arXiv (All)
open access
MaxCut for $\mathrm{MTP}_2$ Covariances
Let $X=(X_1,\dots,X_n)\in\{0,1\}^n$ have a multivariate totally positive ($\mathrm{MTP}_2$) law. We prove that $$ \sum_{i<j}\mathbb{E}\left[\left|\mathrm{Cov}(X_i,X_j \mid X_{[n]\setminus\{i,j\}})\right|\right] \le n/2, $$ and more generally a weighted MaxCut inequality for the fully conditioned cov
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
- Stochastic Domination of Gaussian Maxima by the Regular Simplex#1075922
- Sharp Tail Bounds Beyond Twice the Mean#1036755
- Tight Stochastic Condition-Number Dependence in Nonconvex-Strongly-Concave Minimax Optimization#1105436
- Stein Kernels and Normal Approximation for Log-Concave Bilinear Forms#819474
- Stein Kernels and Normal Approximation for Log-Concave Bilinear Forms#809478
Record · ID 997353
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