Numerical Computation of the Complex Eigenvalues of a Matrix by solving a Square System of Equations

It is well known that if the largest or smallest eigenvalue of a matrix has been computed by some numerical algorithms and one is interested in computing the corresponding eigenvector, one method that is known to give such good approximations to the eigenvector is inverse iteration with a shift. For complex eigenpairs, instead of using Ruhe’s normalization, we show that the natural two norm normalization for the matrix pencil, yields an underdetermined system of equation and by adding an extra equation, the augmented system becomes square which can be solved by LU factorization at a cheaper rate and quadratic convergence is guaranteed. While the underdetermined system of equations can be solved using QR factorization as shown in an earlier work by the same authors, converting it to a square system of equations has the added advantage that besides using LU factorization, it can be solved by several approaches including iterative methods. We show both theoretically and numerically that both algorithms are equivalent in the absence of roundoff errors

LU factorization with panel rank revealing pivoting and its communication avoiding version

We present the LU decomposition with panel rank revealing pivoting (LU_PRRP), an LU factorization algorithm based on strong rank revealing QR panel factorization. LU_PRRP is more stable than Gaussian elimination with partial pivoting (GEPP). Our extensive numerical experiments show that the new factorization scheme is as numerically stable as GEPP in practice, but it is more resistant to pathological cases and easily solves the Wilkinson matrix and the Foster matrix. We also present CALU_PRRP, a communication avoiding version of LU_PRRP that minimizes communication. CALU_PRRP is based on tournament pivoting, with the selection of the pivots at each step of the tournament being performed via strong rank revealing QR factorization. CALU_PRRP is more stable than CALU, the communication avoiding version of GEPP. CALU_PRRP is also more stable in practice and is resistant to pathological cases on which GEPP and CALU fail.Comment: No. RR-7867 (2012

The double charm decays of BcB_c Meson in the Perturbative QCD Approach

We make a systematic investigation on the double charm decays of $B_c$ meson, by employing the perturbative QCD approach based on $k_T$ factorization. It is found that the non-factorizable emission diagrams are not negligible in these channels. We predict the branching ratios of these $B_c$ decays and also the transverse polarization fractions of $B_c\rightarrow D_{(s)}^{*+}\bar D^{*0}, D_{(s)}^{*+}D^{*0}$ decays, % where V denote the vector $D^*_{(s)}$ meson. We find that the magnitudes of the branching ratios of the decays $B_c\rightarrow D_s\bar{D}^0$ and $B_c\rightarrow D_sD^0$ are very close to each other, which are well suited to extract the Cabibbo-Kobayashi-Maskawa angle $\gamma$ through the amplitude relations. In addition, a large transverse polarization contribution that can reach $50$ is predicted in some of the $B_c$ meson decay to two vector charmed mesons.Comment: 22 pages, 5 tables, to appear at PRD. arXiv admin note: text overlap with arXiv:1112.125

Improving approximate matrix factorizations for implicit time integration in air pollution modelling

For a long time operator splitting was the only computationally feasible way of implicit time integration in large scale Air Pollution Models. A recently proposed attractive alternative is Rosenbrock schemes combined with Approximate Matrix Factorization (AMF). With AMF, linear systems arising in implicit time stepping are solved approximately in such a way that the overall computational costs per time step are not higher than those of splitting methods. We propose and discuss two new variants of AMF. The first one is aimed at yet a further reduction of costs as compared with conventional AMF. The second variant of AMF provides in certain circumstances a better approximation to the inverse of the linear system matrix than standard AMF and requires the same computational work

DMFSGD: A Decentralized Matrix Factorization Algorithm for Network Distance Prediction

The knowledge of end-to-end network distances is essential to many Internet applications. As active probing of all pairwise distances is infeasible in large-scale networks, a natural idea is to measure a few pairs and to predict the other ones without actually measuring them. This paper formulates the distance prediction problem as matrix completion where unknown entries of an incomplete matrix of pairwise distances are to be predicted. The problem is solvable because strong correlations among network distances exist and cause the constructed distance matrix to be low rank. The new formulation circumvents the well-known drawbacks of existing approaches based on Euclidean embedding. A new algorithm, so-called Decentralized Matrix Factorization by Stochastic Gradient Descent (DMFSGD), is proposed to solve the network distance prediction problem. By letting network nodes exchange messages with each other, the algorithm is fully decentralized and only requires each node to collect and to process local measurements, with neither explicit matrix constructions nor special nodes such as landmarks and central servers. In addition, we compared comprehensively matrix factorization and Euclidean embedding to demonstrate the suitability of the former on network distance prediction. We further studied the incorporation of a robust loss function and of non-negativity constraints. Extensive experiments on various publicly-available datasets of network delays show not only the scalability and the accuracy of our approach but also its usability in real Internet applications.Comment: submitted to IEEE/ACM Transactions on Networking on Nov. 201

Hadronic decays of B→a1(1260)b1(1235)B \to a_1(1260) b_1(1235) in the perturbative QCD approach

We calculate the branching ratios and polarization fractions of the $B \to a_1 b_1$ decays in the perturbative QCD(pQCD) approach at leading order, where $a_1$($b_1$) stands for the axial-vector $a_1(1260)[b_1(1235)]$ state. By combining the phenomenological analyses with the perturbative calculations, we find the following results: (a) the large decay rates around $10^{-5}$ to $10^{-6}$ of the $B \to a_1 b_1$ decays dominated by the longitudinal polarization(except for the $B^+ \to b_1^+ a_1^0$ mode) are predicted and basically consistent with those in the QCD factorization(QCDF) within errors, which are expected to be tested by the Large Hadron Collider and Belle-II experiments. The large $B^0 \to a_1^0 b_1^0$ branching ratio could provide hints to help explore the mechanism of the color-suppressed decays. (b) the rather different QCD behaviors between the $a_1$ and $b_1$ mesons result in the destructive(constructive) contributions in the nonfactorizable spectator diagrams with $a_1(b_1)$ emission. Therefore, an interesting pattern of the branching ratios appears for the color-suppressed $B^0 \to a_1^0 a_1^0, a_1^0 b_1^0,$ and $b_1^0 b_1^0$ modes in the pQCD approach, $Br(B^0 \to b_1^0 b_1^0) > Br(B^0 \to a_1^0 b_1^0) \gtrsim Br(B^0 \to a_1^0 a_1^0)$, which is different from $Br(B^0 \to b_1^0 b_1^0) \sim Br(B^0 \to a_1^0 b_1^0) \gtrsim Br(B^0 \to a_1^0 a_1^0)$ in the QCDF and would be verified at future experiments. (c) the large naive factorization breaking effects are observed in these $B \to a_1 b_1$ decays. Specifically, the large nonfactorizable spectator(weak annihilation) amplitudes contribute to the $B^0 \to b_1^+ a_1^-(B^+ \to a_1^+ b_1^0\; {\rm and}\; B^+ \to b_1^+ a_1^0)$ mode(s), which demand confirmations via the precise measurements.Comment: 13 pages, 1 figure, 5 tables, revtex fil

The Bordering Algorithm and Path Following Near Singular Points of Higher Nullity

We study the behavior of the bordering algorithm (a form of block elimination) for solving nonsingular linear systems with coefficient matrices in the partitioned form (A & B \\ C^* & D) when N(A)≧1. Systems with this structure naturally occur in path following procedures. We show that under appropriate assumptions, the algorithm, which is based on solving systems with coefficient matrix A, works as A varies along a path and goes through singular points. The required assumptions are justified for a large class of problems coming from discretizations of boundary value problems for differential equations