8 citations · 16 across the 14 of their papers we have counts for
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On Convergence Behavior of Randomized Kaczmarz-type Methods for Solving Doubly Noisy Linear Systems
Yudan Gan, Gang Wu
In this paper, we consider the convergence of some popular Kaczmarz-type methods, including the randomized Kaczmarz (RK) method, the randomized extended Kaczmarz (REK) method, the…
On convergence of the generalized Lanczos trust-region method for trust-region subproblems
Bo Feng, Gang Wu
The generalized Lanczos trust-region (GLTR) method is one of the most popular approaches for solving large-scale trust-region subproblem (TRS). Recently, Jia and Wang [Z. Jia and F…
Sparse power methods for large-scale higher-order PageRank problems
Jun Huang, Gang Wu
A commonly used technique for the higher-order PageRank problem is the power method that is computationally intractable for large-scale problems. The truncated power method propose…
A Kaczmarz Method with Simple Random Sampling for Solving Large Linear Systems
Yutong Jiang, Gang Wu, Long Jiang
The Kaczmarz method is a popular iterative scheme for solving large-scale linear systems. The randomized Kaczmarz method (RK) greatly improves the convergence rate of the Kaczmarz…
A weighted global GMRES algorithm with deflation for solving large Sylvester matrix equations
Najmeh Azizi Zadeh, Azita Tajaddini, Gang Wu
The solution of large scale Sylvester matrix equation plays an important role in control and large scientific computations. A popular approach is to use the global GMRES algorithm.…
Refined Bounds on the Number of Distinct Eigenvalues of a Matrix After Perturbation
Yunjie Wang, Gang Wu
The eigenproblem of low-rank updated matrices are of crucial importance in many applications. Recently, an upper bound on the number of distinct eigenvalues of a perturbed matrix w…
A New Method for Computing -functions and Their Condition Numbers of Large Sparse Matrices
Gang Wu, Lu Zhang
We propose a new method for computing the -functions of large sparse matrices with low rank or fast decaying singular values. The key is to reduce the computation of -…
On the convergence of harmonic Ritz vectors and harmonic Ritz values
Gang Wu
We are interested in computing a simple eigenpair of a large non-Hermitian matrix , by a general harmonic Rayleigh-Ritz projection method. Given a search subspace…
Inexact Krylov Subspace Algorithms for Large Matrix Exponential Eigenproblem from Dimensionality Reduction
Gang Wu, Ting-ting Feng, Li-jia Zhang +1
Matrix exponential discriminant analysis (EDA) is a generalized discriminant analysis method based on matrix exponential. It can essentially overcome the intrinsic difficulty of sm…
On the correction equation of the Jacobi-Davidson method
Gang Wu, Hong-kui Pang
The Jacobi-Davidson method is one of the most popular approaches for iteratively computing a few eigenvalues and their associated eigenvectors of a large matrix. The key of this me…
Inexact Shift-and-Invert Arnoldi for Toeplitz Matrix Exponential
Ting-ting Feng, Gang Wu, Yimin Wei
We revisit the shift-and-invert Arnoldi method proposed in [S. Lee, H. Pang, and H. Sun. {\it Shift-invert Arnoldi approximation to the Toeplitz matrix exponential}, SIAM J. Sci. C…
A theoretical contribution to the fast implementation of null linear discriminant analysis method using random matrix multiplication with scatter matrices
Ting-ting Feng, Gang Wu
The null linear discriminant analysis method is a competitive approach for dimensionality reduction. The implementation of this method, however, is computationally expensive. Recen…
A framework of the harmonic Arnoldi method for evaluating -functions with applications to exponential integrators
Gang Wu, Lu Zhang, Ting-ting Xu
In recent years, a great deal of attention has been focused on numerically solving exponential integrators. The important ingredient to the implementation of exponential integrator…
Preconditioning the Restarted and Shifted Block FOM Algorithm for Matrix Exponential Computation
Gang Wu, Hong-kui Pang, Jiang-li Sun
The approximation of where is a large sparse matrix and a rectangular matrix is the key ingredient in many scientific and engineering computations. A powerful too…