Convergence results for a self-dual regularization of convex problems

We study a one-parameter regularization technique for convex optimization problems whose main feature is self-duality with respect to the Legendre–Fenchel conjugation. The self-dual technique, introduced by Goebel, can be defined for both convex and saddle functions. When applied to the latter, we show that if a saddle function has at least one saddle point, then the sequence of saddle points of the regularized saddle functions converges to the saddle point of minimal norm of the original one. For convex problems with inequality and state constraints, we apply the regularization directly on the objective and constraint functions, and show that, under suitable conditions, the associated Lagrangians of the regularized problem hypo/epi-converge to the original Lagrangian, and that the associated value functions also epi-converge to the original one. Finally, we find explicit conditions ensuring that the regularized sequence satisfies Slater's condition.     

Abelian monopoles and complex geometry

Three remarks concerning the constructive properties of smooth 4-manifolds with nontrivial Seiberg-Witten invariants are presented. Translated fromMatematicheskie Zametki, Vol. 65, No. 3, pp. 420–428, March, 1999.     

Gromov–Witten Invariants of Blow-ups Along Surfaces

In this paper, using the gluing formula of Gromov–Witten invariants for symplectic cutting developed by Li and Ruan, we established some relations between Gromov–Witten invariants of a semipositive symplectic manifold M and its blow-ups along a smooth surface.     

Extended Gauss–Markov Theorem for Nonparametric Mixed-Effects Models

The Gauss–Markov theorem provides a golden standard for constructing the best linear unbiased estimation for linear models. The main purpose of this article is to extend the Gauss–Markov theorem to include nonparametric mixed-effects models. The extended Gauss–Markov estimation (or prediction) is shown to be equivalent to a regularization method and its minimaxity is addressed. The resulting Gauss–Markov estimation serves as an oracle to guide the exploration for effective nonlinear estimators adaptively. Various examples are discussed. Particularly, the wavelet nonparametric regression example and its connection with a Sobolev regularization is presented.     

A HIERARCHY OF INTEGRABLE LATTICE SOLITON EQUMIONS AND ITS INTEGRABLE SYMPLECTIC MAP

1TheLatticeSolitonHierarchyDenoteEf(n)-f(n 1),nEZ,andwritef(n)=f,f(n k)=E*f.Considerthediscrete3x3matrixspectralproblem:whereajb,carethreepotentials,Aisaconstantspectralparameter.Theadjointrepresentationequationof(1)iswhereeachentryVj=Kj(B(A),C(A),D(A))0fthe3x3matrixVisaLaurentexpansionofA:Then(2)isequivaentt0'therecursi0nrelations:,Theaboverecursionequationscanbesolvedsuccessivelyt0deducethefollowingresults:Wedefine{Dj}bythefollowingrelation:From(3)we0btainKGj-1=JGj,Gj=(Aj,Bj,Cj…     

In response to the article by Maxim Kontsevich: Rozansky–Witten Invariants Via Formal Geometry

We show that recently constructed invariants of 3-dimensional manifolds and of hyperkähler manifolds (L. Rozansky and E. witten) come from characteristic classes of foliations and from Gelfand-fuks cohomology.     

Symplectic Self-adjointness of Infinite Dimensional Hamiltonian Operators

Symplectic self-adjointness of infinite dimensional Hamiltonian operators is studied, the necessary and sufficient conditions are given. Using the relatively bounded perturbation, the sufficient conditions about symplectic self-adjointness are shown.     

Regularization inertial proximal point algorithm for common solutions of a finite family of inverse-strongly monotone equations

The aim of the paper is to propose an iterative regularization method of proximal point type for finding a common solution for a finite family of inverse-strongly monotone equations in Hilbert spaces.     

用于前向光散射颗粒粒度测量的基函数Tikhonov正则化算法

以前向光散射颗粒测试技术中的反演问题为研究对象,将颗粒粒径分布函数表示为一系列B-spline函数的线性组合以降低反演问题的病态性。模拟计算与实验表明:采用基函数形式的Tikhonov正则化方法可减少传统Tikhonov正则化反演带来的振荡,使其更加光顺。