arXiv (OAI Expanded)
open access
Accelerated Prox-Level Methods for Unknown Piecewise-Smooth Optimization II: Function-constrained Optimization
We introduce an anytime, almost parameter-free algorithm for convex function-constrained optimization, in which the objective and constraint functions are unknown piecewise-smooth. Our algorithm, Restarted Penalty APEX (Penalty Accelerated Prox-level method for Exploring Piecewise Smoothness), is an
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
- Linearly Convergent Algorithms for Nonsmooth Problems with Unknown Smooth Regions#1344448
- Accelerated Prox-Level Methods for Unknown Piecewise-Smooth Optimization II: Function-constrained Optimization#925605
- Accelerated Bregman Proximal Gradient Methods from Dual Geometric Perspectives#1129394
- Peppy: An AI-Assisted Workflow for Tight Convergence Analysis of Optimization Algorithms#1130407
- Doubly exponential convergence of the cyclic steepest descent method for strictly convex quadratics in arbitrary dimensions#1061430
Record · ID 1210052
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