.png){kind=icon}
Return
Greedy randomized-type methods for vertical tensor complementarity problem
.png){kind=icon}
PRE
AI
DOI：10.1016/j.cam.2026.117771.png)
.png){kind=icon}
Original request for help.png){kind=icon}
Share.png){kind=icon}
Save
Abstract
En 中文
The aim of this paper is to introduce two greedy randomized algorithms, i.e., the modified greedy randomized Kaczmarz (MGRK) algorithm and the modified greedy randomized coordinate descent (MGRCD) algorithm, to solve the vertical tensor complementarity problem (VTCP) with the type strong extended vertical P (EVP) tensors, and establish their convergences with expected exponential rates. Numerical results demonstrate that the greedy randomized-type methods are more effective than the existing randomized-type methods.
Keywords:
Modified greedy randomized Kaczmarz algorithm
Modified greedy randomized coordinate descent algorithm
Vertical tensor complementarity problem
Type strong EV P tensors
Convergence
Journal
J
IF:
2.6
Papers:
336
Citations:
0