What was already known
Anari and coauthors had an e^((γ+1)n) deterministic approximation. Ebrahimnejad, Nagda and Oveis Gharan reduced the upper exponent to γ+0.9999 and showed hardness below γ−ε.
AI involvement
In a one-shot exchange without mathematical hints, GPT-5.5 Pro Extended produced the theorem, relaxation and proof for Nima Anari. Farzam Ebrahimnejad independently reached the same result in a guided exchange. Both authors report verifying the mathematics and explain the method; Codex typeset the manuscript.
The first interaction supplied only prior work and the target ratio; no hints or follow-up mathematical input preceded the model’s full result.
Importance
Reports closure of the known approximation-exponent gap for PSD permanents.
Limitations
The algorithm attains γ+ε for any fixed ε>0, not an exact γ exponent at finite n. Optimality is conditional on P≠NP; it concerns PSD permanents, not arbitrary matrices.
Technical verification
Both authors explicitly report verifying the complete theorem and proof. This is a source-reported human check; no machine proof or independent replay is documented.
Formal checking
Both authors verified the mathematics after separate model interactions
- Checker
- Nima Anari and Farzam Ebrahimnejad, paper authors
- Scope
- Theorem 1 PSD permanent sandwich and deterministic approximation
- Basis
- Reported by the linked checker
Checking a formal statement does not establish that it matches the original problem.
Human understanding
The human authors explain the entropy-corrected relaxation through the Gaussian moment, Gibbs variational formula and matching upper/lower bounds.
Correction and publication history
First report of the PSD permanent approximation result.
Read this published revision