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Log-Sobolev inequality holds for some Gibbs measures

Research area:Mathematical analysisApplied MathematicsMathematical Physics

What the study found

The authors show that the Gibbs measures for the focusing Schrödinger equation satisfy a log-Sobolev inequality when 2 ≤ p ≤ 4. For p > 4, they establish a lower bound for the Hessian of the effective potential.

Why the authors say this matters

The study suggests that the log-Sobolev inequality can be proved for this class of measures in the range 2 ≤ p ≤ 4. The findings indicate that, when p > 4, known convexity-based multiscale techniques for proving log-Sobolev inequalities cannot be applied to this measure.

What the researchers tested

The researchers examined the Gibbs measure of the focusing Schrödinger equation built by Lebowitz, Rose, and Speer in 1988. This measure is formally defined using the Schrödinger field u, the exponent p, and a cutoff — an indicator restricting the L2 norm of u to be at most K.

What worked and what didn't

For 2 ≤ p ≤ 4, the measure was shown to satisfy a log-Sobolev inequality. For p > 4, the authors obtained a lower bound on the Hessian of the effective potential, and this was enough to show that the known convexity-based multiscale approach does not apply to ρ.

What to keep in mind

The abstract does not describe additional limitations beyond the p-range distinction. It also does not state whether other methods might work for p > 4.

Key points

  • The paper proves a log-Sobolev inequality for the Gibbs measure when 2 ≤ p ≤ 4.
  • For p > 4, the authors derive a lower bound for the Hessian of the effective potential.
  • That lower bound shows known convexity-based multiscale methods cannot be used for this measure in the p > 4 case.
  • The measure studied is the Gibbs measure for the focusing Schrödinger equation with an L2 cutoff.

Disclosure

Research title:
Log-Sobolev inequality holds for some Gibbs measures
Authors:
Guopeng Li, Jiawei Li, Leonardo Tolomeo
Institutions:
Beijing Institute of Technology, Maxwell Institute for Mathematical Sciences
Publication date:
2026-04-25
OpenAlex record:
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AI provenance: This post was generated by OpenAI. The original authors did not write or review this post.