Iterated hard-thresholding for linear inverse problems with sparsity constraints

We describe an iterative algorithm for the minimization of Tikhonov type functionals which involve sparsity constraints in form of ℓ^p -penalties which have been proposed recently for the regularization of ill-posed problems. In contrast to the well-known algorithm considered by Daubechies, Defrise and De Mol, it uses hard instead of soft thresholding. This hard thresholding algorithm is based on the generalized conditional gradient method. General results on the convergence of the generalized conditional gradient method enable us to prove strong convergence of the iterates. Furthermore we are able to establish convergence rates of O (n^–1/2) and O (λⁿ) for p = 1 and 1 < p ≤ 2 respectively. (© 2008 WILEY-VCH Verlag GmbH & Co. KGaA, Weinheim)     

Convergence and Stability of the Calder��n Reproducing Formula in H 1 and BMO

We prove that a general form of the Calderón reproducing formula converges in H ¹(R ^d ) (the real Hardy space of Fefferman and Stein) as a natural limit of approximating integrals. We show that this convergence is H ¹-stable with respect to small errors in dilation and translation. Using duality, we show that the Calderón reproducing formula converges, in a stable fashion, weak-∗ in BMO. We give quantitative estimates of the formula’s stability and rate of convergence. These theorems generalize results of the author on the convergence and stability of the Calderón reproducing formula in L ^p (w), where 1<p<∞ and w is a Muckenhoupt A _p weight.     

L p -Version of the Dubins–Savage Inequality and Some Exponential Inequalities

The Dubins–Savage inequality is generalized by using the pth (1<p≤2) conditional moment of the martingale differences. This inequality is further extended under suitable conditions when p>2. Another martingale inequality due to Freedman is also generalized when 0<p≤2. Implications of these inequalities for strong convergence are discussed. Some general exponential inequalities are also given for martingales (supermartingales) under suitable conditions.      

New models for locating a moving service facility

In this paper we analyze a new location problem which is a generalization of the well-known single facility location model. This extension consists of introducing a general objective function and replacing fixed locations by trajectories. We prove that the problem is well-stated and solvable. A Weiszfeld type algorithm is proposed to solve this generalized dynamic single facility location problem on L ^p spaces of functions, with p ∈(1,2]. We prove global convergence of our algorithm once we have assumed that the set of demand functions and the initial step function belong to a subspace of L ^p called Sobolev space. Finally, examples are included illustrating the application of the model to generalized regression analysis and the convergence of the proposed algorithm. The examples also show that the pointwise extension of the algorithm does not have to converge to an optimal solution of the considered problem while the proposed algorithm does.     

BMO estimates for lacunary series

We proveBMO andL ^p norm inequalities inR ⁿ for lacunary Walsh and generalized trigonometric series.     

Mean Convergence of Lagrnage Type Interpolation on an Arbitrary System of Nodes

Abstract   Sufficient conditions of convergence and rate of convergence for Lagrange type interpolation in the weighted L ^p norm on an arbitrary system of nodes are given. Supported by the National Natural Sciences Foundation of China (No.19671082)     

Cosine families generated by second-order differential operators on W1,1(0, 1) with generalized Wentzell boundary conditions

Using the abstract framework [Bátkai, A. and Engel, K.-J., 2004, Abstract wave equations with generalized Wentzell boundary conditions. Journal of Differential Equations, 207, 1–20.] we show that certain second-order differential operators with generalized Wentzell boundary conditions generate cosine families and hence also analytic semigroups on W^1,1(0,1). This complements the main result [Favini, A., Ruiz Goldstein, G., Goldstein, J.A., Obrecht, E. and Romanelli, S., 2003, General Wentzell boundary conditions and analytic semigroups on W^{1, p} (0,1). Applicable Analysis, 82, 927–935.] on the generation of an analytic semigroup by the second derivative with generalized Wentzell boundary conditions on W^{1, p} (0,?1) for 1<p<∞.     

Local convergence in a generalized Fermat-Weber problem

In the Fermat-Weber problem, the location of a source point in ^N is sought which minimizes the sum of weighted Euclidean distances to a set of destinations. A classical iterative algorithm known as the Weiszfeld procedure is used to find the optimal location. Kuhn proves global convergence except for a denumerable set of starting points, while Katz provides local convergence results for this algorithm. In this paper, we consider a generalized version of the Fermat-Weber problem, where distances are measured by anl _p norm and the parameterp takes on a value in the closed interval [1, 2]. This permits the choice of a continuum of distance measures from rectangular (p=1) to Euclidean (p=2). An extended version of the Weiszfeld procedure is presented and local convergence results obtained for the generalized problem. Linear asymptotic convergence rates are typically observed. However, in special cases where the optimal solution occurs at a singular point of the iteration functions, this rate can vary from sublinear to quadratic. It is also shown that for sufficiently large values ofp exceeding 2, convergence of the Weiszfeld algorithm will not occur in general.     

Generalized Padé-type approximants to continuous functions

Summary We define generalized Padé-type approximants to continuous functions on a compact subset Eof Rⁿsatisfying the Markov's inequality and we show that the Fourier series expansion of a generalized Padé-type approximant to a u ∈ C ^∞(E ) matches the Fourier series expansion of uas far as possible. After studying the errors, we give integral representations and an answer to the convergence problem of a generalized Padé-type approximation sequence.     

On the convergence of double Fourier series of functions in L P[0, 1]2, where p=(p 1, p 2)

Pointwise convergence of double trigonometric Fourier series of functions in the Lebesgue space L _p[0,2]²was studied by M. I. Dyachenko. In this paper, we also consider the problems of the convergence of double Fourier series in Pringsheim"s sense with respect to the trigonometric as well as the Walsh systems of functions in the Lebesgue space L _p[0,1]², p=(p₁,p₂), endowed with a mixed norm, in the particular case when the coefficients of the series in question are monotone with respect to each of the indices. We shall obtain theorems which generalize those of M. I. Dyachenko to the case when p is a vector. We shall also show that our theorems in the case of trigonometric Fourier series are best possible.     

Statistical Convergence of Order α for Difference Sequences

Abstract

In this paper the concepts of ?^m_v-statistical convergence of order α and strong (p, ?^m)-Ces`aro summability of order α are introduced for sequences of complex (or real) numbers. Some relations between the ?<sup>m</sup><sub>v</sub>-statistical convergence of order α and strong (p, ?<sup>m</sup><sub>v</sub>)-Ces`aro summability of order α are given. Also some relations between the space ω^α_p (?^m_v, f) and S^α (?^m_v) are examined.     

Cutting Plane Algorithms for Nonlinear Semi-Definite Programming Problems with Applications

We will propose an outer-approximation (cutting plane) method for minimizing a function f X subject to semi-definite constraints on the variables XR ⁿ. A number of efficient algorithms have been proposed when the objective function is linear. However, there are very few practical algorithms when the objective function is nonlinear. An algorithm to be proposed here is a kind of outer-approximation(cutting plane) method, which has been successfully applied to several low rank global optimization problems including generalized convex multiplicative programming problems and generalized linear fractional programming problems, etc. We will show that this algorithm works well when f is convex and n is relatively small. Also, we will provide the proof of its convergence under various technical assumptions.     

Convergence of Some Greedy Algorithms in Banach Spaces

We consider some theoretical greedy algorithms for approximation in Banach spaces with respect to a general dictionary. We prove convergence of the algorithms for Banach spaces which satisfy certain smoothness assumptions. We compare the algorithms and their rates of convergence when the Banach space is L_p(\mathbbT^d)L_p(\mathbb{T}^d) ($1相似文献   

Nonlinear approximation by renormalized trigonometric system

We study the convergence of greedy algorithmwith regard to renormalized trigonometric system. Necessary and sufficient conditions are found for system’s normalization to guarantee almost everywhere convergence, and convergence in L ^p (T) for 1 < p < ∞ of the greedy algorithm, where T is the unit torus. Also the non existence is proved for normalization which guarantees convergence almost everywhere for functions from L ¹(T), or uniform convergence for continuous functions.     

<Emphasis Type="Italic">L</Emphasis><Superscript><Emphasis Type="Italic">r</Emphasis></Superscript> convergence for <Emphasis Type="Italic">B</Emphasis>-valued random elements

The paper investigates L ^p convergence and Marcinkiewicz-Zygmund strong laws of large numbers for random elements in a Banach space under the condition that the Banach space is of Rademacher type p, 1 < p < 2. The paper also discusses L ^r convergence and L ^r bound for random elements without any geometric restriction condition on the Banach space.     

On statistical convergence of difference sequences of fractional order and related Korovkin type approximation theorems

Abstract

Prior to investigating on sequence spaces and their convergence, we study the notion of statistical convergence of difference sequences of fractional order α ∈ ?. As generalizations of previous works, this study includes several special cases under different limiting conditions of α, such as the notion of statistical convergence of difference sequences of zeroth and mth (integer) order. In fact, we study certain new results on statistical convergence via the difference operator Δ^α and interpret them to those of previous works. Also, by using the convergence of Δ^α-summable sequences which is stronger than statistical convergence of difference sequences, we apply classical Bernstein operator and a generalized form of Meyer-Konig and Zeller operator to construct an example in support of our result. Also, we study the rates of Δ^α-statistical convergence of positive linear operators.     

Lagrange interpolation on extended generalized Jacobi nodes

We consider theL ^p -convergence of interpolatory processes for nonsmooth functions. Therefore we use generalizations of the well-known Marcinkiewicz-Zygmund inequality for trigonometric polynomials to the case of algebraic polynomials, extending a result of Y. Xu. Particularly, we obtain the order of convergence for certain Lagrange and quasi-Lagrange interpolatory processes on generalized Jacobi nodes. Our approach enables us also to discuss the influence of additional nodes near the endpoints ±1.     

On the convergence of some Procrustean averaging algorithms

Euclidean “(size-and-)shape spaces” are spaces of configurations of points in R ^N modulo certain equivalences. In many applications one seeks to average a sample of shapes, or sizes-and-shapes, thought of as points in one of these spaces. This averaging is often done using algorithms based on generalized Procrustes analysis (GPA). These algorithms have been observed in practice to converge rapidly to the Procrustean mean (size-and-)shape, but proofs of convergence have been lacking. We use a general Riemannian averaging (RA) algorithm developed in [Groisser, D. (2004) “Newton's method, zeroes of vector fields, and the Riemannian center of mass”, Adv. Appl. Math. 33, pp. 95–135] to prove convergence of the GPA algorithms for a fairly large open set of initial conditions, and estimate the convergence rate. On size-and-shape spaces the Procrustean mean coincides with the Riemannian average, but not on shape spaces; in the latter context we compare the GPA and RA algorithms and bound the distance between the averages to which they converge.     

Mean Convergence of Derivatives of Extended Lagrange Interpolation with Additional Nodes

Weighted L^p convergence of derivatives of extended Lagrange interpolation at the union of zeros of generalized Jacobi polynomials and some additional points is investigated.     

A probabilistic interpretation and stochastic particle approximations of the 3-dimensional Navier-Stokes equations

We develop a probabilistic interpretation of local mild solutions of the three dimensional Navier-Stokes equation in the L^p spaces, when the initial vorticity field is integrable. This is done by associating a generalized nonlinear diffusion of the McKean-Vlasov type with the solution of the corresponding vortex equation. We then construct trajectorial (chaotic) stochastic particle approximations of this nonlinear process. These results provide the first complete proof of convergence of a stochastic vortex method for the Navier-Stokes equation in three dimensions, and rectify the algorithm conjectured by Esposito and Pulvirenti in 1989. Our techniques rely on a fine regularity study of the vortex equation in the supercritical L^p spaces, and on an extension of the classic McKean-Vlasov model, which incorporates the derivative of the stochastic flow of the nonlinear process to explain the vortex stretching phenomenon proper to dimension three. Supported by Fondecyt Project 1040689 and Nucleus Millennium Information and Randomness ICM P01-005.