| Full text | |
| Author(s): |
Massambone, Rafael
;
Costa, Eduardo Fontoura
;
Helou, Elias Salomao
Total Authors: 3
|
| Document type: | Journal article |
| Source: | IEEE Transactions on Automatic Control; v. 68, n. 1, p. 16-pg., 2023-01-01. |
| Abstract | |
In this article, a stochastic incremental subgradient algorithm for the minimization of a sum of convex functions is introduced. The method sequentially uses partial subgradient information, and the sequence of partial subgradients is determined by a general Markov chain. This makes it suitable to be used in networks, where the path of information flow is stochastically selected. We prove convergence of the algorithm to a weighted objective function, where the weights are given by the Cesaro limiting probability distribution of the Markov chain. Unlike previous works in the literature, the Cesaro limiting distribution is general (not necessarily uniform), allowing for general weighted objective functions and flexibility in the method. (AU) | |
| FAPESP's process: | 17/20934-9 - Filtering and control of jump parameter systems |
| Grantee: | Eduardo Fontoura Costa |
| Support Opportunities: | Regular Research Grants |
| FAPESP's process: | 13/07375-0 - CeMEAI - Center for Mathematical Sciences Applied to Industry |
| Grantee: | Francisco Louzada Neto |
| Support Opportunities: | Research Grants - Research, Innovation and Dissemination Centers - RIDC |