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Log-Sobolev inequality holds for some focusing Schrödinger Gibbs measures

Research area:mathematicsapplied-computational-mathematics

What the study found

The authors show that the Gibbs measure for the focusing Schrödinger equation satisfies a log-Sobolev inequality when 2 ≤ p ≤ 4. For p > 4, they do not prove such an inequality; instead, they show a lower bound for the Hessian of the effective potential.

Why the authors say this matters

The authors conclude that, for p > 4, the known convexity-based multiscale techniques for proving log-Sobolev inequalities cannot be applied to this measure. In this setting, a log-Sobolev inequality is a functional inequality used to study the measure's behavior.

What the researchers tested

The study examines the Gibbs measure built by Lebowitz, Rose, and Speer for the focusing Schrödinger equation on the torus, with a cutoff on the L2 norm. The analysis considers the parameter p in the nonlinear term and checks whether the measure satisfies a log-Sobolev inequality.

What worked and what didn't

For 2 ≤ p ≤ 4, the measure does satisfy a log-Sobolev inequality. For p > 4, the authors establish a lower bound for the Hessian of the effective potential, but this does not allow the known convexity-based multiscale methods to be used.

What to keep in mind

The abstract does not describe any limitations beyond the parameter range. It also does not state whether the results extend beyond the specific Gibbs measure, torus setting, and L2 cutoff considered here.

Key points

  • The Gibbs measure for the focusing Schrödinger equation satisfies a log-Sobolev inequality when 2 ≤ p ≤ 4.
  • For p > 4, the authors establish a lower bound for the Hessian of the effective potential.
  • The authors say known convexity-based multiscale techniques cannot be applied when p > 4.
  • The measure studied is the Gibbs measure built by Lebowitz, Rose, and Speer with an L2-norm cutoff on the torus.

Disclosure

Research title:
Log-Sobolev inequality holds for some focusing Schrödinger Gibbs measures
Authors:
Guopeng Li, Jiawei Li, Leonardo Tolomeo
Institutions:
Beijing Institute of Technology, Maxwell Institute for Mathematical Sciences, Maxwell Institute for Mathematical Sciences
Publication date:
2026-04-25
OpenAlex record:
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AI provenance: This post was generated by gpt-5.4-mini (OpenAI). The original authors did not write or review this post.