activity
20172026
most citedOn the Convergence Rate of Variants of the Conjugate Gradient Algorithm in Finite Precision Arithmetic

5 citations · 6 across the 6 of their papers we have counts for

collaborators

6 papers

math.CO2026

A Combinatorial Framework for the Pons-Batle Identity: Young Tableaux, Lattice Paths, and Limit Laws

Hexuan Liu, Michael Wallner, Guan-Ru Yu

Tree-child networks are an important class of phylogenetic network used to model reticulate evolutionary processes. These networks have attracted increasing attention from research…

math.CO2026

A characterization of terminal planar networks by forbidden structures

Haruki Miyaji, Yuki Noguchi, Hexuan Liu +4

The class of terminal planar networks was recently introduced from a biological perspective in relation to the visualization of phylogenetic networks, and its connection to upward…

math.CO2022

Enumerative and Distributional Results for -combining Tree-Child Networks

Yu-Sheng Chang, Michael Fuchs, Hexuan Liu +2

Tree-child networks are one of the most prominent network classes for modeling evolutionary processes which contain reticulation events. Several recent studies have addressed count…

math.NA2021

Analysis of Truncated Orthogonal Iteration for Sparse Eigenvector Problems

Hexuan Liu, Aleksandr Aravkin

A wide range of problems in computational science and engineering require estimation of sparse eigenvectors for high dimensional systems. Here, we propose two variants of the Trunc…

math.NA2019★ 5 cited

On the Convergence Rate of Variants of the Conjugate Gradient Algorithm in Finite Precision Arithmetic

Anne Greenbaum, Hexuan Liu, Tyler Chen

We consider three mathematically equivalent variants of the conjugate gradient (CG) algorithm and how they perform in finite precision arithmetic. It was shown in [{\em Behavior of…

q-bio.NC2017★ 1 cited

Functional Connectomics from Data: Probabilistic Graphical Models for Neuronal Network of C. elegans

Hexuan Liu, Jimin Kim, Eli Shlizerman

We propose a data-driven approach to represent neuronal network dynamics as a Probabilistic Graphical Model (PGM). Our approach learns the PGM structure by employing dimension redu…