10 citations · 45 across the 28 of their papers we have counts for
28 papers
Reducing operator complexity in Algebraic Multigrid with Machine Learning Approaches
Ru Huang, Kai Chang, Huan He +2
We propose a data-driven and machine-learning-based approach to compute non-Galerkin coarse-grid operators in algebraic multigrid (AMG) methods, addressing the well-known issue of…
NLTGCR: A class of Nonlinear Acceleration Procedures based on Conjugate Residuals
Huan He, Ziyuan Tang, Shifan Zhao +2
This paper develops a new class of nonlinear acceleration algorithms based on extending conjugate residual-type procedures from linear to nonlinear equations. The main algorithm ha…
An Adaptive Factorized Nyström Preconditioner for Regularized Kernel Matrices
Shifan Zhao, Tianshi Xu, Hua Huang +2
The spectrum of a kernel matrix significantly depends on the parameter values of the kernel function used to define the kernel matrix. This makes it challenging to design a precond…
MedDiff: Generating Electronic Health Records using Accelerated Denoising Diffusion Model
Huan He, Shifan Zhao, Yuanzhe Xi +1
Due to patient privacy protection concerns, machine learning research in healthcare has been undeniably slower and limited than in other application domains. High-quality, realisti…
MuG: A Multimodal Classification Benchmark on Game Data with Tabular, Textual, and Visual Fields
Jiaying Lu, Yongchen Qian, Shifan Zhao +2
Previous research has demonstrated the advantages of integrating data from multiple sources over traditional unimodal data, leading to the emergence of numerous novel multimodal ap…
Data-Driven Linear Complexity Low-Rank Approximation of General Kernel Matrices: A Geometric Approach
Difeng Cai, Edmond Chow, Yuanzhe Xi
A general, {\em rectangular} kernel matrix may be defined as where is a kernel function and where and are two…
An Efficient Nonlinear Acceleration method that Exploits Symmetry of the Hessian
Huan He, Shifan Zhao, Ziyuan Tang +3
Nonlinear acceleration methods are powerful techniques to speed up fixed-point iterations. However, many acceleration methods require storing a large number of previous iterates an…
AUTM Flow: Atomic Unrestricted Time Machine for Monotonic Normalizing Flows
Difeng Cai, Yuliang Ji, Huan He +2
Nonlinear monotone transformations are used extensively in normalizing flows to construct invertible triangular mappings from simple distributions to complex ones. In existing lite…
Data-driven Construction of Hierarchical Matrices with Nested Bases
Difeng Cai, Hua Huang, Edmond Chow +1
Hierarchical matrices provide a powerful representation for significantly reducing the computational complexity associated with dense kernel matrices. For general kernel functions,…
parGeMSLR: A Parallel Multilevel Schur Complement Low-Rank Preconditioning and Solution Package for General Sparse Matrices
Tianshi Xu, Vassilis Kalantzis, Ruipeng Li +3
This paper discusses parGeMSLR, a C++/MPI software library for the solution of sparse systems of linear algebraic equations via preconditioned Krylov subspace methods in distribute…
GDA-AM: On the effectiveness of solving minimax optimization via Anderson Acceleration
Huan He, Shifan Zhao, Yuanzhe Xi +2
Many modern machine learning algorithms such as generative adversarial networks (GANs) and adversarial training can be formulated as minimax optimization. Gradient descent ascent (…
Fast randomized non-Hermitian eigensolver based on rational filtering and matrix partitioning
Vassilis Kalantzis, Yuanzhe Xi, Lior Horesh
This paper describes a set of rational filtering algorithms to compute a few eigenvalues (and associated eigenvectors) of non-Hermitian matrix pencils. Our interest lies in computi…
Learning optimal multigrid smoothers via neural networks
Ru Huang, Ruipeng Li, Yuanzhe Xi
Multigrid methods are one of the most efficient techniques for solving linear systems arising from Partial Differential Equations (PDEs) and graph Laplacians from machine learning…
Fast deterministic approximation of symmetric indefinite kernel matrices with high dimensional datasets
Difeng Cai, James Nagy, Yuanzhe Xi
Kernel methods are used frequently in various applications of machine learning. For large-scale high dimensional applications, the success of kernel methods hinges on the ability t…
Generating a Doppelganger Graph: Resembling but Distinct
Yuliang Ji, Ru Huang, Jie Chen +1
Deep generative models, since their inception, have become increasingly more capable of generating novel and perceptually realistic signals (e.g., images and sound waves). With the…
A power Schur complement Low-Rank correction preconditioner for general sparse linear systems
Qingqing Zheng, Yuanzhe Xi, Yousef Saad
An effective power based parallel preconditioner is proposed for general large sparse linear systems. The preconditioner combines a power series expansion method with some low-rank…
An ADMM-LAP method for total variation myopic deconvolution of adaptive optics retinal images
Xiaotong Chen, James L. Herring, James G. Nagy +2
Adaptive optics (AO) corrected ood imaging of the retina is a popular technique for studying the retinal structure and function in the living eye. However, the raw retinal images a…
The computation of seismic normal modes with rotation as a quadratic eigenvalue problem
Jia Shi, Ruipeng Li, Yuanzhe Xi +2
A new approach is presented to compute the seismic normal modes of a fully heterogeneous, rotating planet. Special care is taken to separate out the essential spectrum in the prese…
A non-perturbative approach to computing seismic normal modes in rotating planets
Jia Shi, Ruipeng Li, Yuanzhe Xi +2
A Continuous Galerkin method-based approach is presented to compute the seismic normal modes of rotating planets. Special care is taken to separate out the essential spectrum in th…
Structured FISTA for Image Restoration
Zixuan Chen, James Nagy, Yuanzhe Xi +1
In this paper, we propose an efficient numerical scheme for solving some large scale ill-posed linear inverse problems arising from image restoration. In order to accelerate the co…
Solving the 3D High-Frequency Helmholtz Equation using Contour Integration and Polynomial Preconditioning
Xiao Liu, Yuanzhe Xi, Yousef Saad +1
We propose an iterative solution method for the 3D high-frequency Helmholtz equation that exploits a contour integral formulation of spectral projectors. In this framework, the sol…
The Eigenvalues Slicing Library (EVSL): Algorithms, Implementation, and Software
Ruipeng Li, Yuanzhe Xi, Lucas Erlandson +1
This paper describes a software package called EVSL (for EigenValues Slicing Library) for solving large sparse real symmetric standard and generalized eigenvalue problems. As its n…
Beyond AMLS: Domain decomposition with rational filtering
Vassilis Kalantzis, Yuanzhe Xi, Yousef Saad
This paper proposes a rational filtering domain decomposition technique for the solution of large and sparse symmetric generalized eigenvalue problems. The proposed technique is pu…
A Multi-level Mixed Element scheme of the two dimensional helmholtz transmission eigenvalue problem
Y. Xi, X. Ji, S. Zhang
In this paper, we present a multi-level mixed element scheme for the Helmholtz transmission eigenvalue problem on polygonal domains that are not necessarily able to be covered by r…