Entanglement,Quantum Phase Transitions,and Density Matrix Renormalization

We investigate the role of entanglement in quantum phase transitions, and show that the success of the density matrix renormalization group (DMRG) in understanding such phase transitions is due to the way it preserves entanglement under renormalization. We provide a reinterpretation of the DMRG in terms of the language and tools of quantum information science which allows us to rederive the DMRG in a physically transparent way. Motivated by our reinterpretation we suggest a modification of the DMRG which manifestly takes account of the entanglement in a quantum system. This modified renormalization scheme is shown, in certain special cases, to preserve more entanglement in a quantum system than traditional numerical renormalization methods. PACS: 03.65.Ud, 73.43.Nq, 05.10.-a     

基于矩阵对角化变换的高阶容积卡尔曼滤波

为了提高高阶容积卡尔曼滤波器(CKF)的滤波性能, 提出一种基于矩阵对角化变换的高阶CKF 算法. 该算法基于高阶容积准则, 利用矩阵对角化变换代替标准高阶CKF 中的Cholesky 分解, 使得协方差矩阵分解后的平方根矩阵保留了原有的特征空间信息, 状态统计量计算更加准确, 从而提高了滤波精度; 同时, 矩阵对角化变换不要求协方差矩阵正定, 增强了算法滤波稳定性. 仿真结果表明, 所提出的算法是可行而有效的, 明显改善了标准高阶CKF 的滤波效果.

     

Computation of extreme eigenvalues in higher dimensions using block tensor train format

We consider approximate computation of several minimal eigenpairs of large Hermitian matrices which come from high-dimensional problems. We use the tensor train (TT) format for vectors and matrices to overcome the curse of dimensionality and make storage and computational cost feasible. We approximate several low-lying eigenvectors simultaneously in the block version of the TT format. The computation is done by the alternating minimization of the block Rayleigh quotient sequentially for all TT cores. The proposed method combines the advances of the density matrix renormalization group (DMRG) and the variational numerical renormalization group (vNRG) methods. We compare the performance of the proposed method with several versions of the DMRG codes, and show that it may be preferable for systems with large dimension and/or mode size, or when a large number of eigenstates is sought.     

两正定矩阵联合对角化盲分离算法

针对具有时间结构的盲分离问题,提出了一种基于两正定矩阵精确联合对角化的盲分离算法。利用多个不同时延统计量构造了两个正定矩阵,以提取出数据的时间结构;再利用所提算法联合对角化构造的两个正定矩阵,得到分离矩阵,进而估计出源信号。所提算法克服了已有算法因采用多个矩阵联合对角化导致的计算量大和采用单个矩阵导致的分离精度低的缺点。计算机仿真结果表明了在有或无噪声情况下,所提算法性能均优于其他对比算法。     

Cross-diffusion controlled particle dissolution in metallic alloys

A model for particle dissolution is formulated for the case of several simultaneously diffusing and strongly interacting alloying elements. The model is analyzed by using diagonalization of the diffusion matrix. A numerical solution procedure, also based on diagonalization, is proposed. Further, self-similar solutions for the resulting moving boundary problem are derived for the case of both a diagonalizable and non-diagonalizable diffusion matrix. Finally, we use this technique to approximate a two-dimensional problem with Finite Elements.     

具有非奇异约束的线性卷积混合信号盲分离联合对角化方法

联合对角化能够成功解决盲分离问题, 但在求解时会得到非期望的奇异解, 从而无法完全分离出源信号. 鉴于此, 提出一种用于线性卷积混合盲分离的联合对角化方法, 将卷积混合模型变换为瞬时模型, 并对变换后的模型应用联合对角化求取分离矩阵. 在求解过程中, 引入约束条件对解的范围进行限定, 避免了奇异解的出现. 仿真结果表明, 所提出的方法能够成功实现卷积混合信号盲分离.

     

Joint diagonalization DOA matrix method

A novel joint diagonalization （DOA） matrix method is proposed to estimate the two-dimensional （2-D） DOAs of uncorrelated narrowband signals. The method constructs three subarrays by exploiting the special structure of the array, thereby obtaining the 2-D DOAs of the array based on joint diagonalization directly with neither peak search nor pair matching. The new method can handle sources with common 1-D angles. Simulation results show the effectiveness of the method.     

Computing the block factorization of complex Hankel matrices

In this paper, we present an algorithm for finding an approximate block diagonalization of complex Hankel matrices. Our method is based on inversion techniques of an upper triangular Toeplitz matrix, specifically, by simple forward substitution. We also consider an approximate block diagonalization of complex Hankel matrices via Schur complementation. An application of our algorithm by calculating the approximate polynomial quotient and remainder appearing in the Euclidean algorithm is also given. We have implemented our algorithms in Matlab. Numerical examples are included. They show the effectiveness of our strategy.     

A solution to the diagonalization problem by constantprecompensator and dynamic output feedback

A solution is presented for the problem of diagonalization (row-by-row decoupling). The problem is solved using a constant precompensator and a dynamic output feedback compensator of a p×m linear time-invariant system. The solvability condition is compact and concerns the dimension of a single subspace defined via the concepts of essential rows and static kernels associated with the transfer matrix. A characterization of the set of all solutions to the problem is also given. In solving this dynamic feedback problem, a complete solution to its state-feedback counterpart, namely, the restricted state-feedback problem of diagonalization, is also presented