassical Gegenbauer polynomials C_{n}^{(-1/2)}(x) with n≥2, which have been proven to form a complete orthogonal set with respect to the proper weight function. A procedure to sample matrices from the ensemble is outlined and used to provide a numerical verification for some of the analytical results. This ensemble is pointed out to potentially have applications in quantum many-body physics.

Bilinear character correlators in superintegrable theory

We continue investigating the superintegrability property of matrix models, i.e. factorization of the matrix model averages of characters. This paper focuses on the Gaussian Hermitian example, where the role of characters is played by the Schur functions. We find a new intriguing corollary of superintegrability: factorization of an infinite set of correlators bilinear in the Schur functions. More exactly, these are correlators of products of the Schur functions and polynomials K_Delta that form a complete basis in the space of invariant matrix polynomials. Factorization of these correlators with a small subset of these K_Delta follow from the fact that the Schur functions are eigenfunctions of the generalized cut-an-join operators, but the full set of K_Delta is generated by another infinite commutative set of operators, which we manifestly describe.

Ab initio no-core shell-model description of C10–14 isotopes

We present a systematic study of the $^{10--14}\mathrm{C}$ isotopes within the ab initio no-core shell-model theory. We apply four different realistic nucleon-nucleon $(NN)$ interactions: (i) the charge-dependent Bonn 2000 (CDB2K) potential; (ii) the inside nonlocal outside Yukawa (INOY) potential; (iii) the next-to-next-to-next-to-leading order $({\mathrm{N}}^{3}\mathrm{LO})$ potential; and (iv) the optimized next-to-next-to-leading order $({\mathrm{N}}^{2}{\mathrm{LO}}_{\mathrm{opt}})$ potential. We report the low-lying energy spectra of both positive- and negative-parity states for $^{10--14}\mathrm{C}$ isotopes and investigate the level structures. We also calculate electromagnetic properties such as transition strengths, quadrupole moments, and magnetic moments. The dependence of point-proton radii on the harmonic-oscillator frequency and basis space is shown. We present calculations of the translation invariant one-body density matrix in the no-core shell-model and discuss isotopic trends in the density distribution. The maximum basis space reached is $10\ensuremath{\hbar}\mathrm{\ensuremath{\Omega}}$ for $^{10}\mathrm{C}$ and $8\ensuremath{\hbar}\mathrm{\ensuremath{\Omega}}$ for $^{11--14}\mathrm{C}$, with a maximum M-scheme dimension of $1.3\ifmmode\times\else\texttimes\fi{}{10}^{9}$ for $^{10}\mathrm{C}$. We found that, while the INOY interaction gives the best description of the ground-state energies, the ${\mathrm{N}}^{3}\mathrm{LO}$ interaction best reproduces the point-proton radii.

Ellipsoidal Design of Robust Stabilization of Power Systems Exposed to a Cycle of Lightning Surges Modeled by Continuous-Time Markov Jumps

Power system stability is greatly affected by two types of stochastic or random disturbances: (1) topological and (2) parametric. The topological stochastic disturbances due to line faults caused by a series of lightning strikes (associated with circuit breaker, C.B., opening, and auto-reclosing) are modeled in this paper as continuous-time Markov jumps. Additionally, the stochastic parameter changes e.g., the line reactance, are influenced by the phase separation, which in turn depends on the stochastic wind speed. This is modeled as a stochastic disturbance. In this manuscript, the impact of the above stochastic disturbance on power system small-disturbance stability is studied based on stochastic differential equations (SDEs). The mean-square stabilization of such a system is conducted through a novel excitation control. The invariant ellipsoid and linear matrix inequality (LMI) optimization are used to construct the control system. The numerical simulations are presented on a multi-machine test system.

Parameter estimation for a class of time‐varying systems with the invariant matrix

AbstractThis article is concerned with the identification of time‐varying systems. Differently from the conventional polynomial approximation approaches, the changing laws of the time‐varying parameters are considered to build the identification model for the time‐varying systems. Specifically, the concept of the invariant matrix is put forward to characterize the time‐varying parameters and to establish the state‐space model with regard to the system parameters. Then this article proposes a stacked state estimation algorithm to achieve the time‐varying parameter estimation. Moreover, for the purpose of enhancing the computational efficiency, a detached state estimation algorithm is proposed by reducing the dimension of the state vector to reconstruct the state equation. Finally, a numerical simulation example is employed to demonstrate the effectiveness of the proposed algorithms.

Permutation symmetry in large- N matrix quantum mechanics and partition algebras

We describe the implications of permutation symmetry for the state space and dynamics of quantum mechanical systems of matrices of general size $N$. We solve the general 11-parameter permutation invariant quantum matrix harmonic oscillator Hamiltonian and calculate the canonical partition function. The permutation invariant sector of the Hilbert space, for general Hamiltonians, can be described using partition algebra diagrams forming the bases of a tower of partition algebras ${P}_{k}(N)$. The integer $k$ is interpreted as the degree of matrix oscillator polynomials in the quantum mechanics. Families of interacting Hamiltonians are described which are diagonalized by a representation theoretic basis for the permutation invariant subspace which we construct for $N\ensuremath{\ge}2k$. These include Hamiltonians for which the low-energy states are permutation invariant and can give rise to large ground state degeneracies related to the dimensions of partition algebras. A symmetry-based mechanism for quantum many body scars discussed in the literature can be realized in these matrix systems with permutation symmetry. A mapping of the matrix index values to lattice sites allows a realization of the mechanism in the context of modified Bose-Hubbard models. Extremal correlators analogous to those studied in $\mathrm{AdS}/\mathrm{CFT}$ are shown to obey selection rules based on Clebsch-Gordan multiplicites (Kronecker coefficients) of symmetric groups.

Random entire functions from random polynomials with real zeros

We point out a simple criterion for convergence of polynomials to a concrete entire function in the Laguerre-Pólya (LP) class (of all functions arising as uniform limits of polynomials with only real roots). We then use this to show that any random LP function can be obtained as the uniform limit of rescaled characteristic polynomials of principal submatrices of an infinite unitarily invariant random Hermitian matrix. Conversely, the rescaled characteristic polynomials of principal submatrices of any infinite random unitarily invariant Hermitian matrix converge uniformly to a random LP function. This result also has a natural extension to β-ensembles. Distinguished cases include random entire functions associated to the β-Sine, and more generally β-Hua-Pickrell, β-Bessel and β-Airy point processes studied in the literature.

Hidden symmetries and large N factorisation for permutation invariant matrix observables

Permutation invariant polynomial functions of matrices have previously been studied as the observables in matrix models invariant under SN, the symmetric group of all permutations of N objects. In this paper, the permutation invariant matrix observables (PIMOs) of degree k are shown to be in one-to-one correspondence with equivalence classes of elements in the diagrammatic partition algebra Pk (N). On a 4-dimensional subspace of the 13-parameter space of SN invariant Gaussian models, there is an enhanced O(N) symmetry. At a special point in this subspace, is the simplest O(N) invariant action. This is used to define an inner product on the PIMOs which is expressible as a trace of a product of elements in the partition algebra. The diagram algebra Pk (N) is used to prove the large N factorisation property for this inner product, which generalizes a familiar large N factorisation for inner products of matrix traces invariant under continuous symmetries.

Centralizer matrix algebras and symmetric polynomials of partitions

For a field R of characteristic p≥0 and a matrix c in the full n×n matrix algebra Mn(R) over R, let Sn(c,R) be the centralizer algebra of c in Mn(R). We show that Sn(c,R) is a Frobenius-finite, 1-Auslander-Gorenstein, and gendo-symmetric algebra, and that the extension Sn(c,R)⊆Mn(R) is separable and Frobenius. Further, we study the isomorphism problem of invariant matrix algebras. Let σ be a permutation in the symmetric group Σn and cσ the corresponding permutation matrix in Mn(R). We give sufficient and necessary conditions for the invariant algebra Sn(cσ,R) to be semisimple. If R is an algebraically closed field, we establish a combinatoric characterization of when two semisimple invariant R-algebras are isomorphic in terms of the cycle types of permutations.

Algorithm Analysis of Clothing Classification Based on Neural Network

With the rapid development of Internet e-commerce, the online transaction volume of clothing has increased day by day, and the importance of clothing images in transactions has also increased. However, there are many clothing categories and different classification standards. It is difficult for consumers and e-commerce merchants to unify the description of clothing categories, which can easily lead to a poor clothing shopping experience. Neural network has an excellent list in the field of computer vision, which can effectively classify clothing. The purpose of this article is to study the algorithm analysis of clothing classification based on neural network. Starting from the neural network, this paper proposes a clothing image classification algorithm based on a multi-task convolutional neural network (Convolutional Neural Network, CNN). Through hierarchical classification data combined with multi-task technology, the basic structure of the network model is not changed. The accuracy of clothing image classification improves the network’s ability to express refined clothing categories. This paper proposes a clothing classification algorithm based on the feature fusion of Hu invariant matrix and CNN network. The feature fusion of the features extracted by the convolutional neural network is initially explored, the information gain of the feature is calculated, and the shape feature is used to eliminate the feature with less information gain. This paper also designs a clothing classification system based on neural network to realize the recognition, detection and classification of clothing images. The experimental results show that the clothing classification accuracy rates under the four combined tasks are 93.54%, 89.26%, 92.14%, 95.66%, and 93.54%, respectively. It can be seen that the model based on convolutional neural network can further improve the accuracy of clothing classification.

Gaussianity and typicality in matrix distributional semantics

Constructions in type-driven compositional distributional semantics associate large collections of matrices of size D to linguistic corpora. We develop the proposal of analysing the statistical characteristics of this data in the framework of permutation invariant matrix models. The observables in this framework are permutation invariant polynomial functions of the matrix entries, which correspond to directed graphs. Using the general 13-parameter permutation invariant Gaussian matrix models recently solved, we find, using a dataset of matrices constructed via standard techniques in distributional semantics, that the expectation values of a large class of cubic and quartic observables show high Gaussianity at levels between 90 to 99 percent. Beyond expectation values, which are averages over words, the dataset allows the computation of standard deviations for each observable, which can be viewed as a measure of typicality for each observable. There is a wide range of magnitudes in the measures of typicality. The permutation invariant matrix models, considered as functions of random couplings, give a very good prediction of the magnitude of the typicality for different observables. We find evidence that observables with similar matrix model characteristics of Gaussianity and typicality also have high degrees of correlation between the ranked lists of words associated to these observables.

Symmetries and local transformations of translationally invariant matrix product states

We determine the local symmetries and local transformation properties of translationally invariant matrix product states (MPS). We focus on physical dimension $d=2$ and bond dimension $D=3$ and use the procedure introduced in D. Sauerwein et al., Phys. Rev. Lett. 123, 170504 (2019) to determine all (including non--global) symmetries of those states. We identify and classify the stochastic local transformations (SLOCC) that are allowed among MPS. We scrutinize two very distinct sets of MPS and show the big diversity (also compared to the case $D=2$) occurring in both, their symmetries and the possible SLOCC transformations. These results reflect the variety of local properties of MPS, even if restricted to translationally invariant states with low bond dimension. Finally, we show that states with non-trivial local symmetries are of measure zero for $d = 2$ and $D > 3$.

Unified Framework for Generalized Statistics: Canonical Partition Function, Maximum Occupation Number, and Permutation Phase of Wave Function

Beyond Bose and Fermi statistics, there still exist various kinds of generalized quantum statistics. Two ways to approach generalized quantum statistics: (1) in quantum mechanics, generalize the permutation symmetry of the wave function and (2) in statistical mechanics, generalize the maximum occupation number of quantum statistics. The connection between these two approaches, however, is obscure. In this paper, we suggest a unified framework to describe various kinds of generalized quantum statistics. We first provide a general formula of canonical partition functions of ideal $N$-particle gases obeying various kinds of generalized quantum statistics. Then we reveal the connection between the permutation phase of the wave function and the maximum occupation number, through constructing a method to obtain the permutation phase and the maximum occupation number from the canonical partition function. In our scheme, the permutation phase of wave functions is generalized to a matrix phase, rather than a number. It is commonly accepted that different kinds of statistics are distinguished by the maximum number. We show that the maximum occupation number is not sufficient to distinguish different kinds of generalized quantum statistics. As examples, we discuss a series of generalized quantum statistics in the unified framework, giving the corresponding canonical partition functions, maximum occupation numbers, and the permutation phase of wave functions. Especially, we propose three new kinds of generalized quantum statistics which seem to be the missing pieces in the puzzle. The mathematical basis of the scheme are the mathematical theory of the invariant matrix, the Schur-Weyl duality, the symmetric function, and the representation theory of the permutation group and the unitary group. The result in this paper builds a bridge between the statistical mechanics and such mathematical theories.

Translationally invariant matrix elements of general one-body operators

Precision tests of the standard model and searches for beyond the standard model physics often require nuclear structure input. There has been a tremendous progress in the development of nuclear ab initio techniques capable of providing accurate nuclear wave functions. For the calculation of observables, matrix elements of complicated operators need to be evaluated. Typically, these matrix elements would contain spurious contributions from the center-of-mass (c.m.) motion. This could be problematic when precision results are sought. Here, I derive a transformation relying on properties of harmonic oscillator wave functions that allows an exact removal of the c.m. motion contamination applicable to any one-body operator depending on nucleon coordinates and momenta. Resulting many-nucleon matrix elements are translationally invariant provided that the nuclear eigenfunctions factorize as products of the intrinsic and c.m. components as is the case, e.g., in the no-core shell model approach. An application of the transformation has been recently demonstrated in calculations of the nuclear structure recoil corrections for the $\ensuremath{\beta}$ decay of $^{6}\mathrm{He}$.