New lower bounds for partial k‐parallelisms

Due to the applications in network coding, subspace codes and designs have received many attentions. Suppose that $k\mid n$ and $V\left(n,q\right)$ is an $n$‐dimensional space over the finite field ${\mathbb{F}}_q$. A $k$‐spread is a $(q^n?1)/(q^k?1)$‐set of $k$‐dimensional subspaces of $V\left(n,q\right)$ such that each nonzero vector is contained in exactly one element of it. A partial $k$‐parallelism in $V\left(n,q\right)$ is a set of pairwise disjoint $k$‐spreads. As the number of $k$‐dimensional subspaces in $V\left(n,q\right)$ is ${\left[\genfrac{}{}{0ex}{}{n}{k}\right]}_q$, there are at most ${\left[\genfrac{}{}{0ex}{}{n?1}{k?1}\right]}_q$ spreads in a partial $k$‐parallelism. By studying the independence numbers of Cayley graphs associated to a special type of partial $k$‐parallelisms in $V\left(n,q\right)$, we obtain new lower bounds for partial $k$‐parallelisms. In particular, we show that there exist at least $\frac{q^k?1}{q^n?1}{\left[\genfrac{}{}{0ex}{}{n?1}{k?1}\right]}_q$ pairwise disjoint $k$‐spreads in $V\left(n,q\right)$.     

拉曼光谱子空间重合识别修正液中氯代烃

利用拉曼光谱子空间重合对修正液中氯代烃成分进行识别分析。通过计算混合卤代烃组分与标准样品数据库拉曼光谱之间的子间空夹角,依据夹角变化排列筛选出含有最少标准样品数目的子空间,该子空间所含的标准样品组成为待定性混合氯代烃组成,从而实现对混合氯代烃组分的定性分析。将该方法用于修正液中氯代烃的成分定性分析,准确率达100%。该方法操作简单,检测时间短,准确率高,适用于多组分混合体系中的物质定性分析。     

Characterization of derivations on B(X) by local actions

Let A be a unital algebra and M be a unital A-bimodule. A linear map δ : A →M is said to be Jordan derivable at a nontrivial idempotent P ∈ A if δ(A) ? B + A ? δ(B) =δ(A ? B) for any A, B ∈ A with A ? B = P, here A ? B = AB + BA is the usual Jordan product. In this article, we show that if A = Alg N is a Hilbert space nest algebra and M = B(H), or A = M = B(X), then, a linear map δ : A → M is Jordan derivable at a nontrivial projection P ∈ N or an arbitrary but fixed nontrivial idempotent P ∈ B(X) if and only if it is a derivation. New equivalent characterization of derivations on these operator algebras was obtained.     

Sufficient Dimension Reduction and Variable Selection for Large-p-Small-n Data With Highly Correlated Predictors

Sufficient dimension reduction (SDR) is a paradigm for reducing the dimension of the predictors without losing regression information. Most SDR methods require inverting the covariance matrix of the predictors. This hinders their use in the analysis of contemporary datasets where the number of predictors exceeds the available sample size and the predictors are highly correlated. To this end, by incorporating the seeded SDR idea and the sequential dimension-reduction framework, we propose a SDR method for high-dimensional data with correlated predictors. The performance of the proposed method is studied via extensive simulations. To demonstrate its use, an application to microarray gene expression data where the response is the production rate of riboflavin (vitamin B₂) is presented.     

Local reconstruction of low‐rank matrices and subspaces

We study the problem of reconstructing a low‐rank matrix, where the input is an n × m matrix M over a field and the goal is to reconstruct a (near‐optimal) matrix that is low‐rank and close to M under some distance function Δ. Furthermore, the reconstruction must be local, i.e., provides access to any desired entry of by reading only a few entries of the input M (ideally, independent of the matrix dimensions n and m). Our formulation of this problem is inspired by the local reconstruction framework of Saks and Seshadhri (SICOMP, 2010). Our main result is a local reconstruction algorithm for the case where Δ is the normalized Hamming distance (between matrices). Given M that is ‐close to a matrix of rank (together with d and ), this algorithm computes with high probability a rank‐d matrix that is ‐close to M. This is a local algorithm that proceeds in two phases. The preprocessing phase reads only random entries of M, and stores a small data structure. The query phase deterministically outputs a desired entry by reading only the data structure and 2d additional entries of M. We also consider local reconstruction in an easier setting, where the algorithm can read an entire matrix column in a single operation. When Δ is the normalized Hamming distance between vectors, we derive an algorithm that runs in polynomial time by applying our main result for matrix reconstruction. For comparison, when Δ is the truncated Euclidean distance and , we analyze sampling algorithms by using statistical learning tools. A preliminary version of this paper appears appears in ECCC, see: http://eccc.hpi-web.de/report/2015/128/ © 2017 Wiley Periodicals, Inc. Random Struct. Alg., 51, 607–630, 2017     

Tensor train completion: Local recovery guarantees via Riemannian optimization

In this work, we estimate the number of randomly selected elements of a tensor that with high probability guarantees local convergence of Riemannian gradient descent for tensor train completion. We derive a new bound for the orthogonal projections onto the tangent spaces based on the harmonic mean of the unfoldings' singular values and introduce a notion of core coherence for tensor trains. We also extend the results to tensor train completion with auxiliary subspace information and obtain the corresponding local convergence guarantees.     

计及气动和隐身约束结构综合优化的并行子空间优化方法

通过分析基于响应面的并行子空间优化算法的特点指出并行子空间优化算法学科级优化的作用在于向系统级优化响应面提供性能优良的设计点.在此基础上,建立了不要求学科级优化的改进的并行子空间优化算法,进一步降低了设计优化的计算量,解决了分析模块与优化模块间的接口困难.依据该算法建立了结构、气动和隐身一体化设计优化框架,实现了某无人机机翼计及气动和隐身约束的结构综合优化.     

杂交元本征应力模式和应力子空间的性质研究

详细讨论了有限元本征应力模式和应力子空间的性质，并着重讨论和进一步完善了与杂交应力有限元应力子空间有关的一些定理，为提出新方法提供了理论基础，主要包括：（1）证明了杂交元特征值不大于对应位移元的特征值；（2）证明了矩阵H非奇异的充分必要条件是假设应力模式线性无关；（3）证明了杂交元所对应位移元的本征应力模式形成的杂交元与该位移元相同；（4）证明了等价假设应力模式形成相同的杂交元；（5）证明了确定杂交元本征应力模式的充分必要条件是其范数平方等于所形成杂交元的变形模态特征值；（6）证明了杂交元假设应力模式与变形模态的能量一一对应的充分必要条件是假设应力模式彼此正交且与所对应位移元的本征应力模式除了一一对应者之外都正交。