Pointwise Lower Bounds for Solutions of Semilinear Elliptic Equations and Applications
Bibliographic record
Abstract
Abstract We consider the semilinear elliptic problem −Δu = f (x, u), posed in a smooth bounded domain Ω of ℝ N with Dirichiel data u|∂Ω = 0, where f : Ω × [0, α f ) → ℝ + (0 < α f ≤ +∞) is a function of appropriate regularity which blows up at α f . We give pointwise lower bounds for the supersolutions under some appropriate conditions on f , and apply them to eigenvalue problem −Δu = λ f (x, u), by giving upper and lower bounds for the extremal parameter λ∗ and the extremal solution u∗. To demonstrate the sharpness of our results, we consider the eigenvalue problem −Δu = λ f (u p ) (p ≥ 1) with Dirichlet boundary condition, and show that for every increasing, convex and superlinear C 2 function f: ℝ + →ℝ + with , where ψΩ is the maximum of the torsion function of Ω. Also, we consider the eigenvalue problem −Δu = λρ(x) f (u), where f is either a regular singularity such as f (u) = e u , or a singular one such as and give explicit estimates on λ∗ and u∗, that improve and extend several results in the literature, by Payne[17], Sperb [21], Brezis-Vasquez [3], Guo-Pan-Ward [11], Ghoussoub-Guo [10], Cowan-Ghoussoub [6], and others.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.007 | 0.032 |
| Meta-epidemiology (narrow) | 0.004 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.005 | 0.001 |
| Science and technology studies | 0.001 | 0.004 |
| Scholarly communication | 0.004 | 0.006 |
| Open science | 0.003 | 0.007 |
| Research integrity | 0.003 | 0.009 |
| Insufficient payload (model declined to judge) | 0.009 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".