Polyhedral approximation of convex compact bodies by filling methods

A class of iterative methods??filling methods??for polyhedral approximation of convex compact bodies is introduced and studied. In contrast to augmentation methods, the vertices of the approximating polytope can lie not only on the boundary of the body but also inside it. Within the proposed class, Hausdorff or H-methods of filling are singled out, for which the convergence rates (asymptotic and at the initial stage of the approximation) are estimated. For the approximation of nonsmooth convex compact bodies, the resulting convergence rate estimates coincide with those for augmentation H-methods.     

Efficiency of the estimate refinement method for polyhedral approximation of multidimensional balls

The estimate refinement method for the polyhedral approximation of convex compact bodies is analyzed. When applied to convex bodies with a smooth boundary, this method is known to generate polytopes with an optimal order of growth of the number of vertices and facets depending on the approximation error. In previous studies, for the approximation of a multidimensional ball, the convergence rates of the method were estimated in terms of the number of faces of all dimensions and the cardinality of the facial structure (the norm of the f-vector) of the constructed polytope was shown to have an optimal rate of growth. In this paper, the asymptotic convergence rate of the method with respect to faces of all dimensions is compared with the convergence rate of best approximation polytopes. Explicit expressions are obtained for the asymptotic efficiency, including the case of low dimensions. Theoretical estimates are compared with numerical results.     

Hausdorff methods for approximating the convex Edgeworth–Pareto hull in integer problems with monotone objectives

Adaptive methods for the polyhedral approximation of the convex Edgeworth–Pareto hull in multiobjective monotone integer optimization problems are proposed and studied. For these methods, theoretical convergence rate estimates with respect to the number of vertices are obtained. The estimates coincide in order with those for filling and augmentation H-methods intended for the approximation of nonsmooth convex compact bodies.     

Optimal growth order of the number of vertices and facets in the class of Hausdorff methods for polyhedral approximation of convex bodies

The internal polyhedral approximation of convex compact bodies with twice continuously differentiable boundaries and positive principal curvatures is considered. The growth of the number of facets in the class of Hausdorff adaptive methods of internal polyhedral approximation that are asymptotically optimal in the growth order of the number of vertices in approximating polytopes is studied. It is shown that the growth order of the number of facets is optimal together with the order growth of the number of vertices. Explicit expressions for the constants in the corresponding bounds are obtained.     

Discrete Approximations of Controlled Stochastic Systems with Memory: A Survey

This survey article considers discrete approximations of an optimal control problem in which the controlled state equation is described by a general class of stochastic functional differential equations with a bounded memory. Specifically, three different approximation methods, namely (i) semidiscretization scheme; (ii) Markov chain approximation; and (iii) finite difference approximation, are investigated. The convergence results as well as error estimates are established for each of the approximation methods.     

The modified method of refined bounds for polyhedral approximation of convex polytopes

The modified method of refined bounds is proposed and experimentally studied. This method is designed to iteratively approximate convex multidimensional polytopes with a large number of vertices. Approximation is realized by a sequence of convex polytopes with a relatively small but gradually increasing number of vertices. The results of an experimental comparison between the modified and the original methods of refined bounds are presented. The latter was designed for the polyhedral approximation of multidimensional convex compact bodies of general type.     

Nonadaptive methods for polyhedral approximation of the Edgeworth—Pareto hull using suboptimal coverings on the direction sphere

For multicriteria convex optimization problems, new nonadaptive methods are proposed for polyhedral approximation of the multidimensional Edgeworth-Pareto hull (EPH), which is a maximal set having the same Pareto frontier as the set of feasible criteria vectors. The methods are based on evaluating the support function of the EPH for a collection of directions generated by a suboptimal covering on the unit sphere. Such directions are constructed in advance by applying an asymptotically effective adaptive method for the polyhedral approximation of convex compact bodies, namely, by the estimate refinement method. Due to the a priori definition of the directions, the proposed EPH approximation procedure can easily be implemented with parallel computations. Moreover, the use of nonadaptive methods considerably simplifies the organization of EPH approximation on the Internet. Experiments with an applied problem (from 3 to 5 criteria) showed that the methods are fairly similar in characteristics to adaptive methods. Therefore, they can be used in parallel computations and on the Internet.     

Existence and convergence of Galerkin approximation for second order hyperbolic equations with memory term

We study a second order hyperbolic initial‐boundary value partial differential equation (PDE) with memory that results in an integro‐differential equation with a convolution kernel. The kernel is assumed to be either smooth or no worse than weakly singular, that arise for example, in linear and fractional order viscoelasticity. Existence and uniqueness of the spatial local and global Galerkin approximation of the problem is proved by means of Picard's iteration. Then, spatial finite element approximation of the problem is formulated, and optimal order a priori estimates are proved by the energy method. The required regularity of the solution, for the optimal order of convergence, is the same as minimum regularity of the solution for second order hyperbolic PDEs. Spatial rate of convergence of the finite element approximation is illustrated by a numerical example. © 2015 Wiley Periodicals, Inc. Numer Methods Partial Differential Eq 32: 548–563, 2016     

Greedy-Type Approximation in Banach Spaces and Applications

We continue to study the efficiency of approximation and convergence of greedy-type algorithms in uniformly smooth Banach spaces. Two greedy-type approximation methods, the Weak Chebyshev Greedy Algorithm (WCGA) and the Weak Relaxed Greedy Algorithm (WRGA), have been introduced and studied in [24]. These methods (WCGA and WRGA) are very general approximation methods that work well in an arbitrary uniformly smooth Banach space $X$ for any dictionary ${\Cal D}$. It turns out that these general approximation methods are also very good for specific dictionaries. It has been observed in [7] that the WCGA and WRGA provide constructive methods in $m$-term trigonometric approximation in $L_p$, $p\in[2,\infty)$, which realize an optimal rate of $m$-term approximation for different function classes. In [25] the WCGA and WRGA have been used in constructing deterministic cubature formulas for a wide variety of function classes with error estimates similar to those for the Monte Carlo Method. The WCGA and WRGA can be considered as a constructive deterministic alternative to (or substitute for) some powerful probabilistic methods. This observation encourages us to continue a thorough study of the WCGA and WRGA. In this paper we study modifications of the WCGA and WRGA that are motivated by numerical applications. In these modifications we are able to perform steps of the WCGA (or WRGA) approximately with some controlled errors. We prove that the modified versions of the {\it WCGA and WRGA perform as well as the WCGA and WRGA}. We give two applications of greedy-type algorithms. First, we use them to provide a constructive proof of optimal estimates for best $m$-term trigonometric approximation in the uniform norm. Second, we use them to construct deterministic sets of points $\{\xi^1,\ldots,\xi^m\} \subset [0,1]^d$ with the $L_p$ discrepancy less than $Cp^{1/2}m^{-1/2}$, $C$ is an effective absolute constant.     

Minimization methods for approximating tensors and their comparison

Application of various minimization methods to trilinear approximation of tensors is considered. These methods are compared based on numerical calculations. For the Gauss-Newton method, an efficient implementation is proposed, and the local rate of convergence is estimated for the case of completely symmetric tensors.     

Approximating the convex Edgeworth-Pareto hull in integer multi-objective problems with monotone criteria

A method for the iterative polyhedral approximation of the convex Edgeworth-Pareto hull is proposed and examined experimentally. This method is designed for integer multi-objective problems with monotone objective functions and constraints given by a computational module. It is based on a synthesis of the ideas of the branch-and-bound method and the methods for the polyhedral approximation of convex bodies. A sequence of interior and exterior polyhedral sets is constructed so as to approximate the Edgeworth-Pareto hull to the desired accuracy. The results of the theoretical and experimental analyses of the proposed method are presented.     

Comparing Nonsmooth Nonconvex Bundle Methods in Solving Hemivariational Inequalities

Hemivariational inequalities can be considered as a generalization of variational inequalities. Their origin is in nonsmooth mechanics of solid, especially in nonmonotone contact problems. The solution of a hemivariational inequality proves to be a substationary point of some functional, and thus can be found by the nonsmooth and nonconvex optimization methods. We consider two type of bundle methods in order to solve hemivariational inequalities numerically: proximal bundle and bundle-Newton methods. Proximal bundle method is based on first order polyhedral approximation of the locally Lipschitz continuous objective function. To obtain better convergence rate bundle-Newton method contains also some second order information of the objective function in the form of approximate Hessian. Since the optimization problem arising in the hemivariational inequalities has a dominated quadratic part the second order method should be a good choice. The main question in the functioning of the methods is how remarkable is the advantage of the possible better convergence rate of bundle-Newton method when compared to the increased calculation demand.     

Spectral approximation of variationally-posed eigenvalue problems by nonconforming methods

This paper deals with the nonconforming spectral approximation of variationally posed eigenvalue problems. It is an extension to more general situations of known previous results about nonconforming methods. As an application of the present theory, convergence and optimal order error estimates are proved for the lowest order Crouzeix–Raviart approximation of the eigenpairs of two representative second-order elliptical operators.     

A bundle-Newton method for nonsmooth unconstrained minimization

An algorithm based on a combination of the polyhedral and quadratic approximation is given for finding stationary points for unconstrained minimization problems with locally Lips-chitz problem functions that are not necessarily convex or differentiable. Global convergence of the algorithm is established. Under additional assumptions, it is shown that the algorithm generates Newton iterations and that the convergence is superlinear. Some encouraging numerical experience is reported. This work was supported by the grant No. 201/96/0918 given by the Czech Republic Grant Agency.     

Approximation Spaces in the Numerical Analysis of Operator Equations

We show that generalized approximation spaces can be used to prove stability and convergence of projection methods for certain types of operator equations in which unbounded operators occur. Besides the convergence, we also get orders of convergence by this approach, even in case of non-uniformly bounded projections. We give an example in which weighted uniform convergence of the collocation method for an easy Cauchy singular integral equation is studied.     

Some variations of the Bohman–Korovkin theorem

In this paper we develop the main aspects of the Bohman–Korovkin theorem on approximation of continuous functions with the use of A-statistical convergence and matrix summability method which includes both convergence and almost convergence. Since statistical convergence and almost convergence methods are incompatible we conclude that these methods can be used alternatively to get some approximation results.     

Discrete,nonlinear approximation problems in polyhedral norms

Summary A Levenberg-like algorithm is given for the solution of discrete nonlinear approximation problems in polyhedral norms. Convergence and rate of convergence results are discussed and some numerical results presented.     

Stable enrichment and local preconditioning in the particle-partition of unity method

This paper is concerned with the stability and approximation properties of enriched meshfree and generalized finite element methods. In particular we focus on the particle-partition of unity method (PPUM) yet the presented results hold for any partition of unity based enrichment scheme. The goal of our enrichment scheme is to recover the optimal convergence rate of the uniform h-version independent of the regularity of the solution. Hence, we employ enrichment not only for modeling purposes but rather to improve the approximation properties of the numerical scheme. To this end we enrich our PPUM function space in an enrichment zone hierarchically near the singularities of the solution. This initial enrichment however can lead to a severe ill-conditioning and can compromise the stability of the discretization. To overcome the ill-conditioning of the enriched shape functions we present an appropriate local preconditioner which yields a stable and well-conditioned basis independent of the employed initial enrichment. The construction of this preconditioner is of linear complexity with respect to the number of discretization points. We obtain optimal error bounds for an enriched PPUM discretization with local preconditioning that are independent of the regularity of the solution globally and within the employed enrichment zone we observe a kind of super-convergence. The results of our numerical experiments clearly show that our enriched PPUM with local preconditioning recovers the optimal convergence rate of O(h ^p ) of the uniform h-version globally. For the considered model problems from linear elastic fracture mechanics we obtain an improved convergence rate of O(h ^p+δ ) with d 3 \frac12{\delta\geq\frac{1}{2}} for p = 1. The convergence rate of our multilevel solver is essentially the same for a purely polynomial approximation and an enriched approximation.     

Convergence rate for predictive recursion estimation of finite mixtures

Predictive recursion (PR) is a fast stochastic algorithm for nonparametric estimation of mixing distributions in mixture models. It is known that the PR estimates of both the mixing and mixture densities are consistent under fairly mild conditions, but currently very little is known about the rate of convergence. Here I first investigate asymptotic convergence properties of the PR estimate under model misspecification in the special case of finite mixtures with known support. Tools from stochastic approximation theory are used to prove that the PR estimates converge, to the best Kullback-Leibler approximation, at a nearly root-n rate. When the support is unknown, PR can be used to construct an objective function which, when optimized, yields an estimate of the support. I apply the known-support results to derive a rate of convergence for this modified PR estimate in the unknown support case, which compares favorably to known optimal rates.