A new algorithm for solving large inhomogeneous linear system of algebraic equations

An algorithm based on a small matrix approach to the solution of a system of inhomogeneous linear algebraic equations is developed and tested in this short communication. The solution is assumed to lie in an initial subspace and the dimension of the subspace is augmented iteratively by adding the component of the correction vector obtained from the Jacobi scheme on the coefficient matrix A (A^TA, if the matrix A is nondefinite) that is orthogonal to the subspace. If the dimension of the subspace becomes inconveniently large, the iterative scheme can be restarted. The scheme is applicable to both symmetric and nonsymmetric matrices. The small matrix is symmetric (nonsymmetric), if the coefficient matrix is symmetric (nonsymmetric). The scheme has rapid convergence even for large nonsymmetric sparse systems.     

Accurate and efficient determination of higher roots in diagonalization of large matrices based in function restricted optimization algorithms

The problem of large‐scale matrix diagonalization is analyzed in the context of normal function optimization techniques with particular emphasis on the problem of obtaining high roots. New methods based on function restricted optimization algorithms are presented. The efficiency of these methods is illustrated for lowest and higher and degenerate roots of selected matrices. The diagonalization process is commonly carried out in a subspace, and involves a sort of optimization process, and the dimension of this subspace increases at each iteration. In addition, the success of a diagonalization method in obtaining a desired root strongly depends on the particular optimization procedure chosen. In this work, a rational function optimization procedure is presented that permits obtaining the lowest and higher eigenpairs in an efficient way. Update Hessian matrices formulae, routinely used in normal function optimization problems, are explored in the framework of diagonalization techniques. Finally, a diagonalization method with a fixed subspace dimension during the iterative process is presented. Some examples focused in lowest, higher and degenerate eigenpairs are discussed. © 2000 John Wiley & Sons, Inc. J Comput Chem 21: 1375–1386, 2000     

iVI-TD-DFT: An iterative vector interaction method for exterior/interior roots of TD-DFT

The recently proposed iterative vector interaction (iVI) method for large Hermitian eigenvalue problems (Huang et al., J. Comput. Chem. 2017, 38, 2481) is extended to generalized eigenvalue problems, HC = SCE , with the metric S being either positive definite or not. Although, it works with a fixed-dimensional search subspace, iVI can converge quickly and monotonically from above to the exact exterior/interior roots. The algorithms are further specialized to nonrelativistic and relativistic time-dependent density functional theories (TD-DFT) by taking the orbital Hessian as the metric (i.e., the inverse TD-DFT eigenvalue problem) and incorporating explicitly the paired structure into the trial vectors. The efficacy of iVI-TD-DFT is demonstrated by various examples. © 2018 Wiley Periodicals, Inc.     

大型广义特征值问题的部分特征值和特征向量的块迭代求解

将求解标准特征值问题的Davidson方法推广到求解大型广义特征值问题, 并给出了相应的块迭代算法. 经过理论分析和数值计算发现, 如果迭代过程不发散, 则块迭代算法经过有限次迭代一定收敛. 设矩阵的维数为n, 要求的特征值和相应特征向量的个数为k, 初始的子空间大小为r(r≥k),迭代次数为m,则它们之间满足关系n=r+km. 通过调节子空间大小, 就得到迭代次数m的正整数解.     

An efficient nonlinear programming strategy for PCA models with incomplete data sets

Processing plants can produce large amounts of data that process engineers use for analysis, monitoring, or control. Principal component analysis (PCA) is well suited to analyze large amounts of (possibly) correlated data, and for reducing the dimensionality of the variable space. Failing online sensors, lost historical data, or missing experiments can lead to data sets that have missing values where the current methods for obtaining the PCA model parameters may give questionable results due to the properties of the estimated parameters. This paper proposes a method based on nonlinear programming (NLP) techniques to obtain the parameters of PCA models in the presence of incomplete data sets. We show the relationship that exists between the nonlinear iterative partial least squares (NIPALS) algorithm and the optimality conditions of the squared residuals minimization problem, and how this leads to the modified NIPALS used for the missing value problem. Moreover, we compare the current NIPALS‐based methods with the proposed NLP with a simulation example and an industrial case study, and show how the latter is better suited when there are large amounts of missing values. The solutions obtained with the NLP and the iterative algorithm (IA) are very similar. However when using the NLP‐based method, the loadings and scores are guaranteed to be orthogonal, and the scores will have zero mean. The latter is emphasized in the industrial case study. Also, with the industrial data used here we are able to show that the models obtained with the NLP were easier to interpret. Moreover, when using the NLP many fewer iterations were required to obtain them. Copyright © 2010 John Wiley & Sons, Ltd.     

An efficient algorithm for solving nonlinear equations with a minimal number of trial vectors: applications to atomic-orbital based coupled-cluster theory

The conjugate residual with optimal trial vectors (CROP) algorithm is developed. In this algorithm, the optimal trial vectors of the iterations are used as basis vectors in the iterative subspace. For linear equations and nonlinear equations with a small-to-medium nonlinearity, the iterative subspace may be truncated to a three-dimensional subspace with no or little loss of convergence rate, and the norm of the residual decreases in each iteration. The efficiency of the algorithm is demonstrated by solving the equations of coupled-cluster theory with single and double excitations in the atomic orbital basis. By performing calculations on H(2)O with various bond lengths, the algorithm is tested for varying degrees of nonlinearity. In general, the CROP algorithm with a three-dimensional subspace exhibits fast and stable convergence and outperforms the standard direct inversion in iterative subspace method.     

A perturbation-based super-CI approach for the orbital optimization of a CASSCF wave function

A perturbation theory-based algorithm for the iterative orbital update in complete active space self-consistent-field (CASSCF) calculations is presented. Following Angeli et al. (J. Chem. Phys. 2002, 117, 10525), the first-order contribution of singly excited configurations to the CASSCF wave function is evaluated using the Dyall Hamiltonian for the determination of a zeroth-order Hamiltonian. These authors employ an iterative diagonalization of the first-order density matrix including the first-order correction arising from single excitations, whereas the present approach uses the single-excitation amplitudes directly for the construction of the exponential of an anti-Hermitian matrix resulting in a unitary matrix which can be used for the orbital update. At convergence, the single-excitation amplitudes vanish as a consequence of the generalized Brillouin's theorem. It is shown that this approach in combination with direct inversion of the iterative subspace (DIIS) leads to very rapid convergence of the CASSCF iteration procedure. © 2019 Wiley Periodicals, Inc.     

Functional analysis concepts and Fartree-Fock instability conditions

Concepts of functional analysis, namely, regular points, tangent subspaces, constraint surfaces, Lagrangian matrix restricted to the tangent subspace of a constraint surface, are presented in connection with the Hartree-Fock (HF) problem. The energy functional in LCAO approximation is considered to be a polynomial function of several variables subject to subsidiary conditions. General HF equations and instability conditions for the unrestricted Hartree-Fock (UHF) solutions are derived from this standpoint.     

Linear-scaling implementation of molecular response theory in self-consistent field electronic-structure theory

A linear-scaling implementation of Hartree-Fock and Kohn-Sham self-consistent field theories for the calculation of frequency-dependent molecular response properties and excitation energies is presented, based on a nonredundant exponential parametrization of the one-electron density matrix in the atomic-orbital basis, avoiding the use of canonical orbitals. The response equations are solved iteratively, by an atomic-orbital subspace method equivalent to that of molecular-orbital theory. Important features of the subspace method are the use of paired trial vectors (to preserve the algebraic structure of the response equations), a nondiagonal preconditioner (for rapid convergence), and the generation of good initial guesses (for robust solution). As a result, the performance of the iterative method is the same as in canonical molecular-orbital theory, with five to ten iterations needed for convergence. As in traditional direct Hartree-Fock and Kohn-Sham theories, the calculations are dominated by the construction of the effective Fock/Kohn-Sham matrix, once in each iteration. Linear complexity is achieved by using sparse-matrix algebra, as illustrated in calculations of excitation energies and frequency-dependent polarizabilities of polyalanine peptides containing up to 1400 atoms.     

An improved iterative solution to solve the electrostatic problem in the polarizable continuum model

 We present a discrete iterative interpolation scheme (DIIS) to improve the convergence rate of electrostatic calculations in the polarizable continuum model (PCM) to describe solvent effects on molecular solutes. The electrostatic calculations may easily become the bottleneck of the calculation when the solute size is large. For large molecules iterative procedures turn out to be computationally more convenient than matrix inversion or closure methods. The DIIS scheme is compared here to another iterative procedure (DAMP) and to the biconjugate gradient (BCG) method. The comparisons show that DIIS leads to a sizeable saving of computational time for the C-PCM and IEF-PCM methods (average 40%) compared to DAMP, and more than 50% with respect to the BCG method. Received: 5 October 2000 / Accepted: 13 November 2000 / Published online: 19 January 2001     

Effects of concentrated matrices on the determination of trace levels of platinum and gold in aqueous samples using solvent extraction—Zeeman effect graphite furnace atomic absorption spectrometry and inductively coupled plasma—mass spectrometry

Two methods of determining trace levels of platinum and gold in aqueous solutions with high concents of total dissolved solids were investigated. The first involves preconcentration and separation of the precious metals from the interfering matrix by solvent extraction, followed by graphite furnace atomic absorption spectrometry (GFAAS) with Zeeman effect background correction. The direct determination of Pt and Au in solutions of high ionic strength by GFAAS is not desirable because of interference between elements in the matrix and the analyte, increased imprecision of analysis, greatly increased background absorbance leading to increased detection limits and rapid deterioration of the graphite tube. All the extraction methods for gold examined in this study resulted in decreased imprecision, increased sensitivity and lower background absorbance compared with direct measurements on the aqueous solution. All techniques also exhibited good recoveries (> 8%) and reproducibilities (relative standard deviation < 10%). The highest sensitivities for gold extraction from distilled water were obtained for dibutyl sulfide (DBS)—toluene and the lowest for cyanide—dibutyl ketone. The degree of extraction of Au was, however, dependent on the composition of the solution, indicating that standard and sample matrices should be closely matched even when employing solvent extraction. Solvent extraction was generally less successful for Pt. In order to obtain an acceptable imprecision in the Pt extractions, it was found that the use of SnCl₂ as a labilizing agent is essential for most of the techniques investigated.The second method was direct measurement by inductively coupled plasma mass spectrometry (ICP—MS). ICP—MS offers the advantages of a very low detection limit (100 ng l^?1 or better) without preconcentration and a large dynamic range. However, severe matrix effects can occur in concentrated solutions. Whereas high concentrations in solution of both sodium perchlorate and sodium chloride decrease the sensitivity, the presence of sulfide and natural organic (fulvic) acid increase the sensitivity for Pt and Au by a factor of up to 4. Sulfate, on the other hand, decreases the sensitivity of ICP-MS for Pt. The method of standard additions or isotope dilution is recommended for routine use to circumvent this problem, especially when the nature of the matrix is unknown or cannot be easily matched in the standards.     

Iterative solution of Bloch-type equations: stability conditions and chaotic behavior

Two different form of nonperturbative Bloch-type equations are studied: one for the wave operator of the N-electron Schr?dinger equation, another one for obtaining first-order density matrix P in one-electron theories (Hartree–Fock or Kohn–Sham). In both cases, we investigate the possibility of an iterative solution of the nonlinear Bloch equation. To have a closer view on convergence features, we determine the stability matrix of the iterative procedures and determine the Ljapunov exponents from its eigenvalues. For some of the cases when not every exponents are negative, chaotic solutions can be identified, which should of course be carefully avoided in practical iterations.     

Dynamical variational approach to non-adiabatic electronic structure

Studying non-adiabatic effects in molecular dynamics simulations and modeling their optical signatures in linear and non-linear spectroscopies calls for electronic structure calculations in a situation when the ground state is degenerate or almost degenerate. Such degeneracy causes serious problems in invoking single Slater determinant Hartree–Fock (HF) and density functional theory (DFT) methods. To resolve this problem, we develop a generalization of time-dependent (dynamical) variational approach which accounts for the degenerate or almost degenerate ground state structure. Specifically, we propose a ground state ansatz for the subspace of generalized electronic configurations spanned on the degenerate grounds state multi-electron wavefunctions. Further employing the invariant form of Hamilton dynamics we arrive with the classical equations of motion describing the time-evolution of this subspace in the vicinity of the stationary point. The developed approach can be used for accurate calculations of molecular excited states and electronic spectra in the degenerate case.     

Multiple solutions of coupled-cluster doubles equations for the Pariser–Parr–Pople model of benzene

To gain some insight into the structure and physical significance of the multiple solutions to the coupled-cluster doubles (CCD) equations corresponding to the Pariser–Parr–Pople model of cyclic polyenes, complete solutions to the CCD equations for the ¹A _1g ^- states of benzene are obtained by means of the homotopy method. By varying the value of the resonance integral ß from –5.0 to –0.5 eV, we cover the so-called weakly, moderately, and strongly correlated regimes of the model. For each value of ß, 230 CCD solutions are obtained. It turned out, however, that only for a few solutions a correspondence with some physical states can be established. It has also been demonstrated that, unlike for the standard methods of solving CCD equations, some of the multiple solutions to the CCD equations can be attained by means of the iterative process based on Pulay's direct inversion in the iterative subspace approach.     

Systematic Study of Selected Diagonalization Methods for Configuration Interaction Matrices

Several modifications to the Davidson algorithm are systematically explored to establish their performance for an assortment of configuration interaction (CI) computations. The combination of a generalized Davidson method, a periodic two‐vector subspace collapse, and a blocked Davidson approach for multiple roots is determined to retain the convergence characteristics of the full subspace method. This approach permits the efficient computation of wave functions for large‐scale CI matrices by eliminating the need to ever store more than three expansion vectors ( b _i) and associated matrix‐vector products ( σ _i), thereby dramatically reducing the I/O requirements relative to the full subspace scheme. The minimal‐storage, single‐vector method of Olsen is found to be a reasonable alternative for obtaining energies of well‐behaved systems to within μE_h accuracy, although it typically requires around 50% more iterations and at times is too inefficient to yield high accuracy (ca. 10^?10 E_h) for very large CI problems. Several approximations to the diagonal elements of the CI Hamiltonian matrix are found to allow simple on‐the‐fly computation of the preconditioning matrix, to maintain the spin symmetry of the determinant‐based wave function, and to preserve the convergence characteristics of the diagonalization procedure. © 2001 John Wiley & Sons, Inc. J Comput Chem 22: 1574–1589, 2001     

Efficient treatment of solvation shells in 3D molecular theory of solvation

We developed a technique to decrease memory requirements when solving the integral equations of three‐dimensional (3D) molecular theory of solvation, a.k.a. 3D reference interaction site model (3D‐RISM), using the modified direct inversion in the iterative subspace (MDIIS) numerical method of generalized minimal residual type. The latter provides robust convergence, in particular, for charged systems and electrolyte solutions with strong associative effects for which damped iterations do not converge. The MDIIS solver (typically, with 2 × 10 iterative vectors of argument and residual for fast convergence) treats the solute excluded volume (core), while handling the solvation shells in the 3D box with two vectors coupled with MDIIS iteratively and incorporating the electrostatic asymptotics outside the box analytically. For solvated systems from small to large macromolecules and solid–liquid interfaces, this results in 6‐ to 16‐fold memory reduction and corresponding CPU load decrease in MDIIS. We illustrated the new technique on solvated systems of chemical and biomolecular relevance with different dimensionality, both in ambient water and aqueous electrolyte solution, by solving the 3D‐RISM equations with the Kovalenko–Hirata (KH) closure, and the hypernetted chain (HNC) closure where convergent. This core–shell‐asymptotics technique coupling MDIIS for the excluded volume core with iteration of the solvation shells converges as efficiently as MDIIS for the whole 3D box and yields the solvation structure and thermodynamics without loss of accuracy. Although being of benefit for solutes of any size, this memory reduction becomes critical in 3D‐RISM calculations for large solvated systems, such as macromolecules in solution with ions, ligands, and other cofactors. © 2012 Wiley Periodicals, Inc.     

New iterative scheme for a simultaneous calculation of m first eigenstates of a real symmetric matrix

A new iterative scheme for a simultaneous calculation of the m lowest eigenvalues together with their eigenvectors has been derived for a real symmetric matrix. The scheme is based on the orthogonal gradient method and is easy to use for large-scale configuration-interaction calculations of electronic wave functions. A variant of the scheme deals with nonorthogonal basis functions, which are particularly simple in the case of the bonded-function method of Boys.     

iVI: An iterative vector interaction method for large eigenvalue problems

Based on the generic “static‐dynamic‐static” framework for strongly coupled basis vectors (Liu and Hoffman, Theor. Chem. Acc. 2014, 133, 1481), an iterative Vector Interaction (iVI) method is proposed for computing multiple exterior or interior eigenpairs of large symmetric/Hermitian matrices. Although it works with a fixed‐dimensional search subspace, iVI can converge quickly and monotonically from above to the exact exterior/interior roots. The efficacy of iVI is demonstrated by taking both mathematical and physical matrices as examples. © 2017 Wiley Periodicals, Inc.     

Elimination of the diagonalization bottleneck in parallel Direct-SCF methods

Summary It is shown that the matrix diagonalization bottleneck associated with thesequential O(N _BFN ³ ) diagonalization of the fock matrix within each iteration of the Direct-SCF procedure may be eliminated, and replaced instead with a combination ofparallel O(N _BFN ^<4 ) andsequential O(N _Sub ³ ) steps. For large basis sets, the relation N_Sub N_BFN between the dimension of the expansion subspace and the number of basis functions leads to a method of wave-function optimization in which the sequential bottleneck is eliminated. As a side benefit, the second-order iterative procedure on which this method is based displays superior convergence properties, and provides greater insight into the behavior of the energy with respect to orbital variations, than the traditional first-order, fixed-point, iterative approaches. The implementation of this method may be incorporated into essentially any existing Direct-SCF program with only minimal, and localized, changes.