InformationTheory.Shannon.EPI.Stam.ToBridge
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 #
entropyPower_hasDerivAt_of_diffEnt_hasDerivAt— entropy-power chain-rule helper.csiszarLogRatioGap_hasDerivAt— the log-ratio gap path-derivative.csiszarLogRatioGap_deriv_le_zero— the derivative is≤ 0from the 1-source Stam inequality.epi_of_csiszarLogRatioGap_zero_nonneg— recovery of the entropy power inequality from nonnegativity of the gap att = 0.csiszarLogRatioGap_antitoneOn_Ici_zero— antitonicity of the gap on the heat-flow ray.
§2'' — Path-derivative of the 1-source gap #
InformationTheory.Shannon.EPIStamToBridge.entropyPower_hasDerivAt_of_diffEnt_hasDerivAt
sourceEntropy-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.
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Used by
§2''' — 1-source Stam reduction g'(t) ≤ 0 #
InformationTheory.Shannon.EPIStamToBridge.csiszar_ratio_deriv_le_zero_arith
sourceRatio-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.
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Used by
InformationTheory.Shannon.EPIStamToBridge.csiszarLogRatioGap_hasDerivAt
sourceThe 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.
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Used by
InformationTheory.Shannon.EPIStamToBridge.csiszarLogRatioGap_deriv_le_zero
sourceThe 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.
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Used by
InformationTheory.Shannon.EPIStamToBridge.epi_of_csiszarLogRatioGap_zero_nonneg
sourceEntropy 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
InformationTheory.Shannon.EPIStamToBridge.csiszarLogRatioGap_differentiableOn_interior
sourcecsiszarLogRatioGap 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.
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Used by
InformationTheory.Shannon.EPIStamToBridge.csiszarLogRatioGap_continuousWithinAt_zero
sourceEndpoint 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
InformationTheory.Shannon.EPIStamToBridge.csiszarLogRatioGap_antitoneOn_Ici_zero
sourceThe 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.