Section 12 pasting: over all outcomes — error terms and eligible mass #
Eligible-mass bounds and mass identities that feed the final lem:over-all-outcomes
comparison: the counterpart of
MIPRE/Background/LIDT/MIPStarRE/LDT/Pasting/ComparisonLemmas/OverAllOutcomes/ErrorAndMass.lean
in the port of planning/c6b-plan.md (milestone M11, section "Port conventions").
A strategy is a SymStrat params.next 𝔓 K and a slice family an IdxPolyFamily params 𝔓; the
masses are strategy.state.subMeasMass of left-placed submeasurements,
(A.liftLeft strategy.state). The bound of an eligible mass by 1 (vendored through
strategy.isNormalized) uses S.ev_one_of_isNormalized, which takes no hypothesis. The two mass
identities are S.ev_leftTensor_averageOperatorOverDistribution up to definitional unfolding,
and the vanishing masses below d + 1 coordinates and the monotonicity of the restricted mass are
S.ev_zero and S.leftTensor_mono, where the vendored proofs unfold leftTensor and ev.
The error arithmetic (oneThirtySecondErrorSum_nonneg and the two comparisons with
overAllOutcomesError) and the interpolation-uniqueness step are classical: this file imports the
vendored file and names them through an explicit open MIPStarRE.LDT.Pasting (…) list.
Not ported #
dnoteq_term_le_overAllOutcomesError: classical, imported.hBConsistencyError_add_mdq_add_dnoteq_le_overAllOutcomesError: classical, imported.tupleInterpolatedVerticalLine_eq_of_no_supported_mismatch: classical, imported.
References #
In LionSR/MIPStarRE at commit 507e8122, not in this repository:
references/ldt-paper/ld-pasting.texblueprint/src/chapter/ch09_pasting.tex
Upstream keeps this helper private since its Lean-module port (LionSR/MIPStarRE at 5fc363b); the port carries its own copy, with upstream's proof.
The common nonnegative error-sum with exponent 1/32 used by the
Section 12 displayed error terms.
If k < d+1, the interpolation-eligible sandwich total vanishes.
Eligible interpolation mass is nonnegative for every point tuple.
Eligible interpolation mass is at most one for every point tuple.
Distinct tuple averaging is bounded by uniform averaging plus ldDnoteq.
Uniform tuple averaging is bounded by distinct averaging plus ldDnoteq.
The pasted mass is the distinct average of globally consistent eligible mass.
The expansion mass is the uniform average of eligible interpolation mass.
If there are not enough coordinates to interpolate, both sides of the reverse mass comparison are zero.
The pasted-minus-expansion mass loss is bounded by the distinctness error.