arXiv (All)
open access
Computational Guarantees for Restarted PDHG for LP based on "Limiting Error Ratios" and LP Sharpness
In recent years, there has been growing interest in solving linear optimization problems - or more simply "LP" - using first-order methods in order to avoid the costly matrix factorizations of traditional methods for huge-scale LP instances. The restarted primal-dual hybrid gradient method (PDHG) -
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
- Parameter optimization for restarted mixed precision iterative sparse solver#798573
- Parameter optimization for restarted mixed precision iterative sparse solver#797160
- Structural Packing and Dyadic Factorization of Sparse Positive Definite Matrices#798603
- Structural Packing and Dyadic Factorization of Sparse Positive Definite Matrices#797190
- Comparison of Numerical Solvers for Differential Equations for Holonomic Gradient Method in Statistics#128248
Record · ID 987365
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