The Lanczos method applied to quasi-static stark broadening of spectral lines

The quasi-static ion approximation of Stark broadened spectral lines involves an average of the field-dependent line shape over the microfield probability distribution. In the conventional approach, this can become computationally expensive since the calculation at each field point requires inverting a possibly large matrix. It is shown that these calculations are well suited to the “Padé Via Lanczos” approach, which allows for an efficient and accurate numerical integration over the quasi-static field. In turn, the integration forms the basis for determining convergence with Lanczos iterations. Simple examples are used to demonstrate improved performance over conventional methods.     

Lanczos-type Methods for Continuation Problems

We study the Lanczos type methods for continuation problems. First we indicate how the symmetric Lanczos method may be used to solve both positive definite and indefinite linear systems. Furthermore, it can be used to monitor the simple bifurcation points on the solution curve of the eigenvalue problems. This includes computing the minimum eigenvalue, the minimum singular value, and the condition number of the partial tridiagonalizations of the coefficient matrices. The Ritz vector thus obtained can be applied to compute the tangent vector at the bifurcation point for branch-switching. Next, we indicate that the block or band Lanczos method can be used to monitor the multiple bifurcations as well as to solve the multiple right hand sides. We also show that the unsymmetric Lanczos method can be exploited to compute the minimum eigenvalue of a nearly symmetric matrix, and therefore to detect the simple bifurcation point as well. Some preconditioning techniques are discussed. Sample numerical results are reported. Our test problems include second order semilinear elliptic eigenvalue problems. © 1997 by John Wiley & Sons, Ltd.     

Numerical solution of eigenvalue problems by means of a wavelet‐based Lanczos decomposition

The simple Lanczos process is very efficient for finding a few extreme eigenvalues of a large symmetric matrix. The main task in each iteration step consists in evaluating a matrix‐vector product. It is shown how to apply a fast wavelet‐based product in order to speed up computations. Some numerical results are given for three different monodimensional cases: the harmonic oscillator case, the hydrogenlike atoms, and a problem with a pseudo‐double‐well potential. © 2000 John Wiley & Sons, Inc. Int J Quant Chem 77: 552–562, 2000     

Direct calculation of long time correlation functions using an optical potential

We present an ℒ︁² method aimed at directly computing autocorrelation functions 〈Φ₀|Φ_t〉 for systems displaying long time recurrences. By making use of a Lanczos scheme, as previously proposed by Wyatt [Chem. Phys. Lett. 121, 301 (1985)], the method avoids explicit time propagation of the wavefunction. The problem associated with spurious recurrences, due to the finite size of the ℒ︁²-box, is solved in terms of an optical potential located in the asymptotic region. The resulting complex representation of the Hamiltonian operator is handled by a complex symmetric Lanczos scheme, which retains the same basic advantages as its real version. The method is illustrated on the ozone photodissociation process which displays a very detailed recurrence structure over a long time period. It is shown that such a direct calculation of the correlation function is about one order of magnitude faster than an actual wavepacket propagation. The accuracy of the method is assessed by comparison to calculations performed without any optical potential but using a very large box size along the dissociation coordinate. © 1998 John Wiley & Sons, Inc. Int J Quant Chem 68: 317–328, 1998     

Approximating minimum norm solutions of rank-deficient least squares problems

In this paper we consider the solution of linear least squares problems min_x∥Ax - b∥²₂ where the matrix A ∈ R ^{m × n} is rank deficient. Put p = min{m, n}, let σ_i, i = 1, 2,…, p, denote the singular values of A, and let u_i and v_i denote the corresponding left and right singular vectors. Then the minimum norm solution of the least squares problem has the form x* = ∫^r_{i = 1}(u^T_ib/σ_i)v_i, where r ≤ p is the rank of A. The Riley–Golub iteration, x_{k + 1} = arg min_x{∥Ax - b∥²₂ + λ∥x − x_k∥²₂} converges to the minimum norm solution if x₀ is chosen equal to zero. The iteration is implemented so that it takes advantage of a bidiagonal decomposition of A. Thus modified, the iteration requires only O(p) flops (floating point operations). A further gain of using the bidiagonalization of A is that both the singular values σ_i and the scalar products u^T_ib can be computed at marginal extra cost. Moreover, we determine the regularization parameter, λ, and the number of iterations, k, in a way that minimizes the difference x* − x_k with respect to a certain norm. Explicit rules are derived for calculating these parameters. One advantage of our approach is that the numerical rank can be easily determined by using the singular values. Furthermore, by the iterative procedure, x* is approximated without computing the singular vectors of A. This gives a fast and reliable method for approximating minimum norm solutions of well-conditioned rank-deficient least squares problems. Numerical experiments illustrate the viability of our ideas, and demonstrate that the new method gives more accurate approximations than an approach based on a QR decomposition with column pivoting. © 1998 John Wiley & Sons, Ltd.     

PLS works

In a recent paper, claims were made that most current implementations of PLS provide wrong and misleading residuals [1]. In this paper the relation between PLS and Lanczos bidiagonalization is described and it is shown that there is a good rationale behind current implementations of PLS. Most importantly, the residuals determined in current implementations of PLS are independent of the scores used for predicting the dependent variable(s). Oppositely, in the newly suggested approach, the residuals are correlated to the scores and hence may be high due to variation that is actually used for predicting. It is concluded that the current practice of calculating residuals be maintained. Copyright © 2008 John Wiley & Sons, Ltd.     

A numerical solution of the constrained weighted energy problem

A numerical algorithm is presented to solve the constrained weighted energy problem from potential theory. As one of the possible applications of this algorithm, we study the convergence properties of the rational Lanczos iteration method for the symmetric eigenvalue problem. The constrained weighted energy problem characterizes the region containing those eigenvalues that are well approximated by the Ritz values. The region depends on the distribution of the eigenvalues, on the distribution of the poles, and on the ratio between the size of the matrix and the number of iterations. Our algorithm gives the possibility of finding the boundary of this region in an effective way.We give numerical examples for different distributions of poles and eigenvalues and compare the results of our algorithm with the convergence behavior of the explicitly performed rational Lanczos algorithm.     

Lanczos Potential for the van Stockung Space-Time

The Lanczos Potential is a theoretical useful tool to find the conformal Weyl curvature tensor C _abcd of a given relativistic metric. In this paper we find the Lanczos potential L _abc for the van Stockung vacuum gravitational field. Also, we show how the wave equation can be combined with spinor methods in order to find this important three covariant index tensor.     

求解大规模Hamilton矩阵特征问题的辛Lanczos算法的误差分析

对求解大规模稀疏Hamilton矩阵特征问题的辛Lanczos算法给出了舍入误差分析.分析表明辛Lanczos算法在无中断时,保Hamilton结构的限制没有破坏非对称Lanczos算法的本质特性.本文还讨论了辛Lanczos算法计算出的辛Lanczos向量的J一正交性的损失与Ritz值收敛的关系.结论正如所料,当某些Ritz值开始收敛时.计算出的辛Lanczos向量的J-正交性损失是必然的.以上结果对辛Lanczos算法的改进具有理论指导意义.     

Letter: A Solution of the Weyl–Lanczos Equations for the Schwarzschild Space-Time

The spin coefficient form of the Weyl–Lanczos equations is analysed for the Schwarzschild space-time. The solution obtained yields an alternative form of Lanczos coefficients to the one currently known for this particular metric.