arXiv (All)
open access
Stabilized SQP Method for Convex Piecewise Linear-Quadratic Composite Optimization Problems
This paper addresses the local convergence analysis of the stabilized sequential quadratic programming method for a class of composite optimization problems with convex piecewise linear- quadratic (CPLQ) modeling functions. We demonstrate that an appropriate second-order sufficient condition (SOSC)
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
- GPU-Accelerated Conic Quadratic Programming with Local Linear Convergence under Strict Complementarity#441756
- CaCuTe Meets SESOP: Better Theory and Practice with Lipschitz Hessians#1215655
- Adaptive Variance-Reduced Stochastic Sequential (Quadratic Constrained) Quadratic Programming#1343938
- Finite-Time Convergence of Single-Trajectory Chi-Square Robust Q-Learning With Linear Function Approximation#925066
- Finite-Time Convergence of Single-Trajectory Chi-Square Robust Q-Learning With Linear Function Approximation#920832
Record · ID 1298237
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