Hao Ji

Convergence Analysis of Markov Chain Monte Carlo Linear Solvers Using Ulam--von Neumann Algorithm

The convergence of Markov chain--based Monte Carlo linear solvers using the Ulam--von Neumann algorithm for a linear system of the form

A load-balancing workload distribution scheme for three-body interaction computation on Graphics Processing Units (GPU)

x = Hx + b

An implementation of block conjugate gradient algorithm on CPU-GPU processors

is investigated in this paper. We analyze the convergence of the Monte Carlo solver based on the original Ulam--von Neumann algorithm under the conditions that

Reusing Random Walks in Monte Carlo Methods for Linear Systems

\|H\|<1

Monte Carlo Methods and Their Applications in Big Data Analysis

as well as

The impact of XBRL on information asymmetry: evidence from loan contracting

\rho(H)<1

Block Conjugate Gradient algorithms for least squares problems

, where

An Apache Spark Implementation of Block Power Method for Computing Dominant Eigenvalues and Eigenvectors of Large-Scale Matrices

\rho(H)

The visualization of constraints conflict in collaborative design

is the spectral radius of

A breakdown-free block conjugate gradient method

H