arXiv (All)
open access
AHEAD: Adaptive Neural Acceleration for Large-Scale Linear Programming
Large-scale linear programs with millions of variables and constraints strain time and memory budgets. First-order methods such as the primal-dual hybrid gradient scale to this regime because they avoid matrix factorizations, but they require many iterations. Learned accelerators can reduce these it
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
- A Scalable Learned Iterative Solver for Nonlinear MPC via Temporal Decomposition#1297832
- Reinforcement Learning to Accelerate Primal-Dual Hybrid Gradient for Linear Programming#1156818
- GPU-Accelerated Presolving for Linear Programming#1110560
- PDNQP: A GPU-based Factorization-free Method for Large-scale Nonconvex Quadratic Programming#1014149
- Decision Titan: Test-Time Training for Long-Term Memory in Offline Reinforcement Learning#1156785
Record · ID 1467645
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