Simultaneous Matrix Diagonalization for Structural Brain Networks Classification

This paper considers the problem of brain disease classification based on connectome data. A connectome is a network representation of a human brain. The typical connectome classification problem is very challenging because of the small sample size and high dimensionality of the data. We propose to use simultaneous approximate diagonalization of adjacency matrices in order to compute their eigenstructures in more stable way. The obtained approximate eigenvalues are further used as features for classification. The proposed approach is demonstrated to be efficient for detection of Alzheimer's disease, outperforming simple baselines and competing with state-of-the-art approaches to brain disease classification

Decoupling Multivariate Polynomials Using First-Order Information

We present a method to decompose a set of multivariate real polynomials into linear combinations of univariate polynomials in linear forms of the input variables. The method proceeds by collecting the first-order information of the polynomials in a set of operating points, which is captured by the Jacobian matrix evaluated at the operating points. The polyadic canonical decomposition of the three-way tensor of Jacobian matrices directly returns the unknown linear relations, as well as the necessary information to reconstruct the univariate polynomials. The conditions under which this decoupling procedure works are discussed, and the method is illustrated on several numerical examples

A New SLNR-based Linear Precoding for Downlink Multi-User Multi-Stream MIMO Systems

Signal-to-leakage-and-noise ratio (SLNR) is a promising criterion for linear precoder design in multi-user (MU) multiple-input multiple-output (MIMO) systems. It decouples the precoder design problem and makes closed-form solution available. In this letter, we present a new linear precoding scheme by slightly relaxing the SLNR maximization for MU-MIMO systems with multiple data streams per user. The precoding matrices are obtained by a general form of simultaneous diagonalization of two Hermitian matrices. The new scheme reduces the gap between the per-stream effective channel gains, an inherent limitation in the original SLNR precoding scheme. Simulation results demonstrate that the proposed precoding achieves considerable gains in error performance over the original one for multi-stream transmission while maintaining almost the same achievable sum-rate.Comment: 8 pages, 1 figur

Efficient Algorithm for Asymptotics-Based Configuration-Interaction Methods and Electronic Structure of Transition Metal Atoms

Asymptotics-based configuration-interaction (CI) methods [G. Friesecke and B. D. Goddard, Multiscale Model. Simul. 7, 1876 (2009)] are a class of CI methods for atoms which reproduce, at fixed finite subspace dimension, the exact Schr\"odinger eigenstates in the limit of fixed electron number and large nuclear charge. Here we develop, implement, and apply to 3d transition metal atoms an efficient and accurate algorithm for asymptotics-based CI. Efficiency gains come from exact (symbolic) decomposition of the CI space into irreducible symmetry subspaces at essentially linear computational cost in the number of radial subshells with fixed angular momentum, use of reduced density matrices in order to avoid having to store wavefunctions, and use of Slater-type orbitals (STO's). The required Coulomb integrals for STO's are evaluated in closed form, with the help of Hankel matrices, Fourier analysis, and residue calculus. Applications to 3d transition metal atoms are in good agreement with experimental data. In particular we reproduce the anomalous magnetic moment and orbital filling of Chromium in the otherwise regular series Ca, Sc, Ti, V, Cr.Comment: 14 pages, 1 figur

On simultaneous diagonalization via congruence of real symmetric matrices

Simultaneous diagonalization via congruence (SDC) for more than two symmetric matrices has been a long standing problem. So far, the best attempt either relies on the existence of a semidefinite matrix pencil or casts on the complex field. The problem now is resolved without any assumption. We first propose necessary and sufficient conditions for SDC in case that at least one of the matrices is nonsingular. Otherwise, we show that the singular matrices can be decomposed into diagonal blocks such that the SDC of given matrices becomes equivalently the SDC of the sub-matrices. Most importantly, the sub-matrices now contain at least one nonsingular matrix. Applications to simplify some difficult optimization problems with the presence of SDC are mentioned