InformationTheory

InformationTheory.Shannon.EPI.Stam.ToBridge

source

Stam → EPI: Csiszár ratio path-derivative cluster #

This file holds the Csiszár-coupling ratio path-derivative lemmas — the AntitoneOn and continuity chain along the heat-flow path — consumed by the entropy power inequality case-1 closure.

Main statements #

§2'' — Path-derivative of the 1-source gap #

theorem

InformationTheory.Shannon.EPIStamToBridge.entropyPower_hasDerivAt_of_diffEnt_hasDerivAt

source
{f : } {d t : } (h : HasDerivAt f d t) :
HasDerivAt (fun (s : ) => Real.exp (2 * f s)) (Real.exp (2 * f t) * (2 * d)) t

Entropy-power chain-rule helper: if f has derivative d at t, then the entropy-power composition s ↦ Real.exp (2 · f s) has derivative Real.exp (2 · f t) · (2 · d) at t. Used to lift the V2 de Bruijn identity to entropy-power form. @audit:ok

Used by

    §2''' — 1-source Stam reduction g'(t) ≤ 0 #

    theorem

    InformationTheory.Shannon.EPIStamToBridge.csiszar_ratio_deriv_le_zero_arith

    source
    (J_X J_Y J_sum N_X N_Y : ) (hJX : 0 < J_X) (hJY : 0 < J_Y) (hJsum : 0 < J_sum) (hNX : 0 < N_X) (hNY : 0 < N_Y) (h_stam : 1 / J_sum 1 / J_X + 1 / J_Y) :
    J_sum - (N_X * J_X + N_Y * J_Y) / (N_X + N_Y) 0

    Ratio-gap derivative core (pure arithmetic): from the harmonic Stam inequality 1 / J_sum ≥ 1 / J_X + 1 / J_Y and positivity of the entropy powers N_X, N_Y, the log-ratio gap derivative J_sum − (N_X · J_X + N_Y · J_Y) / (N_X + N_Y) is ≤ 0, equivalently J_sum · (N_X + N_Y) ≤ N_X · J_X + N_Y · J_Y.

    This is the factor-1 inequality (coefficient 1 on J_sum), a true real-arithmetic inequality over the free variables. The analogous factor-2 statement is false from harmonic Stam and positivity alone; the factor mismatch for the 𝒩(0, 2) sum coupling lives in the de Bruijn lift, not in this lemma. @audit:ok

    Used by
      theorem

      InformationTheory.Shannon.EPIStamToBridge.csiszarLogRatioGap_hasDerivAt

      source
      {Ω : Type u_1} { : MeasurableSpace Ω} (X Y Z_X Z_Y : Ω) (P : MeasureTheory.Measure Ω) [MeasureTheory.IsProbabilityMeasure P] (hX : Measurable X) (hZX : Measurable Z_X) (hXZX : ProbabilityTheory.IndepFun X Z_X P) (hY : Measurable Y) (hZY : Measurable Z_Y) (hYZY : ProbabilityTheory.IndepFun Y Z_Y P) (hXYZXY : ProbabilityTheory.IndepFun (fun (ω : Ω) => X ω + Y ω) (fun (ω : Ω) => Z_X ω + Z_Y ω) P) (h_reg_sum : StamEPIBridge.IsDeBruijnRegularityHyp (fun (ω : Ω) => X ω + Y ω) (fun (ω : Ω) => Z_X ω + Z_Y ω) P) (h_reg_X : StamEPIBridge.IsDeBruijnRegularityHyp X Z_X P) (h_reg_Y : StamEPIBridge.IsDeBruijnRegularityHyp Y Z_Y P) {t : } (ht : 0 < t) :

      The log-ratio gap derivative: csiszarLogRatioGap X Y Z_X Z_Y P has derivative J_sum − (N_X · J_X + N_Y · J_Y) / (N_X + N_Y) at any t > 0, where N_i = entropyPower (P.map path_i t) and J_i = fisherInfoOfDensityReal ((h_reg_i.reg_at t ht).density_t). Built from the three per-term entropy-power derivatives (via the de Bruijn V2 identity and the chain-rule helper), HasDerivAt.log for the two log terms, composed by HasDerivAt.sub. The h_reg_* are regularity preconditions.

      The conclusion is the factor-1 derivative, correct under the stated hypotheses: h_reg_sum's Z_law field asserts P.map (Z_X + Z_Y) = gaussianReal 0 1. For the sum coupling with independent unit-variance noises the sum law is gaussianReal 0 2, so h_reg_sum is then uninhabitable; the sum line is instead closed by the two-time route, where X and Y are perturbed with separate unit-variance noises and the variance-2 view never arises. @audit:ok

      Used by
        theorem

        InformationTheory.Shannon.EPIStamToBridge.csiszarLogRatioGap_deriv_le_zero

        source
        {Ω : Type u_1} { : MeasurableSpace Ω} (X Y Z_X Z_Y : Ω) (P : MeasureTheory.Measure Ω) [MeasureTheory.IsProbabilityMeasure P] (h_reg_sum : StamEPIBridge.IsDeBruijnRegularityHyp (fun (ω : Ω) => X ω + Y ω) (fun (ω : Ω) => Z_X ω + Z_Y ω) P) (h_reg_X : StamEPIBridge.IsDeBruijnRegularityHyp X Z_X P) (h_reg_Y : StamEPIBridge.IsDeBruijnRegularityHyp Y Z_Y P) {t : } (ht : 0 < t) (hJX_pos : 0 < FisherInfo.fisherInfoOfDensityReal (h_reg_X.reg_at t ht).density_t) (hJY_pos : 0 < FisherInfo.fisherInfoOfDensityReal (h_reg_Y.reg_at t ht).density_t) (hJsum_pos : 0 < FisherInfo.fisherInfoOfDensityReal (h_reg_sum.reg_at t ht).density_t) (h_stam : StamEPIBridge.IsStamInequalityHyp (fun (ω : Ω) => X ω + t * Z_X ω) (fun (ω : Ω) => Y ω + t * Z_Y ω) P) (h_regdens_X : FisherInfo.IsRegularDensityV2 (h_reg_X.reg_at t ht).density_t) (h_regdens_Y : FisherInfo.IsRegularDensityV2 (h_reg_Y.reg_at t ht).density_t) (h_norm_X : (x : ), (h_reg_X.reg_at t ht).density_t x = 1) (h_norm_Y : (x : ), (h_reg_Y.reg_at t ht).density_t x = 1) (h_conv_id : ∀ (x : ), (h_reg_sum.reg_at t ht).density_t x = EPIConvDensity.convDensityAdd (h_reg_X.reg_at t ht).density_t (h_reg_Y.reg_at t ht).density_t x) (h_blachman : EPIBlachmanDensity.IsBlachmanConvReady (h_reg_X.reg_at t ht).density_t (h_reg_Y.reg_at t ht).density_t) :

        The log-ratio gap derivative is ≤ 0 from the 1-source Stam inequality: the value J_sum − (N_X · J_X + N_Y · J_Y) / (N_X + N_Y) ≤ 0 follows from the harmonic Stam inequality 1 / J_sum ≥ 1 / J_X + 1 / J_Y (extracted from h_stam) plus positivity, via the arithmetic core csiszar_ratio_deriv_le_zero_arith. Unlike the difference-gap form, the ratio form is closable from harmonic Stam (weights α = N_X / (N_X + N_Y), β = N_Y / (N_X + N_Y), with α² ≤ α).

        The harmonic Stam inequality is extracted by applying the producer h_stam at the three path densities, using the Fisher identifications J_i = (fisherInfoOfMeasureV2 (P.map _) f_i).toReal (rfl, since fisherInfoOfMeasureV2 ignores its measure argument) together with the caller-supplied regularity preconditions (IsRegularDensityV2, the normalizations, the pointwise convolution identification, and the IsBlachmanConvReady bundle). The inequality core is supplied by the producer h_stam, not by those preconditions. @audit:ok

        Used by
          theorem

          InformationTheory.Shannon.EPIStamToBridge.epi_of_csiszarLogRatioGap_zero_nonneg

          source

          Entropy power inequality recovery from nonnegativity of the gap at t = 0. The log-ratio gap at t = 0 is log (eP(X + Y)) − log (eP X + eP Y); since both arguments are strictly positive, 0 ≤ r(0) is equivalent to eP X + eP Y ≤ eP(X + Y) by Real.log_le_log_iff.

          Used by
            theorem

            InformationTheory.Shannon.EPIStamToBridge.csiszarLogRatioGap_differentiableOn_interior

            source
            {Ω : Type u_1} { : MeasurableSpace Ω} (X Y Z_X Z_Y : Ω) (P : MeasureTheory.Measure Ω) [MeasureTheory.IsProbabilityMeasure P] (hX : Measurable X) (hZX : Measurable Z_X) (hXZX : ProbabilityTheory.IndepFun X Z_X P) (hY : Measurable Y) (hZY : Measurable Z_Y) (hYZY : ProbabilityTheory.IndepFun Y Z_Y P) (hXYZXY : ProbabilityTheory.IndepFun (fun (ω : Ω) => X ω + Y ω) (fun (ω : Ω) => Z_X ω + Z_Y ω) P) (h_reg_sum : StamEPIBridge.IsDeBruijnRegularityHyp (fun (ω : Ω) => X ω + Y ω) (fun (ω : Ω) => Z_X ω + Z_Y ω) P) (h_reg_X : StamEPIBridge.IsDeBruijnRegularityHyp X Z_X P) (h_reg_Y : StamEPIBridge.IsDeBruijnRegularityHyp Y Z_Y P) :

            csiszarLogRatioGap X Y Z_X Z_Y P is differentiable on the interior Set.Ioi 0 = interior (Set.Ici 0), via csiszarLogRatioGap_hasDerivAt and HasDerivAt.differentiableAt. @audit:ok

            Used by
              theorem

              InformationTheory.Shannon.EPIStamToBridge.csiszarLogRatioGap_continuousWithinAt_zero

              source
              {Ω : Type u_1} { : MeasurableSpace Ω} (X Y Z_X Z_Y : Ω) (P : MeasureTheory.Measure Ω) [MeasureTheory.IsProbabilityMeasure P] (h_endpt_sum : IsHeatFlowEndpointRegular (fun (ω : Ω) => X ω + Y ω) (fun (ω : Ω) => Z_X ω + Z_Y ω) P) (h_endpt_X : IsHeatFlowEndpointRegular X Z_X P) (h_endpt_Y : IsHeatFlowEndpointRegular Y Z_Y P) :

              Endpoint continuity ContinuousWithinAt (Set.Ioi 0) 0 of csiszarLogRatioGap X Y Z_X Z_Y P, by composing the endpoint continuity of the three heat-flow entropy powers (heatFlowEntropyPower_continuousWithinAt_zero) through ContinuousWithinAt.log / .add / .sub (with entropyPower_pos / add_pos discharging the ≠ 0 premises). The interior t > 0 continuity is supplied separately by csiszarLogRatioGap_differentiableOn_interior.

              Used by
                theorem

                InformationTheory.Shannon.EPIStamToBridge.csiszarLogRatioGap_antitoneOn_Ici_zero

                source
                {Ω : Type u_1} { : MeasurableSpace Ω} (X Y Z_X Z_Y : Ω) (P : MeasureTheory.Measure Ω) [MeasureTheory.IsProbabilityMeasure P] (hX : Measurable X) (hZX : Measurable Z_X) (hXZX : ProbabilityTheory.IndepFun X Z_X P) (hY : Measurable Y) (hZY : Measurable Z_Y) (hYZY : ProbabilityTheory.IndepFun Y Z_Y P) (hXYZXY : ProbabilityTheory.IndepFun (fun (ω : Ω) => X ω + Y ω) (fun (ω : Ω) => Z_X ω + Z_Y ω) P) (h_reg_sum : StamEPIBridge.IsDeBruijnRegularityHyp (fun (ω : Ω) => X ω + Y ω) (fun (ω : Ω) => Z_X ω + Z_Y ω) P) (h_reg_X : StamEPIBridge.IsDeBruijnRegularityHyp X Z_X P) (h_reg_Y : StamEPIBridge.IsDeBruijnRegularityHyp Y Z_Y P) (h_endpt_sum : IsHeatFlowEndpointRegular (fun (ω : Ω) => X ω + Y ω) (fun (ω : Ω) => Z_X ω + Z_Y ω) P) (h_endpt_X : IsHeatFlowEndpointRegular X Z_X P) (h_endpt_Y : IsHeatFlowEndpointRegular Y Z_Y P) (h_pos_stam : ∀ (t : ) (ht : 0 < t), 0 < FisherInfo.fisherInfoOfDensityReal (h_reg_X.reg_at t ht).density_t 0 < FisherInfo.fisherInfoOfDensityReal (h_reg_Y.reg_at t ht).density_t 0 < FisherInfo.fisherInfoOfDensityReal (h_reg_sum.reg_at t ht).density_t StamEPIBridge.IsStamInequalityHyp (fun (ω : Ω) => X ω + t * Z_X ω) (fun (ω : Ω) => Y ω + t * Z_Y ω) P FisherInfo.IsRegularDensityV2 (h_reg_X.reg_at t ht).density_t FisherInfo.IsRegularDensityV2 (h_reg_Y.reg_at t ht).density_t (x : ), (h_reg_X.reg_at t ht).density_t x = 1 (x : ), (h_reg_Y.reg_at t ht).density_t x = 1 (∀ (x : ), (h_reg_sum.reg_at t ht).density_t x = EPIConvDensity.convDensityAdd (h_reg_X.reg_at t ht).density_t (h_reg_Y.reg_at t ht).density_t x) EPIBlachmanDensity.IsBlachmanConvReady (h_reg_X.reg_at t ht).density_t (h_reg_Y.reg_at t ht).density_t) :
                AntitoneOn (fun (t : ) => EPIL3Integration.csiszarLogRatioGap X Y Z_X Z_Y P t) (Set.Ici 0)

                The log-ratio gap csiszarLogRatioGap X Y Z_X Z_Y P is antitone on the heat-flow ray Set.Ici 0. Applies antitoneOn_of_deriv_nonpos on the convex domain, with interior differentiability (csiszarLogRatioGap_differentiableOn_interior), endpoint continuity (csiszarLogRatioGap_continuousWithinAt_zero), and the per-t deriv ≤ 0 from csiszarLogRatioGap_hasDerivAt and csiszarLogRatioGap_deriv_le_zero.

                Used by

                  §5 — Predicate manipulation: symmetry, congruence, pass-through #

                  §6 — Chain forms (3-arg / 4-arg) via scaling decomposition #

                  §7 — Round-trip / sanity-check theorems #