arXiv (All)
open access
Moment Relaxations for Data-Driven Wasserstein Distributionally Robust Optimization
We propose moment relaxations for data-driven $p$-Wasserstein distributionally robust optimization ($p$-WDRO) problems that are defined by polynomials. The proposed moment relaxations admit Benders-type decomposition with parallel evaluation of the subgradients using each sample subproblem, which en
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
- Distributionally Robust Optimization over Wasserstein Balls with i.i.d. Structure#139140
- Sampled-Max Subgradient Method for Convex Finite-Max Optimization#1117585
- DIFFRACT: Neuralized Utility Maximization for Wireless Networks by Differentiable Programming#266136
- M-duality in fixed-point problems for general normed spaces#985533
- Doubly Robust Estimators with Weak Overlap#146983
Record · ID 927129
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