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Early channel choice for prepared accepting-pair injection

Lax342547.PreparedInjectionScale · concepts/Lax342547/PreparedInjectionScale.lean · lax-342547

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    Natural Language Statement

    Theorem

    Choose channel size before the ambient dimension, density law, or accepting mass exponent. The later onset pays every fixed polynomial loss; a density exponent at most N/100 and primal dimension at most N/8 suffice.

    Concept map
    118 concepts; 1 descendant hidden
    100%
    Finite accepting-family lossesActual finite accepting-pair injectionActual projected span deficitChannel bounds with adaptive protectedcoefficient spacesSampling after exposed coordinatesAdaptive sampling on disjoint coordinatesetsUnion over adaptive component coversExact-image bounds for independent affinecolumnsFinite amplified-test averagingExact uniform bilinear character meanWalsh operator bounds with explicit bilinearrankRank of a lifted tensor sumIndependent binary channel charactersActual raw frame channel phase tailsJoint channel law and itsindependent-column densityActual channel moments and phase tailsCombined column and row mode exposureRows of diagonal tensor mapsMoment estimates for actual componenttensor phasesComponentwise mode spaces and diagonaltensorsConcrete coordinates, quadratic testers, andthe self-Gram formThe affine minus-column law at a fixed plusframeExact conditioning costs and recovery offinite probability massesEntropy progress for residual pair lawsIndependence of distinct sample positionsJoin-stable classes of mode coversCount tensors killed by exposureActual exposure cover projectionCut profiles and the constant kernelRank of a diagonal familyDyadic span deficit estimatesA small dyadic tail scaleActual dyadic deficit recurrenceEntropy along feasible mixture lines,including new supportFull feasible support and finite informationprojectionExact image pins in nominal coefficientspacesProjection removes the exposed termsCount tested index occurrencesFinite pin-injection exceptionFinite linear images and their uniform-lawdensity boundsFinite even moment expansionFinite independent sampling and vertexexception tailsAmbient symmetries and frame marginalsReciprocal raw-frame orientation and dualpin avoidanceThe symmetric binary gradient formGram-conditioned columns and theirrank-failure probabilityTwo-sided Gram normalization forindividually injective framesGreedy independence of subspacesGreedy mode space exposureSmall actual greedy exposure tailsThe binary hole relationPaying the reference-image conditioning anddimension costsGreedy independent failure samplesAmplified channel failures on independentpinned spacesNo-cover rank growth on arbitrary finiteindicesRelative injection loss for polynomialaccepting massEarly channel choice and injection exponentbudgetsInjection parameter quantifiersAccepting-pair test unions and relative lossUniform injective-matrix densityUniform injective frames and channeltranspose failureActual deficits indexed by a distinct listFinite list tail statisticsFactorization and counting of low-rankbinary matricesDimension deficits after an arbitrary modemapExposed mode dimension budgetExplicit no-cover moment parameter marginsBoolean point moments with restricted basecoordinatesFinite moment probability and rank splitA low rank sum supplies an actual adaptivecoverNo-cover phase momentsRank growth without an adaptive modecoverSquared restriction cost for independent uniteventsFinite exposure partitionsActual phase averages from small exceptionaltailsPrepared-unit injection on actual acceptingpairsEarly channel choice for preparedaccepting-pair injectionUniform primal-span avoidanceExact images mixing independent injectiveframesJoint minus images after exposing severalplus framesProjected nonzero terms in the actualremaining sumPrimal projections and preservation ofeffective spacesDimensions of projected mode spacesExact probabilities for independent protectedchannel imagesWalsh bounds for independent image lawsand separated phasesRank of a tensor killed in two quotientspacesRank loss under two restrictionsRaw accepting-family injection withexponential lossActual frame observations realize thenominal channel contractionsRaw matrix frames and their tensorrealizationThe finite uniform raw-vertex lawActual raw primal-span avoidanceOriginal retained-cell laws from finite PMFsJoint reference image caps across both signsand all drawsThe full reference cap for exact pin eventsFinite relative entropy and support costsRank loss under restriction of a bilinear formImage caps inside original retained cellsFinite sequential testsOne exposure controls both modesCounting all bounded-dimensional protectedspacesNumerical cross tables, injection flags, andunary admissibilityMonotonicity of span deficitsMode space span deficitsNonzero tensor count from span deficitsRank of an actual linear map sumTable contractions on effective profiles andtheir full extensionsActual table injection and ambient pinfailuresTable coordinates and private channelcomplementsMajority intersections in the cyclic taggeometryCharacters of all independent tensor channelsWell-defined channel contractions onprojected tensor spacesCounting component tensors with boundedtotal rankUniform raw channel phase tails overbounded-rank targetsAdmissible pair lawsWhole-unit conditioned pin avoidanceUniversal protected pin witnessesOrthogonality and finite Walsh correlationbounds
    Proven claimDefinitionThis conceptRelated conceptA → B: B builds on A
    Evidence

    Each proof establishes this claim relative to its assumptions.

    Lean source view on GitHub

    1import Lax342547.PreparedInjection
    2
    3/-!
    4---
    5title: Early channel choice for prepared accepting-pair injection
    6type: theorem
    7---
    8Choose channel size before the ambient dimension, density law, or accepting
    9mass exponent. The later onset pays every fixed polynomial loss; a density
    10exponent at most N/100 and primal dimension at most N/8 suffice.
    11-/
    12
    13namespace Lax342547.PreparedInjectionScale
    14
    15open Lax342547.PreparedInjection
    16
    17axiom exists_prepared_parameters (K Cs J : ℕ) (ε : ℝ) (hJ : 0 < J) (hε : 0 < ε) :
    18 ∃ t h : ℕ,∀ c : ℕ,∃ N₀ : ℕ,∀ N : ℕ,N₀ ≤ N → ∀ p m : ℕ,
    19 p ≤ N/8 → m ≤ N/100 →
    20 Budget K m t Cs p h N (ε/(2*J)) ∧
    21 (J : ℝ)*(1/(2 : ℝ)^(N/100)+1/(2 : ℝ)^N) ≤ (ε/2)/(N : ℝ)^c
    22
    23end Lax342547.PreparedInjectionScale
    24
    Show Proof
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