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Xinyu Shan

Publications and source records attributed to Xinyu Shan.

3 recordsLinked to original sources

A Structure-Preserving LOBPCG Algorithm for the Bethe-Salpeter Eigenvalue Problem

The Bethe-Salpeter eigenvalue problem is a structured eigenvalue problem arising in many-body physics. In practice, a few of the smallest positive eigenvalues and the corresponding eigenvectors need to be computed. In principle, the LOBPCG algorithm can be applied to solve this eigenvalue problem. However, direct application of the existing LOBPCG algorithm does not utilize the inherent structure of the problem. We design a structure-preserving eigensolver based on the indefinite LOBPCG algorithm to efficiently solve the Bethe-Salpeter eigenvalue problem. We propose an improved Hetmaniuk-Lehoucq trick for the indefinite inner product, as well as an adaptive, multi-level orthogonalization strategy to ensure the numerical stability of our algorithm. Numerical experiments demonstrate that the proposed algorithm can efficiently and accurately compute the desired eigenpairs. Since the symplectic eigenvalue problem for symmetric positive definite matrices can be transformed to the Bethe-Salpeter eigenvalue problem, our algorithm can naturally be adopted as a symplectic eigensolver.

math.NA

A Contour Integral-Based Algorithm for Computing Generalized Singular Values

We propose a contour integral-based algorithm for computing a few singular values of a matrix or a few generalized singular values of a matrix pair. Mathematically, the generalized singular values of a matrix pair are the eigenvalues of an equivalent Hermitian-definite matrix pencil, known as the Jordan-Wielandt matrix pencil. However, direct application of the FEAST algorithm does not fully exploit the structure of this problem. We analyze several projection strategies on the Jordan-Wielandt matrix pencil, and propose an effective and robust scheme tailored to GSVD. Both theoretical analysis and numerical experiments demonstrate that our algorithm achieves rapid convergence and satisfactory accuracy.

math.NA

An Improved Two-Archive Evolutionary Algorithm for Constrained Multi-Objective Optimization

Constrained multi-objective optimization problems (CMOPs) are ubiquitous in real-world engineering optimization scenarios. A key issue in constrained multi-objective optimization is to strike a balance among convergence, diversity and feasibility. A recently proposed two-archive evolutionary algorithm for constrained multi-objective optimization (C-TAEA) has be shown as a latest algorithm. However, due to its simple implementation of the collaboration mechanism between its two co-evolving archives, C-TAEA is struggling when solving problems whose \textit{pseudo} Pareto-optimal front, which does not take constraints into consideration, dominates the \textit{feasible} Pareto-optimal front. In this paper, we propose an improved version C-TAEA, dubbed C-TAEA-II, featuring an improved update mechanism of two co-evolving archives and an adaptive mating selection mechanism to promote a better collaboration between co-evolving archives. Empirical results demonstrate the competitiveness of the proposed C-TAEA-II in comparison with five representative constrained evolutionary multi-objective optimization algorithms.

cs.NE