Zobrazeno 1 - 10
of 165
pro vyhledávání: '"MING-JUN LAI"'
Publikováno v:
Journal of Algorithms & Computational Technology, Vol 15 (2021)
We present a convergence analysis for a finite difference scheme for the time dependent partial different equation called gradient flow associated with the Rudin-Osher-Fetami model. We devise an iterative algorithm to compute the solution of the fini
Externí odkaz:
https://doaj.org/article/7eac299f072b4ff09026ee41613333f3
Publikováno v:
Journal of Algorithms & Computational Technology, Vol 13 (2019)
Total variation smoothing methods have been proven to be very efficient at discriminating between structures (edges and textures) and noise in images. Recently, it was shown that such methods do not create new discontinuities and preserve the modulus
Externí odkaz:
https://doaj.org/article/115818f5093a41a5a8cdc78f71e1b89e
Publikováno v:
Journal of Computational Mathematics. 41:18-38
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::b783f59941b67ef661cf37813b501ad2
https://doi.org/10.4208/jcm.2104-m2020-0250
https://doi.org/10.4208/jcm.2104-m2020-0250
Autor:
Ming-Jun Lai, Larry L. Schumaker
Spline functions are universally recognized as highly effective tools in approximation theory, computer-aided geometric design, image analysis, and numerical analysis. The theory of univariate splines is well known but this text is the first comprehe
Publikováno v:
Applied and Computational Harmonic Analysis. 51:157-170
The first part of the paper proves the conjectures on inequalities in the Schatten p-quasi-norm of matrices. The second part of the paper uses the inequalities for proving a sufficient condition when the Schatten p-quasi-norm minimization can be used
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::75d5bce6a592841d50411ef699d17eab
https://doi.org/10.1016/j.acha.2020.11.001
https://doi.org/10.1016/j.acha.2020.11.001
Autor:
Ming-Jun Lai, Daniel Mckenzie
Publikováno v:
SIAM Journal on Mathematics of Data Science. 2:368-395
We show how one can phrase the cut improvement problem for graphs as a sparse recovery problem, whence one can use algorithms originally developed for use in compressive sensing (such as SubspacePursuit or CoSaMP) to solve it. We show that this appro
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::ea19195a6e0e77ef825294939bcd483a
https://doi.org/10.1137/19m1265971
https://doi.org/10.1137/19m1265971
Autor:
Ming-Jun Lai, Zhaiming Shen
A least squares semi-supervised local clustering algorithm based on the idea of compressed sensing is proposed to extract clusters from a graph with known adjacency matrix. The algorithm is based on a two-stage approach similar to the one in \cite{La
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::372711b52819bd944726d52c7f9e30d5
http://arxiv.org/abs/2202.02904
http://arxiv.org/abs/2202.02904
Publikováno v:
SSRN Electronic Journal.
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::5ee690f5eb11e386a3bedcb4f4603ffc
https://doi.org/10.2139/ssrn.4035462
https://doi.org/10.2139/ssrn.4035462
Autor:
Ming-Jun Lai, Jinsil Lee
We use trivariate spline functions for the numerical solution of the Dirichlet problem of the 3D elliptic Monge-Ampére equation. Mainly we use the spline collocation method introduced in [SIAM J. Numerical Analysis, 2405-2434,2022] to numerically so
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::4397378b40af5bafeef751f68aad1e75
Publikováno v:
Computer-Aided Design. 115:78-86
We introduce a redundant basis for numerical solution to the Poisson equation and find a sparse solution to the PDE by using a compressive sensing approach. That is, we refine a partition of the underlying domain of the PDE several times and use the
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::174694a31d3607ea20ebbf05a6b44ed5
https://doi.org/10.1016/j.cad.2019.05.021
https://doi.org/10.1016/j.cad.2019.05.021