aiqbench analysis
This paper advances theoretical machine learning by extending certificates of optimality beyond polynomial functions to abstract function algebras over ordered fields. It provides a rigorous framework for deriving lower-bound and global-optimality guarantees using continuous or definable operations, which is relevant for analyzing complex learning problem constraints.
When Can One Obtain Certificates of Optimality Using Positivstellensaetze?
arXiv:2609.08736v1 Announce Type: new Abstract: We study certificates of positivity and optimality for learning problems whose objectives and constraints need not be polynomial. We isolate an axiomatic core of Fischer's constructive strict and weak Positivstellens\"{a}tze and prove the resulting theorems for abstract function algebras over ordered fields. The framework separates two roles that can otherwise be conflated: objective and constraint functions may be built from broad classes of continuous or definable operations, while the auxiliary primitives used to construct a certificate satisfy explicit scalar and closure axioms. We give instances over continuous and definable function algebras, including ordered fields not closed under square roots, derive lower-bound and global-optimality certificates, and analyze both expanded term length and shared computation-graph complexity.
Title:When Can One Obtain Certificates of Optimality Using Positivstellensaetze?
Abstract:We study certificates of positivity and optimality for learning problems whose objectives and constraints need not be polynomial. We isolate an axiomatic core of Fischer's constructive strict and weak Positivstellensätze and prove the resulting theorems for abstract function algebras over ordered fields. The framework separates two roles that can otherwise be conflated: objective and constraint functions may be built from broad classes of continuous or definable operations, while the auxiliary primitives used to construct a certificate satisfy explicit scalar and closure axioms. We give instances over continuous and definable function algebras, including ordered fields not closed under square roots, derive lower-bound and global-optimality certificates, and analyze both expanded term length and shared computation-graph complexity.
Artificial Intelligence (cs.AI)
arXiv:2609.08736 [cs.AI]
(or arXiv:2609.08736v1 [cs.AI] for this version)
https://doi.org/10.48550/arXiv.2609.08736 Focus to learn more arXiv-issued DOI via DataCite (pending registration)
arXivLabs is a framework that allows collaborators to develop and share new arXiv features directly on our website.
Both individuals and organizations that work with arXivLabs have embraced and accepted our values of openness, community, excellence, and user data privacy. arXiv is committed to these values and only works with partners that adhere to them.
Have an idea for a project that will add value for arXiv's community? Learn more about arXivLabs.
Full text under the publisher's open licence · 217 words
Related coverage

News
Quantum Computing Report · Jun 7, 2026
Optimal Quantum State Testing Even with Limited Entanglement
arXiv · Sep 25, 2026
Bell inequalities tailored to optimal global randomness certification
arXiv · Jul 22, 2026
Following a Unique Path: A Fast Certifier Applied to Outlier-Robust Pose Registration
arXiv · Sep 4, 2026