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In (SIAM J. Matrix Anal. Appl., 23 (2002), pp. 706–727, we developed numerical algorithms for computing sparse low-rank approximations of matrices, and we also provided a detailed error analysis of the proposed algorithms together with some numerical experiments. The low-rank approximations are constructed in a certain factored form with the degree of sparsity of the factors controlled by some user-specified parameters. In this paper, we cast the sparse low-rank approximation problem in the framework of penalized optimization problems. We discuss various approximation schemes for the penalized optimization problem which are more amenable to numerical computations. We also include some analysis to show the relations between the original optimization problem and the reduced one. We then develop a globally convergent discrete Newton-like iterative method for solving the approximate penalized optimization problems. We also compare the reconstruction errors of the sparse low-rank approximations computed by our new methods with those obtained using the methods in the earlier paper and several other existing methods for computing sparse low-rank approximations. Numerical examples show that the penalized methods are more robust and produce approximations with factors which have fewer columns and are sparser. [ABSTRACT FROM AUTHOR]

This paper discusses convergence behavior of the nonlinear inexact Uzawa algorithm for solving saddle-point problems presented in a recent paper of Bramble, Pasciak, and Vassilev (SIAM J. Numer. Anal., 34 (1997), pp. 1072­1092). It is shown that this algorithm converges under a condition weaker than that stated in their paper. Key words: saddle-point problems; nonlinear inexact Uzawa algorithm; convergence behavior [ABSTRACT FROM AUTHOR]

In an earlier paper, we developed a new formulation of the shallow-water equations on the sphere, based on the semi-Lagrangian advection of potential vorticity. An important feature of our two-time-level model is that forward extrapolation of the nonlinear terms is avoided. In this paper, we develop a multigrid algorithm for solving the resulting system of three coupled nonlinear equations. We include a discussion of basic multigrid methods, along with some more advanced techniques required for the development of the multigrid scheme for this system. Results are presented showing that usual optimal multigrid efficiency is obtained. [ABSTRACT FROM AUTHOR]

This paper considers an implicitly restarted Krylov subspace method that approximates a stable, linear transfer function f(s) of order n by one of order m, where n >> m. It is well known that oblique projections onto a Krylov subspace may generate unstable partial realizations. To remedy this situation, the oblique projectors obtained via classical Krylov subspace methods are supplemented with further projectors which enable the formation of stable partial realizations directly from f(s). A key feature of this process is that it may be incorporated into an implicit restart scheme. A second difficulty arises from the fact that Krylov subspace methods often generate partial realizations that contain nonessential modes. To this end, balanced truncation may be employed to discard the unwanted part of the reduced-order model. This paper proposes oblique projection methods for large-scale model reduction that simultaneously compute stable reduced-order models while discarding all nonessential modes. It is shown that both of these tasks may be effected by a single oblique projection process. Furthermore, the process is shown to naturally fit into an implicit restart framework. The theoretical properties of these methods are thoroughly investigated, and exact low-dimensional expressions for the L∞-norm of the residual errors are derived. Finally, the behavior of the algorithm is illustrated on two large-scale examples. [ABSTRACT FROM AUTHOR]

Suffix trees and suffix arrays are the most prominent full-text indices, and their construction algorithms are well studied. In the literature, the fastest algorithm runs in O(n) time, while it requires O(n log n)-bit working space, where n denotes the length of the text. On the other hand, the most space-efficient algorithm requires O(n)-bit working space while it runs in O(n log n) time. It was open whether these indices can be constructed in both o(n log n) time and o(n log n)- bit working space. This paper breaks the above time-and-space barrier under the unit-cost word RAM. We give an algorithm for constructing the suffix array, which takes O(n) time and O(n)-bit working space, for texts with constant-size alphabets. Note that both the time and the space bounds are optimal. For constructing the suffix tree, our algorithm requires O(n logϵ n) time and O(n)-bit working space for any 0 < ϵ < 1. Apart from that, our algorithm can also be adopted to build other existing full-text indices, such as compressed suffix tree, compressed suffix arrays, and FM-index. We also study the general case where the size of the alphabet Σ is not constant. Our algorithm can construct a suffix array and a suffix tree using optimal O(n log ∣Σ∣)-bit working space while running in O(n log log ∣Σ∣) time and O(n(logϵ n + log ∣Σ∣)) time, respectively. These are the first algorithms that achieve o(n log n) time with optimal working space. Moreover, for the special case where log ∣Σ∣ = O((log log n)1-ϵ), we can speed up our suffix array construction algorithm to the optimal O(n). [ABSTRACT FROM AUTHOR]

In this paper, an optimal linear-time algorithm is presented to solve the haplotype inference problem for pedigree data when there are no recombinations and the pedigree has no mating loops. The approach is based on the use of graphs to capture SNP, Mendelian, and parity constraints of the given pedigree. This representation allows us to capture the constraints as the edges in a graph, rather than as a system of linear equations as in previous approaches. Graph traversals are then used to resolve the parity of these edges, resulting in an optimal running time. [ABSTRACT FROM AUTHOR]

In this paper we develop a posteriori error estimates for finite element discretization of elliptic optimization problems with pointwise inequality constraints on the control variable. We derive error estimators for assessing the discretization error with respect to the cost functional as well as with respect to a given quantity of interest. These error estimators provide quantitative information about the discretization error and guide an adaptive mesh refinement algorithm allowing for substantial saving in degrees of freedom. The behavior of the method is demonstrated on numerical examples. [ABSTRACT FROM AUTHOR]

This paper presents a multigrid method for solving variable coefficient Maxwell's equations. The novelty in this method is the use of interpolation operators that do not produce multilevel commutativity complexes that lead to multilevel exactness. Rather, the effects of multilevel exactness are built into the level equations themselves—on the finest level using a discrete T – V formulation and on the coarser grids through the Galerkin coarsening procedure of a T – V formulation. These built-in structures permit the levelwise use of an effective hybrid smoother on the curl-free near-nullspace components and permit the development of interpolation operators for handling the curl-free and divergence-free error components separately. The resulting block-diagonal interpolation operator does not satisfy multilevel commutativity but has good approximation properties for both of these error components. Applying operator-dependent interpolation for each of these error components leads to an effective multigrid scheme for variable coefficient Maxwell's equations, where multilevel commutativity-based methods can degrade. Numerical results are presented to verify the effectiveness of this new scheme. [ABSTRACT FROM AUTHOR]

We propose a new algebraic multigrid (AMG) method for solving the eddy current approximations to Maxwell's equations. This AMG method has its roots in an algorithm proposed by Reitzinger and Schöberl. The main focus in the Reitzinger and Schöberl method is to maintain null-space properties of the weak ∇ × ∇ × operator on coarse grids. While these null-space properties are critical, they are not enough to guarantee h-independent convergence rates of the overall multigrid scheme. We present a new strategy for choosing intergrid transfers that not only maintains the important null-space properties on coarse grids but also yields significantly improved multigrid convergence rates. This improvement is related to those we explored in a previous paper, but is fundamentally simpler, easier to compute, and performs better with respect to both multigrid operator complexity and convergence rates. The new strategy builds on ideas in smoothed aggregation to improve the approximation property of an existing interpolation operator. By carefully choosing the smoothing operators, we show how it is sometimes possible to achieve h-independent convergence rates with a modest increase in multigrid operator complexity. Though this ideal case is not always possible, the overall algorithm performs significantly better than the original scheme in both iterations and run time. Finally, the Reitzinger and Schöberl method, as well as our previous smoothed method, are shown to be special cases of this new algorithm. [ABSTRACT FROM AUTHOR]

Recently, D. Corneil found a simple 3-sweep lexicographic breadth first search (LexBFS) algorithm for the recognition of proper interval graphs. We point out how to modify Corneil's algorithm to make it a certifying algorithm, and then describe a similar certifying 3-sweep LexBFS algorithm for the recognition of proper interval bigraphs. It follows from an earlier paper that the class of proper interval bigraphs is equal to the better known class of bipartite permutation graphs, and so we have a certifying algorithm for that class as well. All our algorithms run in time O(m+n), including the certification phase. The certificates of representability (the intervals) can be authenticated in time O(m + n). The certificates of nonrepresentability (the forbidden subgraphs) can be authenticated in time O(n). [ABSTRACT FROM AUTHOR]

This paper formulates several algorithms for the direct computation of periodic orbits as solutions of boundary value problems. The algorithms emphasize the use of coarse meshes and high orders of accuracy. Convergence theorems are given in the limit of increasing order with a fixed mesh. The algorithms are implemented with the use of MATLAB and ADOL-C, a software package for automatic differentiation. Automatic differentiation enables accurate computation of high-order derivatives of functions without the truncation errors inherent in finite difference calculations. We embed the algorithms in a continuation framework and extend them to compute saddle-node bifur- cations of periodic orbits directly. We present data from numerical studies of four test problems, making some comparisons with other methods for computing periodic orbits. These results demonstrate that high-order methods based upon automatic differentiation are capable of high precision with small meshes. [ABSTRACT FROM AUTHOR]

Low-rank approximation of large and/or sparse matrices is important in many applications, and the singular value decomposition (SVD) gives the best low-rank approximations with respect to unitarily-invariant norms. In this paper we show that good low-rank approximations can be directly obtained from the Lanczos bidiagonalization process applied to the given matrix without computing any SVD. We also demonstrate that a so-called one-sided reorthogonalization process can be used to maintain an adequate level of orthogonality among the Lanczos vectors and produce accurate low-rank approximations. This technique reduces the computational cost of the Lanczos bidiagonalization process. We illustrate the efficiency and applicability of our algorithm using numerical examples from several applications areas. [ABSTRACT FROM AUTHOR]

Various forms of sparse approximate inverses (SAI) have been shown to be useful for preconditioning. Their potential usefulness in a parallel environment has motivated much interest in recent years. However, the capability of an approximate inverse in eliminating the local error has not yet been fully exploited in multigrid algorithms. A careful examination of the iteration matrices of these approximate inverses indicates their superiority in smoothing the high-frequency error in addition to their inherent parallelism. We propose a new class of SAI smoothers in this paper and present their analytic smoothing factors for constant coefficient PDEs. The following are several distinctive features that make this technique special: By adjusting the quality of the approximate inverse, the smoothing factor can be improved accordingly. For hard problems, this is useful. In contrast to the ordering sensitivity of other smoothing techniques, this technique is ordering independent. In general, the sequential performance of many superior parallel algorithms is not very competitive. This technique is useful in both parallel and sequential computations. Our theoretical and numerical results have demonstrated the effectiveness of this new technique. [ABSTRACT FROM AUTHOR]

In this paper, we study the additive Schwarz method for the h-p version of the finite element method in three dimensions. The main idea is to treat separately the h-version (linear) components and the p-version (high-order) components by a vertex-based method. It can also be viewed as a three-level method with the level being the linear finite element approximation on the coarse mesh, the linear finite element approximation on the fine mesh, and the high-order finite element approximation on the fine mesh, respectively. The resulting algorithm can be implemented in parallel on the subdomain level for the h-version components and on the element level for the p-version components. The condition number is of order [This symbol cannot be presented in ASCII format](1 + ln Hipi/hi)⇔2, where Hi stands for the diameter of the subdomain Ωi, hi is the diameter of the elements in Ωi, and pi is the maximum of the polynomial degrees used in Ωi. [ABSTRACT FROM AUTHOR]

In this paper the problem of computing bifurcation diagrams for large-scale nonlinear parameter-dependent steady state systems which arise following the spatial discretization of semilinear PDEs is investigated. A continuation algorithm which employs a preconditioned version of the recursive projection method (RPM) is presented. The RPM is often expensive when it is used in conjunction with the numerical method of lines. Preconditioning the Jacobian of the underlying fixed point operator results in an algorithm (the preconditioned recursive projection method (PRPM)) which is capable of efficiently computing equilibrium solution diagrams of large stiff systems. For many PDE problems the PRPM is a fast and effective means of detecting both steady state and Hopf bifurcation along a branch of solutions. A description of the performance of the PRPM when applied to two numerical examples is given. [ABSTRACT FROM AUTHOR]