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We propose in this paper a new minimization algorithm based on a slightly modified version of the scalar auxialiary variable (SAV) approach coupled with a relaxation step and an adaptive strategy. It enjoys several distinct advantages over popular gradient based methods: (i) it is unconditionally energy diminishing with a modified energy which is intrinsically related to the original energy, and thus no parameter tuning is needed for stability; (ii) it allows the use of large step-sizes which can effectively accelerate the convergence rate. We also present a convergence analysis for some SAV based algorithms, which include our new algorithm without the relaxation step as a special case. We apply our new algorithm to several illustrative and benchmark problems and compare its performance with several popular gradient based methods. The numerical results indicate that the new algorithm is very robust, and its adaptive version usually converges significantly faster than those popular gradient decent based methods. [ABSTRACT FROM AUTHOR]

This paper proposes some efficient and accurate adaptive two-grid (ATG) finite element algorithms for linear and nonlinear PDEs. The main idea of these algorithms is to utilize the solutions on the kth-level adaptive meshes to find the solutions on the (k + 1)th-level adaptive meshes which are constructed by performing adaptive element bisections on the k th-level adaptive meshes. These algorithms transform nonsymmetric positive definite (non-SPD) PDEs (resp., nonlinear PDEs) into symmetric positive definite (SPD) PDEs (resp., linear PDEs). The proposed algorithms are both accurate and efficient due to the following advantages: They do not need to solve the nonsymmetric or nonlinear systems; the degrees of freedom are very small, they are easily implemented, and the interpolation errors are very small. Next, this paper constructs residual-type a posteriori error estimators which are shown to be reliable and efficient. The key ingredient in proving the efficiency is to establish an upper bound of the oscillation terms, which may not be higher-order terms due to the low regularity of the numerical solution. Furthermore, the convergence of the algorithms is proved when bisection is used for the mesh refinements. Finally, numerical experiments are provided to verify the accuracy and efficiency of the ATG finite element algorithms compared to regular adaptive finite element algorithms and two-grid finite element algorithms [J. Xu, SIAM J. Numer. Anal., 33 (1996), pp. 1759-1777]. [ABSTRACT FROM AUTHOR]

The integral equation method is widely used in numerical simulations of 2D/3D acoustic and electromagnetic scattering problems, which need a large number of values of the Green's functions. A significant topic is the scattering problems in periodic domains, where the corresponding Green's functions are quasi-periodic. The quasi-periodic Green's functions are defined by series that converge too slowly to be used for calculations. Many mathematicians have developed several efficient numerical methods to calculate quasi-periodic Green's functions. In this paper, we propose a new FFT-based fast algorithm to compute the 2D/3D quasi-periodic Green's functions for both the Helmholtz equations and Maxwell's equations. The convergence results and error estimates are also investigated in this paper. Further, the numerical examples are given to show that, when a large number of values is needed, the new algorithm is very competitive. [ABSTRACT FROM AUTHOR]

In this paper, two finite difference/element approaches for the time-fractional subdiffusion equation with Dirichlet boundary conditions are developed, in which the time direction is approximated by the fractional linear multistep method and the space direction is approximated by the finite element method. The two methods are unconditionally stable and convergent of order O(τq +hr+1) in the L² norm, where q = 2-β or 2 when the analytical solution to the subdiffusion equation is sufficiently smooth, β (0 < β < 1) is the order of the fractional derivative, τ and h are the step sizes in time and space, respectively, and r is the degree of the polynomial space. The corresponding schemes for the subdiffusion equation with Neumann boundary conditions are presented as well, where the stability and convergence are shown. Numerical examples are provided to verify the theoretical analysis. Comparisons between the algorithms derived in this paper and the existing algorithms are given, which show that our numerical schemes exhibit better performances than the existing ones. [ABSTRACT FROM AUTHOR]

Spectral deferred corrections (SDC) are a class of iterative methods for the numerical solution of ordinary differential equations. SDC can be interpreted as a Picard iteration to solve a fully implicit collocation problem, preconditioned with a low-order method. It has been widely studied for first-order problems, using explicit, implicit, or implicit-explicit Euler and other low-order methods as preconditioner. For first-order problems, SDC achieves arbitrary order of accuracy and possesses good stability properties. While numerical results for SDC applied to the second-order Lorentz equations exist, no theoretical results are available for SDC applied to second-order problems. We present an analysis of the convergence and stability properties of SDC using velocity-Verlet as the base method for general second-order initial value problems. Our analysis proves that the order of convergence depends on whether the force in the system depends on the velocity. We also demonstrate that the SDC iteration is stable under certain conditions. Finally, we show that SDC can be computationally more efficient than a simple Picard iteration or a fourth-order Runge-Kutta--Nyström method. [ABSTRACT FROM AUTHOR]

The alternating direction method of multipliers (ADMM) has been extensively used in a wide variety of different applications. When large datasets with high-dimensional variables are considered, subproblems arising from the ADMM must be inexactly solved even though they may theoretically have closed-form solutions. Such a scenario immediately poses mathematical ambiguities such as how these subproblems should be accurately solved and whether the convergence can still be guaranteed. Although the ADMM is well known, it seems that these topics should be deeply investigated. In this paper, we study the mathematics of how to implement the ADMM for a large dataset scenarios. More specifically, we attempt to focus on the convex programming case where there is a quadratic function with extremely high-dimensional variables in the objective function of the model; thereby there is a huge-scale system for linear equations needing to be solved at each iteration of the ADMM. It is revealed that there is no need, indeed it is impossible, to exactly solve this linear system, and we attempt to propose an adjustable inexactness criterion to automatically and inexactly solve this linear system. We further attempt to identify the safe-guard number for the internally nested iterations that can sufficiently ensure this inexactness criterion if the linear system would be solved by a standard numerical linear algebra solver. The convergence, together with the worst-case convergence rate measured by the iteration complexity, is rigorously established for the ADMM with inexactly solved subproblems. Some numerical experiments for large datasets of the least absolute shrinkage and selection operator containing millions of variables are reported to show the efficiency of the mentioned inaccurate implementation of the ADMM. [ABSTRACT FROM AUTHOR]

In this paper, we consider a classical algebraic multigrid (AMG) form of optimal interpolation that directly minimizes the two-grid convergence rate and compare it with a so-called ideal interpolation that minimizes a weak approximation property of the coarse space. We study compatible relaxation type estimates for the quality of the coarse grid and derive a new sharp measure using optimal interpolation that provides a guaranteed lower bound on the convergence rate of the resulting two-grid method for a given grid. In addition, we design a generalized bootstrap AMG setup algorithm that computes a sparse approximation to the optimal interpolation matrix. We demonstrate numerically that the bootstrap AMG method with sparse interpolation matrix (and spanning multiple levels) converges faster than the two-grid method with the standard ideal interpolation (a dense matrix) for various scalar diffusion problems with highly varying diffusion coefficient. [ABSTRACT FROM AUTHOR]

In this paper, two applications of linear barycentric rational interpolation are used to derive a difference-quadrature scheme for solving a class of systems of Volterra integro-differential equations of the second kind. The convergence of the proposed method is proved, and the order of convergence is obtained in terms of the parameters of the method. Furthermore, the linear stability of the proposed method with respect to both the basic and convolution test equations is analyzed. All the obtained theoretical results are verified by several numerical experiments. [ABSTRACT FROM AUTHOR]

The shifted-inverse iteration based on the multigrid discretizations developed in recent years is an efficient computation method for eigenvalue problems. In this paper, for general self-adjoint eigenvalue problems, including the Maxwell eigenvalue problem and integral operator eigenvalue problem, we establish the inverse iteration with fixed shift based on multigrid discretizations. We study in depth the inverse iteration with fixed shift and Rayleigh quotient iteration based on multigrid discretizations and first prove under general conditions the error estimates and convergence for the iterative solution approximating the exact solution of the original eigenvalue problems, especially in an adaptive fashion. Finally, we present some numerical examples performed to validate our theoretical results. [ABSTRACT FROM AUTHOR]

In this paper, a novel compact operator is derived for the approximation of the Riesz derivative with order α ∈ (1, 2]. The compact operator is proved with fourth-order accuracy. Combining the compact operator in space discretization, a linearized difference scheme is proposed for a two-dimensional nonlinear space fractional Schrödinger equation. It is proved that the difference scheme is uniquely solvable, stable, and convergent with order O(τ² + h4), where τ is the time step size, h = max{h1, h2}, and h1, h2 are space grid sizes in the x direction and the y direction, respectively. Based on the linearized difference scheme, a compact alternating direction implicit scheme is presented and analyzed. Numerical results demonstrate that the compact operator does not bring in extra computational cost but improves the accuracy of the scheme greatly. [ABSTRACT FROM AUTHOR]

In the image processing community, there have recently been many restoration and reconstruction problems that can be reformulated into linearly constrained convex programming models whose objective functions have separable structures. These favorable reformulations have promoted impressive applications of the alternating direction method (ADM) in the field of image processing. At each iteration, the computation of ADM is dominated by solving two subproblems exactly. However, in many restoration and reconstruction applications, it is cither impossible or extremely expensive to obtain exact solutions of these ADM subproblems. This fact urges the development on inexact versions of ADM, which allow the generated ADM subproblems to be solved approximately subject to certain inexactness criteria. In this paper, we develop some truly implementable inexact ADMs whose inexactness criteria controlling the accuracy of the ADM subproblems are easily implementable. The convergence of the new inexact ADMs will be proved. Numerical results on several image processing problems will be given to illustrate the effectiveness of the proposed inexact ADMs. [ABSTRACT FROM AUTHOR]

In this paper, we consider the issue of convergence toward entropy solutions for the high order finite volume weighted essentially nonoscillatory (WENO) scheme and the discontinuous Galerkin (DG) finite element method approximating scalar nonconvex conservation laws. Although such high order nonlinearly stable schemes can usually converge to entropy solutions of convex conservation laws, convergence may fail for certain nonconvex conservation laws. We perform a detailed study to demonstrate such convergence issues for a few representative examples and suggest a modification of the high order schemes based on either first order monotone schemes or a second order entropic projection [Bouchut, Bourdarias, and Perthame, Math. Comp., 65 (1996), pp. 1438-1461] to achieve convergence toward entropy solutions while maintaining high order accuracy in smooth regions. [ABSTRACT FROM AUTHOR]

We consider in this paper the wave scattering problem by an object with Neumann boundary conditions in an anisotropic elastic body. To obtain an efficient numerical method (permitting the use of regular grids) we follow a fictitious domain approach coupled with a first order velocity stress formulation for elastodynamics. We first observe that the method does not always converge when the Qdiv1 - Q0 finite element is used. In particular, the method converges for some scattering object geometries but not for others. Note that the convergence of the Qdiv1 -Q0 finite element method was shown in [E. Bécache, P. Joly, and C. Tsogka, SIAM J. Numer. Anal., 39 (2002), pp. 2109-2132] for the elastodynamic problem in the absence of a scattering object (i.e., without the coupling of the mixed finite elements with the fictitious domain method). Therefore we propose here a modification of the Qdiv1 - Q0 element following the approach in [E. Bécache, J. Rodríguez, and C. Tsogka, On the convergence of the fictitious domain method for wave equation problems, Technical report 5802, INRIA, 2006], where the simpler acoustic case was considered. To study the numerical properties of the new element we carry out a dispersion analysis. Several numerical simulations as well as a numerical convergence analysis show that the proposed method provides a good approximate solution. [ABSTRACT FROM AUTHOR]

This paper is concerned with monotone iterative methods for numerical solutions of a class of nonlinear fourth-order elliptic boundary value problems in a two-dimensional domain. The boundary value problem is discretized by the finite difference method, and two iterative processes, called block Jacobi and block Gauss­Seidel monotone iterations, are presented for the computation of solutions of the finite difference system using either an upper solution or a lower solution as the initial iteration. It is shown that the sequence of iterations converges monotonically to a maximal solution or a minimal solution if the nonlinear function is quasi-monotone nondecreasing. A sufficient condition is given to ensure that the maximal and minimal solutions coincide and their common value is the unique solution of the finite difference system. Similar results are obtained for quasi-monotone nonincreasing functions. An analytical comparison relation between the block Jacobi and block Gauss­Seidel monotone iterations is obtained. It is also shown that the finite difference solution converges to the continuous solution as the mesh size tends to zero. Numerical results by the block monotone iterative schemes are given for two model problems and are compared with the known analytical solutions for accuracy. Also compared are the rates of convergence of the two types of block monotone iterations as well as similar types of pointwise monotone iterations. [ABSTRACT FROM AUTHOR]

This paper deals with a solution approach suitable for composite partial differential equations (PDEs) with interface conditions. We present a general framework based on interface relaxation which provides a uniform platform for building problem solving environments through efficient software integration and for implementing various relaxation schemes. Mathematically, this framework contains many existing domain decomposition methods and also allows the extension to a variety of new relaxers. In particular, we describe a class of relaxers which are suitable for general and complicated interface conditions. Interface relaxation is more general than the traditional domain decomposition methods in that it allows unrelated PDE problems on different subdomains. Convergence analysis, error estimates, and preconditioning strategies are presented which show that these relaxers are competitive with existing domain decomposition methods for model problems involving a single PDE. We present experimental results which demonstrate the wide applicability of this approach. Differences between this approach and other domain decomposition methods are also discussed. [ABSTRACT FROM AUTHOR]

We propose a new numerical method to solve the linearized problem of the travel time tomography with incomplete data. Our method is based on the technique of the truncation of the Fourier series with respect to a special basis of L². This way we derive a boundary value problem for a system of coupled PDEs of the first order. This problem is solved by the quasi-reversibility method. The spatially dependent Fourier coefficients of the solution to the linearized eikonal equation are obtained this way. Numerical results for highly noisy data are presented. [ABSTRACT FROM AUTHOR]

This article investigates the numerical approximation of shape optimization problems with PDE constraint on classes of convex domains. The convexity constraint provides a compactness property which implies well posedness of the problem. Moreover, we prove the convergence of discretizations in two-dimensional situations. A numerical algorithm is devised that iteratively solves the discrete formulation. Numerical experiments show that optimal convex shapes are generally nonsmooth and that three-dimensional problems require an appropriate relaxation of the convexity condition. [ABSTRACT FROM AUTHOR]

Recent research efforts aimed at iteratively solving the Helmholtz equation have focused on incorporating deation techniques for accelerating the convergence of Krylov subpsace methods. The requisite for these efforts lies in the fact that the widely used and well-acknowledged complex shifted Laplacian preconditioner (CSLP) shifts the eigenvalues of the preconditioned system towards the origin as the wave number increases. The two-level-deation preconditioner combined with CSLP showed encouraging results in moderating the rate at which the eigenvalues approach the origin. However, for large wave numbers the initial problem resurfaces and the near-zero eigenvalues reappear. Our findings reveal that the reappearance of these near-zero eigenvalues occurs if the near-singular eigenmodes of the fine-grid operator and the coarse-grid operator are not properly aligned. This misalignment is caused by accumulating approximation errors during the inter-grid transfer operations. We propose the use of higher-order approximation schemes to construct the deation vectors. The results from rigorous Fourier analysis and numerical experiments confirm that our newly proposed scheme outperforms any other deation-based preconditioner for the Helmholtz problem. In particular, the spectrum of the adjusted preconditioned operator stays fixed near one. These results can be generalized to general shifted indefinite systems with random right-hand sides. For the first time, the convergence properties for very large wave numbers (k = 106 in one dimension and k = 103 in two dimensions) have been studied, and the convergence is close to wave number independence. Wave number independence for three dimensions has been obtained for wave numbers up to k = 75. The new scheme additionally shows very promising results for the more challenging Marmousi problem. Irrespective of the strongly varying wave number, we obtain a constant number of iterations and a reduction in computational time as the results remain robust without the use of the CSLP preconditioner. [ABSTRACT FROM AUTHOR]

Algebraic multigrid (AMG) is one of the fastest numerical methods for solving large sparse linear systems. For SPD matrices, convergence of AMG is well motivated in the A-norm, and AMG has proven to be an effective solver for many applications. Recently, several AMG algorithms have been developed that are effective on nonsymmetric linear systems. Although motivation was provided in each case, the convergence of AMG for nonsymmetric linear systems is still not well understood, and algorithms are based largely on heuristics or incomplete theory. For multigrid restriction and interpolation operators, R and P, respectively, let I - II: = I - P (RAP)-1 RA denote the pro jection corresponding to coarse-grid correction in AMG. It is invariably the case in the nonsymmetric setting that |II| = |I - II| > 1 in any known norm. This causes an interesting dichotomy: coarse-grid correction is fundamental to AMG achieving fast convergence, but, in this case, can actually increase the error. Here, we present a detailed analysis of nonsymmetric AMG, discussing why SPD theory breaks down in the nonsymmetric setting, and developing a general framework for convergence of NS-AMG. Classical multigrid weak and strong approximation properties are generalized to a fractional approximation property. Conditions are then developed on R and P to ensure that ||II|✓A*A is nicely bounded, independent of problem size. This is followed by the development of conditions for two-grid and multilevel W-cycle convergence in the ✓A*A-norm. [ABSTRACT FROM AUTHOR]

We propose an extension to our monotone and convergent method for the Monge--Ampére equation in dimension d \geq 2 that incorporates the idea of filtered schemes. The method combines our original monotone operator with a more accurate nonmonotone modification, using an appropriately chosen filter. This results in a remarkable improvement of accuracy, but without sacrificing the convergence to the unique viscosity solution. [ABSTRACT FROM AUTHOR]

An adaptive finite element method for eigenvalue problems is proposed based on the multilevel correction scheme. Different from the standard adaptive finite element method which requires solving eigenvalue problems on adaptively refined triangulations, our scheme only includes solving associated boundary value problems on adaptive triangulations and small scale eigenvalue problems on a very low dimensional space. Since there is no eigenvalue problem to be solved on adaptively refined triangulations, which needs more computation and memory than solving associated boundary value problem, the efficiency of the proposed method can be improved to be similar to that of the adaptive finite element method for associated boundary value problems. The convergence and optimal complexity is theoretically verified and numerically demonstrated. [ABSTRACT FROM AUTHOR]

We present a systematic and constructive methodology to devise various hybridized discontinuous Galerkin (HDG) methods for linearized shallow water equations. It is shown that using the Rankine--Hugoniot condition to solve the Riemann problem is a natural approach to deriving HDG methods. At the heart of our development is an upwind HDG framework obtained by hybridizing the upwind flux in the standard discontinuous Galerkin (D G) approach. Essentially, the HDG framework is a redesign of the standard DG approach to reducing the number of coupled unknowns. An upwind and three other HDG methods are constructed and analyzed for linearized shallow water systems. Rigorous stability and convergence analysis for both semidiscrete and fully discrete systems are provided. We extend the upwind HDG method to a family of penalty HDG schemes and rigorously analyze their well-posedness, stability, and convergence rates. Numerical results for the linear standing wave and the Kelvin wave for oceanic shallow water systems are presented to verify our theoretical findings. [ABSTRACT FROM AUTHOR]

We present a computational framework for analyzing thin shell structures using the finite element method. The framework is based on a mesh-dependent shell model which we derive from the general laws of three-dimensional elasticity. We apply the framework for the so-called Girkmann benchmark problem involving a spherical shell stiffened with a foot ring. In particular, we compare the accuracy of different reduced strain four-node elements in this context. We conclude that the performance of the bilinear shell finite elements depends on the mesh quality but reasonable accuracy of the quantities of interest of the Girkmann problem can be attained in contrast to earlier results obtained with general shell elements for the problem. [ABSTRACT FROM AUTHOR]

We analyze the extension of summation-by-parts operators and weak boundary conditions for solving initial boundary value problems involving second derivatives in time. A wide formulation is obtained by first rewriting the problem on first order form. This formulation leads to optimally sharp fully discrete energy estimates that are unconditionally stable and high order accurate. Furthermore, it provides a natural way to impose mixed boundary conditions of Robin type, including time and space derivatives. We apply the new formulation to the wave equation and derive optimal fully discrete energy estimates for general Robin boundary conditions, including nonreflecting ones. The scheme utilizes wide stencil operators in time, whereas the spatial operators can have both wide and compact stencils. Numerical calculations verify the stability and accuracy of the method. We also include a detailed discussion on the added complications when using compact operators in time and give an example showing that an energy estimate cannot be obtained using a standard second order accurate compact stencil. [ABSTRACT FROM AUTHOR]

This article aims to fill in the gap of the second-order accurate schemes for the time-fractional subdiffusion equation with unconditional stability. Two fully discrete schemes are first proposed for the time-fractional subdiffusion equation with space discretized by finite element method and time discretized by the fractional linear multistep methods. These two methods are unconditionally stable with maximum global convergence order of O(τ + hr+1) in the L² norm, where τ and h are the step sizes in time and space, respectively, and r is the degree of the piecewise polynomial space. The average convergence rates for the two methods in time are also investigated, which shows that the average convergence rates of the two methods are O(τ1.5 + hr+1). Furthermore, two improved algorithms are constrcted, and they are also unconditionally stable and convergent of order O(τ² + hr+1). Numerical examples are provided to verify the theoretical analysis. Comparisons between the present algorithms and the existing ones are included, showing that our numerical algorithms exhibit better performances than the known ones. [ABSTRACT FROM AUTHOR]

We prove that the cycle-convergence of the restarted GMRES applied to a system of linear equations with a normal coefficient matrix is sublinear. [ABSTRACT FROM AUTHOR]

We study the convergence characteristics of two algebraic kernels used in vortex calculations: the Rosenhead-Moore kernel, which is a low-order kernel, and the Winckelmans-Leonard kernel, which is a high-order kernel. To facilitate the study, a method of evaluating particle-cluster interactions is introduced for the Winckelmans-Leonard kernel. The method is based on Taylor series expansion in Cartesian coordinates, as initially proposed by Lindsay and Krasny [J. Comput. Phys., 172 (2001), pp. 879-907] for the Rosenhead-Moore kernel. A recurrence relation for the Taylor coefficients of the Winckelmans-Leonard kernel is derived by separating the kernel into two parts, and an error estimate is obtained to ensure adaptive error control. The recurrence relation is incorporated into a tree-code to evaluate vorticity-induced velocity. Next, comparison of convergence is made while utilizing the tree-code. Both algebraic kernels lead to convergence, but the Winckelmans-Leonard kernel exhibits a superior convergence rate. The combined desingularization and discretization error from the Winckelmans-Leonard kernel is an order of magnitude smaller than that from the Rosenhead-Moore kernel at a typical resolution. Simulations of vortex rings are performed using the two algebraic kernels in order to compare their performance in a practical setting. In particular, numerical simulations of the side-by-side collision of two identical vortex rings suggest that the three-dimensional evolution of vorticity at finite resolution can be greatly affected by the choice of the kernel. We find that the Winckelmans-Leonard kernel is able to perform the same task with a much smaller number of vortex elements than the Rosenhead-Moore kernel, greatly reducing the overall computational cost. [ABSTRACT FROM AUTHOR]

A nine point scheme is presented for discretizing diffusion operators on distorted quadrilateral meshes. The advantage of this method is that highly distorted meshes can be used without the numerical results being altered remarkably, and it treats material discontinuities rigorously and offers an explicit expression for the face-centered flux; moreover, it has only the cell-centered unknowns. We prove that the method is stable and has first-order convergence on distorted meshes. Numerical experiments show that the method has second-order or nearly second-order accuracy on distorted meshes. [ABSTRACT FROM AUTHOR]

Construction of splitting-step methods and properties of related nonnegativity and boundary preserving semianalytic numerical algorithms for solving stochastic differential equations (SDEs) of Itô type are discussed. As the crucial assumption, we oppose conditions such that one can decompose the original system of SDEs into subsystems for which one knows either the exact solution or its conditional transition probability. We present convergence proofs for a newly designed splittingstep algorithm and simulation studies for numerous well-known numerical examples ranging from stochastic dynamics occurring in asset pricing in mathematical finance (Cox-Ingersoll-Ross (CIR) and constant elasticity of variance (CEV) models) to measure-valued diffusion and super-Brownian motion (stochastic PDEs (SPDEs)) as met in biology and physics. [ABSTRACT FROM AUTHOR]

We describe a reordering particularly suited to the PageRank problem, which reduces the computation of the PageRank vector to that of solving a much smaller system and then using forward substitution to get the full solution vector. We compare the theoretical rates of convergence of the original PageRank algorithm to that of the new reordered PageRank algorithm, showing that the new algorithm can do no worse than the original algorithm. We present results of an experimental comparison on five datasets, which demonstrate that the reordered PageRank algorithm can provide a speedup of as much as a factor of 6. We also note potential additional benefits that result from the proposed reordering. [ABSTRACT FROM AUTHOR]

An algorithm for general nonlinearly constrained optimization is presented, which solves an unconstrained piecewise quadratic subproblem and a quadratic programming subproblem at each iterate. The algorithm is robust since it can circumvent the difficulties associated with the possible inconsistency of QP subproblem of the original SQP method. Moreover, the algorithm can converge to a point which satisfies a certain first-order necessary optimality condition even when the original problem is itself infeasible, which is a feature of Burke and Han's methods [Math. Programming, 43 (1989), pp. 277–303]. Unlike Burke and Han's methods, our algorithm does not introduce additional bound constraints. The algorithm solves the same subproblems as the Han­ Powell SQP algorithm at feasible points of the original problem. Under certain assumptions, it is shown that the algorithm coincides with the Han­Powell method when the iterates are sufficiently close to the solution. Some global convergence results are proved and locally superlinear convergence results are also obtained. Preliminary numerical results are reported. [ABSTRACT FROM AUTHOR]

The application of nonparametric probability density function estimation for the purpose of data analysis is well established. More recently, such methods have been applied to fluid flow calculations since the density of the fluid plays a crucial role in determining the flow. Furthermore, when the calculations involve directional or axial data, the domain of interest falls on the surface of the sphere. Accurate and fast estimation of probability density functions is crucial for these calculations since the density estimation is performed at each iteration during the computation. In particular the values fn(X1),fn(X2),…,fn(Xn) of the density estimate at the sampled points Xi are needed to evolve the system. Usual nonparametric estimators make use of kernel functions to construct fn. We propose a special sequence of weight functions for nonparametric density estimation that is especially suitable for such applications. The resulting method has a computational advantage over kernel methods in certain situations and also parallelizes easily. Conditions for convergence turn out to be similar to those required for kernel-based methods. We also discuss experiments on different distributions and compare the computational efficiency of our method with kernel based estimators. [ABSTRACT FROM AUTHOR]