Download lean/OAI/Probability/InvariantIsing/Pressure/GibbsSecant.lean from roseking/openAI-math-dataset: direct link, hf CLI and curl.
- Browser
- Download file 2.1 kB
-
https://huggingface.co/datasets/roseking/openAI-math-dataset/resolve/main/lean/OAI/Probability/InvariantIsing/Pressure/GibbsSecant.lean
- Command line
-
hf download hf://datasets/roseking/openAI-math-dataset/lean/OAI/Probability/InvariantIsing/Pressure/GibbsSecant.lean
-
curl -L -o GibbsSecant.lean https://huggingface.co/datasets/roseking/openAI-math-dataset/resolve/main/lean/OAI/Probability/InvariantIsing/Pressure/GibbsSecant.lean
2.1 kB
| import OAI.Probability.InvariantIsing.Core.Ward | |
| namespace OAI | |
| /-! Endpoint Gibbs averages bound a finite log-partition difference. These | |
| are the convex secant inequalities used to change spectral multiplicities. -/ | |
| noncomputable section | |
| open IsingPerceptron | |
| open scoped BigOperators | |
| namespace InvariantIsing | |
| lemma log_finitePartition_sub_ge_gibbsAverage | |
| {S : Type*} [Fintype S] {w : S → ℝ} (hw : GibbsReference w) | |
| (H K : S → ℝ) : | |
| gibbsAverage w H (fun s => K s - H s) ≤ | |
| Real.log (finitePartition w K) - Real.log (finitePartition w H) := by | |
| let Y := fun s => K s - H s | |
| have hd := hasDerivAt_log_finitePartition hw (t := (0 : ℝ)) | |
| (fun s => (hasDerivAt_const 0 (H s)).add ((hasDerivAt_id 0).mul_const (Y s))) | |
| have hc := convexOn_log_finitePartition hw H Y | |
| have hs := hc.le_slope_of_hasDerivAt (Set.mem_univ (0 : ℝ)) | |
| (Set.mem_univ (1 : ℝ)) (by norm_num) hd | |
| simpa only [slope_def_field, sub_zero, div_one, zero_add, zero_mul, one_mul, | |
| add_sub_cancel, Y, gibbsAverage, Pi.add_apply, id_eq, add_zero] using hs | |
| lemma log_finitePartition_sub_le_gibbsAverage | |
| {S : Type*} [Fintype S] {w : S → ℝ} (hw : GibbsReference w) | |
| (H K : S → ℝ) : | |
| Real.log (finitePartition w K) - Real.log (finitePartition w H) ≤ | |
| gibbsAverage w K (fun s => K s - H s) := by | |
| have h := log_finitePartition_sub_ge_gibbsAverage hw K H | |
| have he : gibbsAverage w K (fun s => H s - K s) = | |
| -gibbsAverage w K (fun s => K s - H s) := by | |
| simp only [gibbsAverage, mul_sub, Finset.sum_sub_distrib] | |
| ring | |
| rw [he] at h | |
| linarith | |
| lemma log_finitePartition_secant_bounds | |
| {S : Type*} [Fintype S] {w : S → ℝ} (hw : GibbsReference w) | |
| (H K : S → ℝ) : | |
| gibbsAverage w H (fun s => K s - H s) ≤ | |
| Real.log (finitePartition w K) - Real.log (finitePartition w H) ∧ | |
| Real.log (finitePartition w K) - Real.log (finitePartition w H) ≤ | |
| gibbsAverage w K (fun s => K s - H s) := | |
| ⟨log_finitePartition_sub_ge_gibbsAverage hw H K, | |
| log_finitePartition_sub_le_gibbsAverage hw H K⟩ | |
| end InvariantIsing | |
| end | |
| end OAI | |