import OAI.Combinatorics.PerfectMatching.Calculus namespace OAI /-! The second-variation inequality at a maximum of the actual entropy defect. -/ noncomputable section open scoped BigOperators Topology open Filter namespace MatchingEntropy variable {V E : Type*} [Fintype V] [Fintype E] [DecidableEq V] [DecidableEq E] def entropyDefect (G : LooplessGraph V E) (m : ℝ) (y : E → ℝ) : ℝ := entropyBarrier m (entropy y)-maxMatchingEntropy G y theorem score_law_eventually_simplex {ι : Type*} [Fintype ι] {p : ι → ℝ} (hp : p ∈ probabilitySimplex ι) (a : ι → ℝ) (hcenter : ∑ i, p i*a i = 0) : ∀ᶠ t : ℝ in 𝓝 0, (fun i => p i+t*(p i*a i)) ∈ probabilitySimplex ι := by have hh : ∀ᶠ t : ℝ in 𝓝 0, ∀ i, 0 < 1+t*a i := by apply Filter.eventually_all.mpr intro i have hc : Continuous (fun t : ℝ => 1+t*a i) := by fun_prop exact (isOpen_lt continuous_const hc).mem_nhds (by norm_num) filter_upwards [hh] with t ht constructor · intro i have h := mul_nonneg (hp.1 i) (ht i).le nlinarith · rw [Finset.sum_add_distrib, ← Finset.mul_sum, hp.2, hcenter] ring omit [Fintype V] [DecidableEq V] in theorem mean_score_support (G : LooplessGraph V E) {p : G.Matching → ℝ} (hp : p ∈ probabilitySimplex G.Matching) (a : G.Matching → ℝ) : ∀ e, G.mean p e = 0 → G.mean (fun M => p M*a M) e = 0 := by intro e he have hterms : ∀ M, p M*G.indicator M e = 0 := by have hh := (Finset.sum_eq_zero_iff_of_nonneg (fun M (_ : M ∈ Finset.univ) => mul_nonneg (hp.1 M) (G.indicator_nonneg M e))).mp he exact fun M => hh M (Finset.mem_univ M) apply Finset.sum_eq_zero intro M _ calc _ = a M*(p M*G.indicator M e) := by ring _ = 0 := by rw [hterms, mul_zero] omit [Fintype V] [DecidableEq V] in /-- A maximum of the *actual* entropy defect satisfies the second-variation inequality. The comparison uses optimizing probability laws rather than a replacement for H. -/ theorem matching_defect_second_variation (G : LooplessGraph V E) {x : E → ℝ} {p : G.Matching → ℝ} (hp : p ∈ G.feasibleLaws x) (hopt : entropy p = maxMatchingEntropy G x) {m : ℝ} (hm : m ≠ 0) (hmax : ∀ y ∈ G.polytope, entropyDefect G m y ≤ entropyDefect G m x) (u : E → ℝ) (c : ℝ) (hcenter : ∑ M, p M*linearScore G u c M = 0) : (8*Real.exp (-entropy x/m)-1)* (∑ e, (G.mean (fun M => p M*linearScore G u c M) e)^2/x e) + (8*Real.exp (-entropy x/m)/m)* (entropyLineD x (G.mean (fun M => p M*linearScore G u c M)) 0)^2 + (∑ M, p M*(linearScore G u c M)^2) ≤ 0 := by let a := linearScore G u c let v := G.mean (fun M => p M*a M) let c₀ := ∑ M, p M*Real.log (p M) let c₁ := ∑ M, (p M*a M)*Real.log (p M) have hsupp : ∀ e, x e = 0 → v e = 0 := by rw [← hp.2] exact mean_score_support G hp.1 a apply entropy_second_variation x v hsupp hp.1 a hcenter hm c₀ c₁ filter_upwards [score_law_eventually_simplex hp.1 a hcenter] with t ht let r := fun M => p M+t*(p M*a M) have hr : r ∈ probabilitySimplex G.Matching := ht have hrmean : G.mean r = fun e => x e+t*v e := by change G.meanLinear (p+t • (fun M => p M*a M)) = _ rw [map_add, map_smul] change G.mean p+t • v = _ rw [hp.2] rfl have hdual := matching_entropy_dual_bound G hp hopt hr (fun M hM => by dsimp [r]; simp [hM]) u c t have hscore : (∑ M, r M*a M) = t*(∑ M, p M*(a M)^2) := by dsimp [r] simp_rw [add_mul, mul_assoc t, Finset.sum_add_distrib, ← Finset.mul_sum] rw [hcenter, zero_add] congr 1 apply Finset.sum_congr rfl intro M _ ring have hlog : (∑ M, r M*Real.log (p M)) = c₀+t*c₁ := by dsimp [r, c₀, c₁] simp_rw [add_mul, mul_assoc t, Finset.sum_add_distrib, ← Finset.mul_sum] change maxMatchingEntropy G (G.mean r) ≤ Real.log (partition p a t) - (∑ M, r M*Real.log (p M)) - t*(∑ M, r M*a M) at hdual rw [hlog, hscore] at hdual have hmaxr := hmax (G.mean r) (G.mean_mem_polytope hr) dsimp only [entropyDefect] at hmaxr have hzero := partition_zero hp.1 a simp only [zero_mul, zero_pow (by omega : 2 ≠ 0), add_zero, hzero, Real.log_one, sub_zero] rw [← hopt] at hmaxr rw [entropy_eq p] at hmaxr rw [← hrmean] dsimp only [c₀] at * nlinarith end MatchingEntropy end end OAI