对合数组与对合不动点集分支的维数(Ⅰ)

设M~n为n维光滑闭流形。给定光滑非自由对合(Mⁿ,τ),本文定义了一个数组I(τ),称为联系于(Mⁿ,τ)的对合数组。我们证明了,I(τ)=(k₀,k₁,…,k_r),0≤r≤n,0≤k_0...     

线性模型中误差方差的二次型估计的可容许性问题

设有线性模型Y=(y₁…y_n)’=Xβ+ε=X(β₁…β_p)’+(ε₁…ε_n)’,这里n≥p,X已知,ε₁,…,ε_n相互独立,E(ε_i)=0,E(ε_i²)=σ²,E(ε_i³)=0,E(ε_i⁴)=3σ⁴,i=1,…,n,β∈R^p,0<σ~2<∞。令?={Y’AY:A≥0}。当损失函数为σ^-4(d-σ²)²且X=I_n或者X=1_n时,给出了 Y’AY(A≥0)在?中是σ₂的可容许估计的充分必要条件。又当ε～N(0,σ²I_n)时,给出了Y’AY(A≥0)在σ²的一切估计类中是可容许的充分条件。     

关于Hayman方向

本文获得如下定理:设f(z)为开平面上λ(0<λ<+∞)级亚纯函数,则至少存在一条由原点引出的半直线(B):arg z=θ_?(0≤θ₀<2π),使得对于任意正数ε,任意正整数k及任意两个开平面上级小于λ的亚纯函数a(z)及b(z),只要b(z)—a^(k)(z)?0就有 lim log{n(r,θ₀,ε,f=a(z))+n(r,θ₀,ε,f^(k)=b(z))}/logr=λ。     

基于一类新方向的宽邻域路径跟踪内点算法

基于一类带有参数theta的新方向, 提出了求解单调线性互补问题的宽邻 域路径跟踪内点算法, 且当theta=1时即为经典牛顿方向. 当取theta为与问题规模 n无关的常数时, 算法具有O(nL)迭代复杂性, 其中L是输入数据的长度, 这与经典宽邻 域算法的复杂性相同; 当取theta=\sqrt{n/\beta\tau}时, 算法具有O(\sqrt{n}L)迭代复杂性, 这里的\beta, \tau是邻域参数, 这与窄邻域算法的复杂性相同. 这是首次研究包括经典宽邻域路径跟踪算法的一类内点算法, 给出了统一的算法框架和收敛性分析方法.     

随机删失半参数回归模型中估计的渐近性质

设Y是表示生存时间并遵从下面半参数模型Y=Xβ+g(T)+ε的随机变量,(X,T)是取值于R×[0,1]上的随机变量,β是未知参数,g(·)是[0,1]上的未知回归函数,ε是随机误差。当Y因受某种随机干扰而被随机右删失时,就删失分布未知的情形分别定义了β与g(·)的估计^{^}β_n与^{^}g_n(·),在一定条件下证明了^{^}β_n的渐近正态性,并得到了^{^}g_n(·)的最优收敛速度。     

有限变系数系统的运动稳定性

以E(p,q;ε)记满足条件的二阶变系数系统的全体所组成的集合。此处p>0,q>0,ε≥0。本文证明了:(甲) 对于任何两个正常数p及q,存在一个正常数ε^*=ε^*(q/p²),使得(ⅰ) 当0≤ε<ε^*,则集合E(p,q;ε)中的每一个系统的平凡解都是渐近稳定的;(ⅱ) 当ε^*<ε,则集合E(p,q;ε)中有系统共平凡解是不稳定的。这就否定了一种普遍的猜想:条件p₁≥p(t)≥p₀>O,q₁≥q(t)≥q₀>0。可以保证系统的平凡解的稳定性;(ⅲ) 当ε^*=ε,则集合E(p,q;ε)中每一系统的平凡解都是稳定的,但存在系统,其平凡解不是渐近稳定的。(乙) 函数ε^*(q/p²)随q/p~2由0增加到+∞,而由1单调减少到0。(丙) 给出了函数ε^*(q/p²)的数值图表,以及近似解析表达式,供工程师及物理、力学家之用。注意,p₁实际上可任意大,ε^*只与p₀,q₀,q₁有关,相应的结果亦已得到。     

关于小区间上的Goldbach数

设B为充分大的正常数,ε为充分小的正常数,N充分大。主要证明了:1)如A=N^7/(78+ε),则(N,N+A)中的偶数,除去O(Alog^-BN)个例外值,均为Goldbach数;2)(N,N+N^23/546+ε)中包含至少一个Goldbach数。     

小区间内的Goldbach数

设B为充分大的正常数,ε为充分小的正常数,N是充分大的正整数,A=N^7/81+ε,证明了:区间(N,N+A)中的偶数,除去O(Alog^-BN)个例外值,均为Goldbach数.     

Gauss-Markov条件下最小二乘估计的强相合性

设Y_i=x＇_iβ+e_i,1≤i≤n为线性模型,β_n=(β_n1,…,β_np)＇为β=(β₁,…,β_p)＇的最小二乘估计,以u_n记(sum from i=1 to n(x_ix＇_i))的(1,1)元,v_n=u_n^-1.证明了在Ee_i=O且{e_i}满足Gauss-Markov条件时,v_i→∞及sum from i=2 to ∞(v_i^-2(v_i-v_i-1)log~2i<∞)为β_n1强相合的充分条件,且对任何ε_n→0,v_i→∞及sum from i=2 to ∞(ε_iv_i^-2(v_i-v_i-1)log²i<∞)已不再充分.提出了β_n1强相合的一个充要条件,它把β_n1强相合归结为正交随机变量级数的收敛问题.     

随机发展方程小扰动大偏差原理的统一方法

设X^ε=|X^ε(t);0≤t≤1|(ε>0)是由随机发展方程 dX^ε(t)=^ε(1/2)σ(X^ε(t))dB(t)+b(X^ε(t),ν(t))dt控制的随机过程,其中ν(t)是与Brown运动B(·)独立的随机过程。讨论了|(X^ε,ν(·));ε>0|的大偏差性质;在特殊情形下,给出了精确的速率函数,解决了Eizenberg和Freidlin所提的一个问题。此外,还得到一个一般性大偏差定理。     

Polynomial complexity of an interior point algorithm with a second order corrector step for symmetric cone programming

In this paper, we propose a second order interior point algorithm for symmetric cone programming using a wide neighborhood of the central path. The convergence is shown for commutative class of search directions. The complexity bound is O(r^3/2 loge^-1){O(r^{3/2}\,\log\epsilon^{-1})} for the NT methods, and O(r² loge^-1){O(r^{2}\,\log\epsilon^{-1})} for the XS and SX methods, where r is the rank of the associated Euclidean Jordan algebra and ${\epsilon\,{ > }\,0}${\epsilon\,{ > }\,0} is a given tolerance. If the staring point is strictly feasible, then the corresponding bounds can be reduced by a factor of r ^3/4. The theory of Euclidean Jordan algebras is a basic tool in our analysis.     

A New Wide Neighborhood Primal–Dual Infeasible-Interior-Point Method for Symmetric Cone Programming

We present a new infeasible-interior-point method, based on a wide neighborhood, for symmetric cone programming. The convergence is shown for a commutative class of search directions, which includes the Nesterov–Todd direction and the xs and sx directions. Moreover, we derive the complexity bound of the wide neighborhood infeasible interior-point methods that coincides with the currently best known theoretical complexity bounds for the short step path-following algorithm.     

A new infeasible-interior-point algorithm for linear programming over symmetric cones

In this paper we present an infeasible-interior-point algorithm, based on a new wide neighbourhood N(τ₁, τ₂, η), for linear programming over symmetric cones. We treat the classical Newton direction as the sum of two other directions. We prove that if these two directions are equipped with different and appropriate step sizes, then the new algorithm has a polynomial convergence for the commutative class of search directions. In particular, the complexity bound is O(r^1.5logε^?1) for the Nesterov-Todd (NT) direction, and O(r²logε^?1) for the xs and sx directions, where r is the rank of the associated Euclidean Jordan algebra and ε > 0 is the required precision. If starting with a feasible point (x⁰, y⁰, s⁰) in N(τ₁, τ₂, η), the complexity bound is \(O\left( {\sqrt r \log {\varepsilon ^{ - 1}}} \right)\) for the NT direction, and O(rlogε^?1) for the xs and sx directions. When the NT search direction is used, we get the best complexity bound of wide neighborhood interior-point algorithm for linear programming over symmetric cones.     

An Arc Search Infeasible Interior-Point Algorithm for Symmetric Optimization Using a New Wide Neighborhood

In this paper, we propose an infeasible interior-point algorithm for symmetric optimization problems using a new wide neighborhood and estimating the central path by an ellipse. In contrast of most interior-point algorithms for symmetric optimization which search an \(\varepsilon\)-optimal solution of the problem in a small neighborhood of the central path, our algorithm searches for optimizers in a new wide neighborhood of the ellipsoidal approximation of central path. The convergence analysis of the algorithm is shown and it is proved that the iteration bound of the algorithm is \(O ( r\log\varepsilon^{-1} ) \) which improves the complexity bound of the recent proposed algorithm by Liu et al. (J. Optim. Theory Appl., 2013,  https://doi.org/10.1007/s10957-013-0303-y) for symmetric optimization by the factor \(r^{\frac{1}{2}}\) and matches the currently best-known iteration bound for infeasible interior-point methods.     

Two wide neighborhood interior-point methods for symmetric cone optimization

In this paper, we present two primal–dual interior-point algorithms for symmetric cone optimization problems. The algorithms produce a sequence of iterates in the wide neighborhood \(\mathcal {N}(\tau ,\,\beta )\) of the central path. The convergence is shown for a commutative class of search directions, which includes the Nesterov–Todd direction and the xs and sx directions. We derive that these two path-following algorithms have

$$\begin{aligned} \text{ O }\left( \sqrt{r\text{ cond }(G)}\log \varepsilon ^{-1}\right) , \text{ O }\left( \sqrt{r}\left( \text{ cond }(G)\right) ^{1/4}\log \varepsilon ^{-1}\right) \end{aligned}$$

iteration complexity bounds, respectively. The obtained complexity bounds are the best result in regard to the iteration complexity bound in the context of the path-following methods for symmetric cone optimization. Numerical results show that the algorithms are efficient for this kind of problems.

     

A new second-order corrector interior-point algorithm for semidefinite programming

In this paper, we propose a second-order corrector interior-point algorithm for semidefinite programming (SDP). This algorithm is based on the wide neighborhood. The complexity bound is O(?nL){O(\sqrt{n}L)} for the Nesterov-Todd direction, which coincides with the best known complexity results for SDP. To our best knowledge, this is the first wide neighborhood second-order corrector algorithm with the same complexity as small neighborhood interior-point methods for SDP. Some numerical results are provided as well.     

A New Second-Order Infeasible Primal-Dual Path-Following Algorithm for Symmetric Optimization

In this article, we propose a new second-order infeasible primal-dual path-following algorithm for symmetric cone optimization. The algorithm further improves the complexity bound of a wide infeasible primal-dual path-following algorithm. The theory of Euclidean Jordan algebras is used to carry out our analysis. The convergence is shown for a commutative class of search directions. In particular, the complexity bound is 𝒪(r^5/4log ?^?1) for the Nesterov-Todd direction, and 𝒪(r^7/4log ?^?1) for the xs and sx directions, where r is the rank of the associated Euclidean Jordan algebra and ? is the required precision. If the starting point is strictly feasible, then the corresponding bounds can be reduced by a factor of r^3/4. Some preliminary numerical results are provided as well.     

On Khachiyan's algorithm for the computation of minimum-volume enclosing ellipsoids

Given A?{a¹,…,a^m}⊂R^d whose affine hull is R^d, we study the problems of computing an approximate rounding of the convex hull of A and an approximation to the minimum-volume enclosing ellipsoid of A. In the case of centrally symmetric sets, we first establish that Khachiyan's barycentric coordinate descent (BCD) method is exactly the polar of the deepest cut ellipsoid method using two-sided symmetric cuts. This observation gives further insight into the efficient implementation of the BCD method. We then propose a variant algorithm which computes an approximate rounding of the convex hull of A, and which can also be used to compute an approximation to the minimum-volume enclosing ellipsoid of A. Our algorithm is a modification of the algorithm of Kumar and Y?ld?r?m, which combines Khachiyan's BCD method with a simple initialization scheme to achieve a slightly improved polynomial complexity result, and which returns a small “core set.” We establish that our algorithm computes an approximate solution to the dual optimization formulation of the minimum-volume enclosing ellipsoid problem that satisfies a more complete set of approximate optimality conditions than either of the two previous algorithms. Furthermore, this added benefit is achieved without any increase in the improved asymptotic complexity bound of the algorithm of Kumar and Y?ld?r?m or any increase in the bound on the size of the computed core set. In addition, the “dropping idea” used in our algorithm has the potential of computing smaller core sets in practice. We also discuss several possible variants of this dropping technique.