Comparator-backed upstream endpointOpenAI Math result 139
Subpolynomial query complexity for log-concave sampling
The exact value-and-gradient oracle model, deterministic query cap, TV 1/10 target, upper bound, and independent logarithmic lower bound must remain separate from runtime complexity.
Samplinglib placement
- Optimisation: Chapter 4, Chapter 8 — The exact oracle and proximal interfaces align with lower-bound and proximal-method chapters without becoming optimisation theorems.
Pinned upstream paths and provenance
Apache-2.0 upstream snapshot adc7f1241b42e322a6451854ab7e4b4c146bf78a. Presence here is provenance, not local compilation.
Upstream Lean endpoints (external; not callable ASTIS dependencies)
OAI.LogConcaveSampling.exact_source_main
Comparator-backed upstream endpointOpenAI Math result 087
Mahler conjectures and equality cases
Convex-body polarity and equality classifications are external source routes; they do not establish a sampling complexity bound.
Samplinglib placement
- Optimisation: Chapter 1, Chapter 9 — Convexity and polarity connect to convex foundations and duality, with no algorithmic convergence claim.
Pinned upstream paths and provenance
Apache-2.0 upstream snapshot adc7f1241b42e322a6451854ab7e4b4c146bf78a. Presence here is provenance, not local compilation.
Upstream Lean endpoints (external; not callable ASTIS dependencies)
OAI.SymmetricMahler.symmetric_mahlerOAI.SymmetricMahler.symmetric_mahler_equality
Comparator-backed, partial familyOpenAI Math result 088
Projection-body inequalities and simplex counterexamples
The comparator endpoints cover named projection-volume claims, not every manuscript statement in the family.
Samplinglib placement
- Optimisation: Extended E1: Convex geometry, vector balancing and fixed points — The family belongs in the convex-geometry extension rather than an algorithm chapter.
Pinned upstream paths and provenance
Apache-2.0 upstream snapshot adc7f1241b42e322a6451854ab7e4b4c146bf78a. Presence here is provenance, not local compilation.
Upstream Lean endpoints (external; not callable ASTIS dependencies)
OAI.PettyProjection.petty_projection_volumeOAI.Paper092.product_counterexample
Comparator-backed, partial familyOpenAI Math result 091
Logarithmic Brunn-Minkowski and the B-conjecture
Keep the precise symmetry, measure and convex-body hypotheses; the family name is not a generic log-concavity theorem.
Samplinglib placement
- Optimisation: Chapter 1 — This supplies convex-geometric examples, not an optimisation method.
Pinned upstream paths and provenance
Apache-2.0 upstream snapshot adc7f1241b42e322a6451854ab7e4b4c146bf78a. Presence here is provenance, not local compilation.
Upstream Lean endpoints (external; not callable ASTIS dependencies)
OAI.LogBrunnMinkowski.main
Comparator-backed, partial familyOpenAI Math result 092
Convex-body covering density
Only the exact comparator endpoint is verification evidence; the full covering-density family has a wider manuscript scope.
Samplinglib placement
- Optimisation: Extended E1: Convex geometry, vector balancing and fixed points — Covering and volumetric techniques belong in the convex-geometry extension.
Pinned upstream paths and provenance
Apache-2.0 upstream snapshot adc7f1241b42e322a6451854ab7e4b4c146bf78a. Presence here is provenance, not local compilation.
Upstream Lean endpoints (external; not callable ASTIS dependencies)
OAI.SingleLatticeCovering.single_lattice_covering
Comparator-backed upstream endpointOpenAI Math result 097
Euclidean Steinitz-Bergstrom theorem
Vector balancing is relevant proof technology but is not a first-order optimisation convergence theorem.
Samplinglib placement
- Optimisation: Chapter 6, Extended E1: Convex geometry, vector balancing and fixed points — Vector balancing is best presented beside nonsmooth convex geometry as an explicit extension.
Pinned upstream paths and provenance
Apache-2.0 upstream snapshot adc7f1241b42e322a6451854ab7e4b4c146bf78a. Presence here is provenance, not local compilation.
Upstream Lean endpoints (external; not callable ASTIS dependencies)
OAI.EuclideanSteinitzBergstrom.main
Comparator-backed, partial familyOpenAI Math result 140
Memory-sample lower bounds for noiseless Gaussian regression
Posterior conditioning, projection moments, subsphere control and final memory-sample lower bounds form an AND-route; none should be displayed as the whole proof alone.
Samplinglib placement
- Optimisation: Chapter 4 — The information model is relevant to lower-bound methodology but is not Chewi's smooth-optimisation oracle theorem.
Pinned upstream paths and provenance
Apache-2.0 upstream snapshot adc7f1241b42e322a6451854ab7e4b4c146bf78a. Presence here is provenance, not local compilation.
Upstream Lean endpoints (external; not callable ASTIS dependencies)
OAI.MemoryPrecision.mainOAI.PosteriorReplicas.source_mainOAI.ProjectionMoments.mainOAI.SubsphereCurrent.full_current_main_scope
Comparator-backed upstream endpointOpenAI Math result 328
Fixed points of nonexpansive maps in reflexive Banach spaces
The fixed-point theorem is reusable analysis, not convergence of a named optimisation algorithm without a verified iteration adapter.
Samplinglib placement
- Optimisation: Chapter 2, Chapter 11, Extended E1: Convex geometry, vector balancing and fixed points — Gradient-flow and alternating/fixed-point viewpoints are the closest teaching locations.
Pinned upstream paths and provenance
Apache-2.0 upstream snapshot adc7f1241b42e322a6451854ab7e4b4c146bf78a. Presence here is provenance, not local compilation.
Upstream Lean endpoints (external; not callable ASTIS dependencies)
OAI.ReflexiveFixedPoints.exists_fixedPoint_of_canonicallyReflexive