.png){kind=icon}
.png){kind=icon}
Return
A generalized multi-parameterized proximal point algorithm for linearly constrained convex optimization problem
.png){kind=icon}
PRE
AI
DOI：10.1016/j.cam.2026.117529.png)
.png){kind=icon}
Original request for help.png){kind=icon}
Share.png){kind=icon}
Save
Abstract
En 中文
Proximal point algorithm (PPA) is an important class of methods for solving the linearly constrained convex problem. In this article, we propose a generalized multi-parameterized proximal point algorithm (GM-PPA) to solve the linearly constrained convex minimization problem by introducing a new generized proximal matrix to the prototypical framework of PPA. The proposed algorithm reduces to some existing algorithms when these parameters take some certain values. Therefore, the proposed method is more general and flexible. Also, by appropriately setting the newly involved parameters, our algorithm should converge faster than some existing PPAs. Numerical experiments on synthetic problem were conducted to demonstrate the efficiency of our algorithm.
Keywords:
Customized proximal point algorithm
Proximal point algorithm
Linearly constraint convex optimization
problem
Global convergence
Journal
J
IF:
2.6
Papers:
315
Citations:
0