arXiv (All)
open access
Whole-Sequence Convergence of Variable-Smoothing Full-Splitting Methods via a Lifted Kurdyka-Łojasiewicz Framework
We establish whole-sequence convergence of the primal iterates of the smoothing-based full-splitting proximal subgradient method (S-FSPS) of Boţ, Li, and Tao (SIAM J. Optim., 35 (2025), pp.~2623--2653) with a prescribed, nonsummable sequence of vanishing smoothing parameters. The challenge is that e
Conceptio Open Archive
Analyze & explore related research in Conceptio
Connect this record directly into your research workflow or AI agent. Explore citations across open repositories, query full text, and export structured proof bundles.
This document is indexed with metadata only: full text is not available in the archive for this record. Open the official source ↗
Related documents
- Variable Stepsize Distributed Forward-Backward Splitting Methods as Relocated Fixed-Point Iterations#1052079
- An Inexact Augmented Lagrangian Method for $(L_0, L_1)$-Smooth Convex Optimization#819718
- An Inexact Augmented Lagrangian Method for $(L_0, L_1)$-Smooth Convex Optimization#809722
- On the Local Linear Convergence of Operator Splitting Methods for Conic Programming#620355
- On the Local Linear Convergence of Operator Splitting Methods for Conic Programming#618676
Record · ID 1064850
Retrieved via Conceptio: every document is proof-bundled with source, license, and retrieval metadata.
Retrieved via Conceptio: every document is proof-bundled with source, license, and retrieval metadata.
Bring this data into your agent: Connect via MCP or API · 20 free credits to start · no card required
Read the Docs → · View CLI source on GitHub · Conceptio Open Knowledge Archive
Read the Docs → · View CLI source on GitHub · Conceptio Open Knowledge Archive