多元统计分析中一类矩阵迹函数最小化问题的有效算法

研究来源于多元统计分析中的一类矩阵迹函数最小化问题$\min c+ tr(AX)+\sum\limits_{j=1}^{m}tr(B_j X C_jX^{T}),\ \ {\rm s. t.} \ X^TX=I_p,$其中$c$为常数, $A\in R^{p\times n}\ (n\geq p)$, $B_j\in R^{n\times n}, C_j\in R^{p\times p}$为给定系数矩阵. 数值实验表明已有的Majorization算法虽可行, 但收敛速度缓慢且精度不高. 本文从黎曼流形的角度重新研究该问题, 基于Stiefel流形的几何性质, 构造一类黎曼非单调共轭梯度迭代求解算法, 并给出算法收敛性分析.数值实验和数值比较验证所提出的算法对于问题模型是高效可行的.     

On the rate of convergence of solutions in free boundary problems via penalization

The rate of convergence of approximate solutions via penalization for free boundary problems are concerned. A key observation is to obtain global bounds of penalized terms which give necessary estimates on integrations by the nonlinear adjoint method by L.C. Evans.     

S-O bond chemically constrained NiS2/rGO nanocomposite with enhanced Na-ion storage capacity

NiS₂ has become a research hotspot of anode materials for Na-ion batteries due to its high theoretical specific capacity. However, the volume effect, the dissolution of polysulfide intermediates and the low conductivity during the charge/discharge process lead to the low specific capacity and poor cycling stability. NiS₂/rGO nanocomposite was prepared by a facile two-step process: GO was prepared by modified Hummers method, and then NiS₂/rGO nanocomposite was synthesized by l-cys assisted hydrothermal method. NiS₂/rGO nanocomposite shows excellent cycle performance and rate performance, which could be attributed to the mesoporous structure on the graphene skeleton with high conductivity. Besides, the chemical constraint of a unique SO bond on NiS₂ could inhibit the dissolution of intermediates and the loss of irreversible capacity.     

融合插值点优化的多项式结构宽带波束形成器设计方法*

多项式结构设计方法是主瓣指向可调宽带波束形成器设计的一类重要方法。多项式结构的阶数是有限的,导致主瓣实际指向与期望指向之间存在偏差,因而影响了波束形成器的指向性指数。针对这一问题,该文提出了一种基于插值点优化的多项式结构宽带波束形成器设计方法。首先,引入多项式结构插值点处阵列响应的空间导数约束,以减小主瓣指向偏差;进而利用粒子群优化算法对多项式结构中的插值点进行优化,以充分利用插值点位置提供的自由度进一步提升多项式结构宽带波束形成器的性能。优化设计结果表明,与现有设计方法相比,该文提出的方法不仅降低了主瓣的指向偏差,同时也提高了指向性指数,有效改善了多项式结构宽带波束形成器的性能。     

均衡约束数学规划问题的一种新的约束规格

本文研究了均衡约束数学规划(MPEC)问题.利用其弱稳定点,获得了一种新的约束规格–MPEC的伪正规约束规格.用一种简单的方式,证明了该约束规格是介于MPEC-MFCQ(即MPEC,Mangasarian-Fromowitz约束规格)与MPEC-ACQ(即MPEC,Abadie约束规格)之间的约束规格,因此该约束规格也可以导出MPEC问题的M-稳定点.最后通过两个例子,说明了该约束规格与MPEC-MFCQ以及与MPEC-ACQ之间是严格的强弱关系.     

Existence and limit behavior of prescribed L2‐norm solutions for Schrödinger‐Poisson‐Slater systems in

In this paper, we study constraint minimizers of the following L ²?critical minimization problem: where E (u ) is the Schrödinger‐Poisson‐Slater functional and N denotes the mass of the particles in the Schrödinger‐Poisson‐Slater system. We prove that e (N ) admits minimizers for and, however, no minimizers for N >N ^?, where Q (x ) is the unique positive solution of in . Some results on the existence and nonexistence of minimizers for e (N ^?) are also established. Further, when e (N ^?) does not admit minimizers, the limit behavior of minimizers as N ↗N ^? is also analyzed rigorously.     

高维约束矩阵回归问题

高维约束矩阵回归是指高维情况下带非凸约束的多响应多预测统计回归问题,其数学模型是一个NP-难的矩阵优化,它在机器学习与人工智能、医学影像疾病诊疗、基因表达分析、脑神经网络、风险管理等领域有广泛应用.从高维约束矩阵回归的优化理论和算法两方面总结和评述这些新成果,同时,列出了相应的重要文献.     

Regularized Principal Component Analysis for Spatial Data

In many atmospheric and earth sciences, it is of interest to identify dominant spatial patterns of variation based on data observed at p locations and n time points with the possibility that p > n. While principal component analysis (PCA) is commonly applied to find the dominant patterns, the eigenimages produced from PCA may exhibit patterns that are too noisy to be physically meaningful when p is large relative to n. To obtain more precise estimates of eigenimages, we propose a regularization approach incorporating smoothness and sparseness of eigenimages, while accounting for their orthogonality. Our method allows data taken at irregularly spaced or sparse locations. In addition, the resulting optimization problem can be solved using the alternating direction method of multipliers, which is easy to implement, and applicable to a large spatial dataset. Furthermore, the estimated eigenfunctions provide a natural basis for representing the underlying spatial process in a spatial random-effects model, from which spatial covariance function estimation and spatial prediction can be efficiently performed using a regularized fixed-rank kriging method. Finally, the effectiveness of the proposed method is demonstrated by several numerical examples.