arXiv (All)
open access
Exact augmented Lagrangian duality for mixed integer convex optimization
Augmented Lagrangian dual augments the classical Lagrangian dual with a non-negative non-linear penalty function of the violation of the relaxed/dualized constraints in order to reduce the duality gap. We investigate the cases in which mixed integer convex optimization problems have an exact penalty
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
- Extended Formulations in Mixed-Integer Convex Programming#364420
- A Bounded Degree SOS Plus SONC Hierarchy for Polynomial Optimization#1061859
- An exact duality theory for semidefinite programming and its complexity implications#445455
- Sparse Cuts for the Positive Semidefinite Cone#793594
- Sparse Cuts for the Positive Semidefinite Cone#789590
Record · ID 1199603
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