A preconditioning approach to the pagerank computation problem

Some spectral properties of the transition matrix of an oriented graph indicate the preconditioning of Euler-Richardson (ER) iterative scheme as a good way to compute efficiently the vertexrank vector associated with such graph. We choose the preconditioner from an algebra U of matrices, thereby obtaining an ER_U method, and we observe that ER_U can outperform ER in terms of rate of convergence. The proposed preconditioner can be updated at a very low cost whenever the graph changes, as is the case when it represents a generic set of information. The particular U utilized requires a surplus of operations per step and memory allocations, which make ER_U superior to ER for not too wide graphs. However, the observed high improvement in convergence rate obtained by preconditioning and the general theory developed, are a reason for investigating different choices of U, more efficient for huge graphs.     

Inverse product Toeplitz preconditioners for non-Hermitian Toeplitz systems

In this paper, we first propose product Toeplitz preconditioners (in an inverse form) for non-Hermitian Toeplitz matrices generated by functions with zeros. Our inverse product-type preconditioner is of the form T_F T_L^-1 T_U^-1T_F T_L^{-1} T_U^{-1} where T _F , T _L , and T _U are full, band lower triangular, and band upper triangular Toeplitz matrices, respectively. Our basic idea is to decompose the generating function properly such that all factors T _F , T _L , and T _U of the preconditioner are as well-conditioned as possible. We prove that under certain conditions, the preconditioned matrix has eigenvalues and singular values clustered around 1. Then we use a similar idea to modify the preconditioner proposed in Ku and Kuo (SIAM J Sci Stat Comput 13:1470–1487, 1992) to handle the zeros in rational generating functions. Numerical results, including applications to the computation of the stationary probability distribution of Markovian queuing models with batch arrival, are given to illustrate the good performance of the proposed preconditioners.     

New breakdown-free variant of AINV method for nonsymmetric positive definite matrices

This paper proposes a new breakdown-free preconditioning technique, called SAINV-NS, of the AINV method of Benzi and Tuma for nonsymmetric positive definite matrices. The resulting preconditioner which is an incomplete factorization of the inverse of a nonsymmetric matrix will be used as an explicit right preconditioner for QMR, BiCGSTAB and GMRES(m) methods. The preconditoner is reliable (pivot breakdown can not occur) and effective at reducing the number of iterations. Some numerical experiments on test matrices are presented to show the efficiency of the new method and comparing to the AINV-A algorithm.     

Block LU factorizations of M–matrices

Summary. It is well known that any nonsingular M–matrix admits an LU factorization into M–matrices (with L and U lower and upper triangular respectively) and any singular M–matrix is permutation similar to an M–matrix which admits an LU factorization into M–matrices. Varga and Cai establish necessary and sufficient conditions for a singular M–matrix (without permutation) to allow an LU factorization with L nonsingular. We generalize these results in two directions. First, we find necessary and sufficient conditions for the existence of an LU factorization of a singular M-matrix where L and U are both permitted to be singular. Second, we establish the minimal block structure that a block LU factorization of a singular M–matrix can have when L and U are M–matrices. Received November 21, 1994 / Revised version received August 4, 1997     

Banded target matrices and recursive FSAI for parallel preconditioning

In this paper we propose a parallel preconditioner for the CG solver based on successive applications of the FSAI preconditioner. We first compute an FSAI factor G _out for coefficient matrix A, and then another FSAI preconditioner is computed for either the preconditioned matrix $S = G_{\rm out} A G_{\rm out}^T$ or a sparse approximation of S. This process can be iterated to obtain a sequence of triangular factors whose product forms the final preconditioner. Numerical results onto large SPD matrices arising from geomechanical models account for the efficiency of the proposed preconditioner which provides a reduction of the iteration number and of the CPU time of the iterative phase with respect to the original FSAI preconditioner. The proposed preconditioner reveals particularly efficient for accelerating an iterative procedure to find the smallest eigenvalues of SPD matrices, where the increased setup cost of the RFSAI preconditioner does not affect the overall performance, being a small percentage of the total CPU time.     

Lie superalgebras whose enveloping algebras satisfy a non-matrix polynomial identity

Let L be a Lie superalgebra with its enveloping algebra U(L) over a field F. A polynomial identity is called non-matrix if it is not satisfied by the algebra of 2×2 matrices over F. We characterize L when U(L) satisfies a non-matrix polynomial identity. We also characterize L when U(L) is Lie solvable, Lie nilpotent, or Lie super-nilpotent.     

稀疏近似逆与多层块ILU预条件技术

设计了一种求解一般稀疏线性方程组的健壮且有效的可并行化预条件子,这种预条件子涉及在多层块ILU预条件子(BILUM)中使用稀疏近似逆(AINV)技术．所得的预条件子保持了BILUM的健壮性,它比标准的BILUM预条件子有两点优势:控制稀疏性的能力和增强了并行性．数值例子显示了新预条件子的有效性和效率．     

The LU-factorization of totally positive matrices

An n × n real matrix A is an STP (strictly totally positive) matrix if all its minors are strictly positive. An n × n real triangular matrix A is a ΔSTP matrix if all its nontrivial minors are strictly positive. It is proved that A is an STP matrix iff A = LU where L is a lower triangular matrix, U is an upper triangular matrix, and both L and U are ΔSTP matrices. Several related results are proved. These results lead to simple proofs of many of the determinantal properties of STP matrices.     

An alternating preconditioner for saddle point problems

Based on matrix splittings, a new alternating preconditioner with two parameters is proposed for solving saddle point problems. Some theoretical analyses for the eigenvalues of the associated preconditioned matrix are given. The choice of the parameters is considered and the quasi-optimal parameters are obtained. The new preconditioner with these quasi-optimal parameters significantly improves the convergence rate of the generalized minimal residual (GMRES) iteration. Numerical experiments from the linearized Navier-Stokes equations demonstrate the efficiency of the new preconditioner, especially on the larger viscosity parameter ν. Further extensions of the preconditioner to generalized saddle point matrices are also checked.     

Flexible incomplete Cholesky factorization with multi‐parameters to control the number of nonzero elements in preconditioners

An incomplete Cholesky (IC) factorization with multi‐parameters is presented. The marked virtue of the proposed IC factorization algorithm is to dynamically control the number of nonzero elements in each column of the IC factorization preconditioner L with the help of these involved parameters. Parameter setting strategies are also given. Numerical results show that the total computing time for both computation of the preconditioner L and iterative solution is evidently reduced for almost all test matrices. In general, these parameters can obviously enhance the effectiveness and performance of the IC factorization. Copyright © 2011 John Wiley & Sons, Ltd.     

Accelerated iterative method for Z-matrices

It has recently been reported that the convergence of the preconditioned Gauss-Seidel method which uses a matrix of the type (I + U) as a preconditioner is faster than the basic iterative method. In this paper, we generalize the preconditioner to the type (I + βU), where β is a positive real number. After discussing convergence of the method applied to Z-matrices, we propose an algorithm for estimating the optimum β. Numerical examples are also given, which show the effectiveness of our algorithm.     

Combinatorial preconditioners for scalar elliptic finite-element problems

We present a new preconditioner for linear systems arising from finite-elements discretizations of scalar elliptic partial differential equations. The solver is based on building a symmetric diagonally dominant (SDD) approximation of the stiffness matrix K. The approximation is built by approximating each element inside the collection {K_e } of element matrices by an SDD matrix L_e. The SDD approximation L is built by assembling the collection {L_e }. We then sparsify L using a graph algorithm, and use the sparsified matrix as a preconditioner. (© 2008 WILEY-VCH Verlag GmbH & Co. KGaA, Weinheim)     

Lower bounds for centered and wrap-around L2-discrepancies and construction of uniform designs by threshold accepting

We study the uniformity of two- and three-level U-type designs based on the centered and wrap-around L₂-discrepancies. By analyzing the known formulae, we find it possible to reexpress them as functions of column balance, and also as functions of Hamming distances of the rows. These new representations allow to obtain two kinds of lower bounds, which can be used as bench marks in searching uniform U-type designs. An efficient updating procedure for the local search heuristic threshold accepting is developed based on these novel formulations of the centered and wrap-around L₂-discrepancies. Our implementation of this heuristic for the two- and three-level case efficiently generates low discrepancy U-type designs. Their quality is assessed using the available lower bounds.     

BTTB preconditioners for BTTB least squares problems

In this paper, we consider solving the least squares problem min_x‖b-Tx‖₂ by using preconditioned conjugate gradient (PCG) methods, where T is a large rectangular matrix which consists of several square block-Toeplitz-Toeplitz-block (BTTB) matrices and b is a column vector. We propose a BTTB preconditioner to speed up the PCG method and prove that the BTTB preconditioner is a good preconditioner. We then discuss the construction of the BTTB preconditioner. Numerical examples, including image restoration problems, are given to illustrate the efficiency of our BTTB preconditioner. Numerical results show that our BTTB preconditioner is more efficient than the well-known Level-1 and Level-2 circulant preconditioners.     

A Multilevel AINV Preconditioner

In this paper we describe an algebraic multilevel extension of the approximate inverse AINV preconditioner for solving symmetric positive definite linear systems Ax=b with the preconditioned conjugate gradient method. The smoother is the approximate inverse M and the coarse grids and the interpolation operator are constructed by looking at the entries of M. Numerical examples are given for problems arising from discretization of partial differential equations.     

Complement total unimodularity

A {0, 1} matrix U is defined to be complement totally unimodular (c.t.u.) if U as well as all matrices derived from U by certain complement operations are totally unimodular. Two decompositions of minimal violation matrices of total unimodularity are utilized to characterize the letter matrices by c.t.u. matrices. In addition properties of c.t.u. matrices are presented.     

A robust AINV‐type method for constructing sparse approximate inverse preconditioners in factored form

This paper suggests a new method, called AINV‐A, for constructing sparse approximate inverse preconditioners for positive‐definite matrices, which can be regarded as a modification of the AINV method proposed by Benzi and Túma. Numerical results on SPD test matrices coming from different applications demonstrate the robustness of the AINV‐A method and its superiority to the original AINV approach. Copyright © 2001 John Wiley & Sons, Ltd.     

A linear programming algorithm for curve fitting in the L∞ norm

The L_∞ norm has been widely studied as a criterion for curve fitting problems. This paper presents an algorithm to solve discrete approximation problems in the L_∞ norm. The algorithm is a special-purpose linear programming method using the dual form of the problem, which employs a reduced basis and multiple pivots. Results of the computational experience with a computer code version of the algorithm are presented.     

Comparison results of the preconditioned AOR methods for L-matrices

In this paper, we first provide comparison results of several types of the preconditioned AOR (PAOR) methods for solving a linear system whose coefficient matrix is an L-matrix satisfying some weaker conditions than those used in the recent literature. Next, we propose an application of PAOR method to a preconditioner of Krylov subspace method. Lastly, numerical results are provided to show that Krylov subspace method with the PAOR preconditioner performs quite well as compared with the ILU (0) preconditioner.     

Factorization of block matrices

An LU-type factorization theorem due to Elsner and to Gohberg and Goldberg is generalized to block matrices. One form of the general factorization takes the form LMU, where L is block lower-triangular, U is block upper-triangular, and M is a subpermutation matrix each of whose blocks is diagonal. A factorization is also given where the middle term is a block diagonal subpermutation matrix, and the factorization is applied to Wiener-Hopf equations. The nonuniqueness of the middle term in the factorization is analyzed. A special factorization for self-adjoint block matrices is also obtained.