On the Degree of Approximation by Manifolds of Finite Pseudo-Dimension

The pseudo-dimension of a real-valued function class is an extension of the VC dimension for set-indicator function classes. A class of finite pseudo-dimension possesses a useful statistical smoothness property. In [10] we introduced a nonlinear approximation width = which measures the worst-case approximation error over all functions by the best manifold of pseudo-dimension n . In this paper we obtain tight upper and lower bounds on ρ _n (W ^r,d _p , L _q ) , both being a constant factor of n ^-r/d , for a Sobolev class W ^r,d _p , . As this is also the estimate of the classical Alexandrov nonlinear n -width, our result proves that approximation of W ^r,d _p by the family of manifolds of pseudo-dimension n is as powerful as approximation by the family of all nonlinear manifolds with continuous selection operators. March 12, 1997. Dates revised: August 26, 1997, October 24, 1997, March 16, 1998, June 15, 1998. Date accepted: June 25, 1998.     

Smooth Approximation of Orlicz-Sobolev Maps Between Manifolds

The problem of density of smooth functions in Orlicz-Sobolev spaces of maps between compact manifolds, without topological restrictions, is addressed. Our contribution refines earlier results in the literature via ad hoc Orlicz space techniques.     

A Gradient-Descent Method for Curve Fitting on Riemannian Manifolds

Given data points p ₀,…,p _N on a closed submanifold M of ℝ ⁿ and time instants 0=t ₀<t ₁<⋅⋅⋅<t _N =1, we consider the problem of finding a curve γ on M that best approximates the data points at the given instants while being as “regular” as possible. Specifically, γ is expressed as the curve that minimizes the weighted sum of a sum-of-squares term penalizing the lack of fitting to the data points and a regularity term defined, in the first case as the mean squared velocity of the curve, and in the second case as the mean squared acceleration of the curve. In both cases, the optimization task is carried out by means of a steepest-descent algorithm on a set of curves on M. The steepest-descent direction, defined in the sense of the first-order and second-order Palais metric, respectively, is shown to admit analytical expressions involving parallel transport and covariant integral along curves. Illustrations are given in ℝ ⁿ and on the unit sphere.     

Polyharmonic and Related Kernels on Manifolds: Interpolation and Approximation

This article is devoted to developing a theory for effective kernel interpolation and approximation in a general setting. For a wide class of compact, connected C ^∞ Riemannian manifolds, including the important cases of spheres and SO(3), and using techniques involving differential geometry and Lie groups, we establish that the kernels obtained as fundamental solutions of certain partial differential operators generate Lagrange functions that are uniformly bounded and decay away from their center at an algebraic rate, and in certain cases, an exponential rate. An immediate corollary is that the corresponding Lebesgue constants for interpolation as well as for L ₂ minimization are uniformly bounded with a constant whose only dependence on the set of data sites is reflected in the mesh ratio, which measures the uniformity of the data. The kernels considered here include the restricted surface splines on spheres, as well as surface splines for SO(3), both of which have elementary closed-form representations that are computationally implementable. In addition to obtaining bounded Lebesgue constants in this setting, we also establish a “zeros lemma” for domains on compact Riemannian manifolds—one that holds in as much generality as the corresponding Euclidean zeros lemma (on Lipschitz domains satisfying interior cone conditions) with constants that clearly demonstrate the influence of the geometry of the boundary (via cone parameters) as well as that of the Riemannian metric.     

On Approximation of Measures by Their Finite-Dimensional Images

Functional Analysis and Its Applications - We consider Borel measures on separable Banach spaces that are limits of their finite-dimensional images in the weak topology. The class of Banach spaces...     

平面Offset曲线的Bezier逼近算法

给出平面offset曲线的Bezier逼近算法,并进行误差分析.     

关于代数逼近测度的注记

本文给出了一类代数数序列的极限的代数逼近测度.     

开口曲线上的奇异积分近似求积公式

本文利用复插值样函数讨论了开口光滑曲线上的奇异积分在被积函数分别属于 H类和 H* 类时的近似求积公式 ,给出了误差估计和收敛性     

The Approximation of a Composite B?zier Cubic Curve by a Composite B?zier Quadratic Curve

In the context of the computer representation of textual characters,we describe an algorithm for approximating a composite B?ziercubic curve by a composite B?rier quadratic curve. The knotsor breakpoints of the approximating function are determinedsuch that the error of approximation does not exceed a specifiedvalue. The number of breakpoints, and hence the number of arcsof the approximation, is not in general optimal but can be expectedto be ‘small’. The work is clearly relevant alsoto other application areas.     

基于l1-l2范数的块稀疏信号重构

压缩感知(compressed sensing,CS) 是一种全新的信息采集与处理的理论框架,借助信号内在的稀疏性或可压缩性,可以从小规模的线性、非自适应的测量中通过求解非线性优化问题重构原信号.块稀疏信号是一种具有块结构的信号,即信号的非零元是成块出现的.受YIN Peng-hang, LOU Yi-fei, HE Qi等提出的l_1-2范数最小化方法的启发,将基于l₁-l₂范数的稀疏重构算法推广到块稀疏模型,证明了块稀疏模型下l₁-l₂范数的相关性质,建立了基于l₁-l₂范数的块稀疏信号精确重构的充分条件,并通过DCA(difference of convex functions algorithm) 和ADMM(alternating direction method of multipliers)给出了求解块稀疏模型下l₁-l₂范数的迭代方法.数值实验表明,基于l₁-l₂范数的块稀疏重构算法比其他块稀疏重构算法具有更高的重构成功率.     

The Set of Measures Given by Bounded Solutions of the Complex Monge-Ampere Equation on Compact Kahler Manifolds

Consider the image of the Monge–Ampère operatoracting on bounded functions, defined on a compact Kählermanifold, whose sum with the local Kähler potential isplurisubharmonic. It is shown that a nonnegative Borel measurebelongs to this image if and only if it belongs to the imagelocally. In particular, those measures form a convex set.     

Approximation of Invariant Measures for Regime-switching Diffusions

In this paper, we are concerned with long-time behavior of Euler-Maruyama schemes associated with regime-switching diffusion processes. The key contributions of this paper lie in that existence and uniqueness of numerical invariant measures are addressed (i) for regime-switching diffusion processes with finite state spaces by the Perron-Frobenius theorem if the “averaging condition” holds, and, for the case of reversible Markov chain, via the principal eigenvalue approach provided that the principal eigenvalue is positive; (ii) for regime-switching diffusion processes with countable state spaces by means of a finite partition method and an M-Matrix theory. We also reveal that numerical invariant measures converge in the Wasserstein metric to the underlying ones. Several examples are constructed to demonstrate our theory.     

On Measures Induced by Extremal Signatures in Best Real Polynomial Approximation

We consider the limit distribution of measures μ _n , that appear in extremal signatures in the best polynomial approximation of a real-valued function . Relations between structural properties of the function f and weak-star limit points of (μ _n ) _n are proved. April 4, 1996. Date revised: October 25, 1996.     

Minimization Algorithms Based on Supervisor and Searcher Cooperation

In the present work, we explore a general framework for the design of new minimization algorithms with desirable characteristics, namely, supervisor-searcher cooperation. We propose a class of algorithms within this framework and examine a gradient algorithm in the class. Global convergence is established for the deterministic case in the absence of noise and the convergence rate is studied. Both theoretical analysis and numerical tests show that the algorithm is efficient for the deterministic case. Furthermore, the fact that there is no line search procedure incorporated in the algorithm seems to strengthen its robustness so that it tackles effectively test problems with stronger stochastic noises. The numerical results for both deterministic and stochastic test problems illustrate the appealing attributes of the algorithm.     

Super-Resolution Meets Machine Learning: Approximation of Measures

Journal of Fourier Analysis and Applications - The problem of super-resolution in general terms is to recuperate a finitely supported measure $$mu $$ given finitely many of its coefficients...     

Blind Channel Identification Based on Noisy Observation by Stochastic Approximation Method

A stochastic approximation algorithm for estimating multichannel coefficients is proposed, and the estimate is proved to converge to the true parameters a.s. up-to a constant scaling factor. The estimate is updated after receiving each new observation, so the output data need not be collected in advance. The input signal is allowed to be dependent and the observation is allowed to be corrupted by noise, but no noise statistics are used in the estimation algorithm.     

基于整体风险最小的多阶段期货套期保值研究

对多阶段套期保值建立模型,综合考虑整体风险,以最终现货与期货的收益的方差建立目标函数.以多阶段整体风险最小为目标函数,考虑资金限制,建立套期保值模型来解决多阶段套期保值的套期保值比率问题.以资金限制为约束,避免了套期保值者因资金短缺而强制平仓造成的套保失败.利用差分算法和罚函数法进行求解.实证结果表明,多阶段的风险比逐个单阶段求得的风险明显的小,且整体套保的单位风险收益比单阶段的大很多,说明多阶段比单阶段能较好的实现套期保值.