arXiv (All)
open access
A Stochastic LineseArch Method (SLAM) for Minimizing Expectation Residuals: Weakening Boundedness of Gradient
We establish convergence guarantees for the Stochastic LineseArch Method (SLAM) proposed and analyzed in Wang et al. (arXiv:2512.14979) for nonconvex composite stochastic optimization, retaining the iteration and sample complexity orders of the original analysis assuming neither the boundedness of t
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
- Randomized Submanifold Subgradient Method for Optimization over Stiefel Manifolds#1116072
- Mirror Descent Linearized Augmented Lagrangian Methods for Nonconvex Constrained Stochastic Zeroth-Order Optimization#793422
- Mirror Descent Linearized Augmented Lagrangian Methods for Nonconvex Constrained Stochastic Zeroth-Order Optimization#789418
- Reflected Anchored Langevin Algorithms#1233561
- The Role of Gradient Modification in Heavy-Tailed Nonconvex Stochastic Min-Max Optimization#973673
Record · ID 1488739
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