FORS-implemented proximal sampler
A paper-first mathematical case study: read the theorem, the derivation route, and the hidden prerequisites before opening formal infrastructure.
Exact source theorem
Theorem G.1(3), specialized to s=1
Reading. Read the accuracy guarantee together with the complexity/rate: the source assumptions and its notion of oracle cost are part of the result.
Primary-source audit complete. The displayed theorem statement is the controlling mathematical contract for this case.
proof omitted; inherited proximal/RGO error tracking
The primary source explicitly omits this proof or inherits it from an earlier argument. ASTIS preserves that omission instead of inventing missing source steps.
What must be true before the rate can be read
Model and geometry
- The target is log-concave with the source's smoothness hypothesis on the potential or score.
- The initialization scale, typically expressed through a Wasserstein or divergence quantity, is part of the bound.
- Implemented proximal results additionally require the restricted-Gaussian/proximal oracle promised by the source theorem.
Analytic / proof prerequisites
- same shared prerequisites as the weak-smooth G.1(3) case
- smooth specialization s=1
ASTIS rigorous LaTeX
Lean formalization
This fold is intentionally quiet while the source statement, proof route, and assumptions are being completed case by case. A source-facing Lean theorem will appear here only after it compiles and its statement has been matched to the audited source.
References and provenance
- Chen, Chewi, Daskalakis, Rakhlin, High-accuracy sampling for diffusion models and log-concave distributions — Theorem G.1(3), specialized to s=1
- Chen et al. (2026), Theorem G.1(3)
ASTIS-SW-SETTING-LOG-CONCAVE-SMOOTH-UPPER-FORS-IMPLEMENTED-PROXIMAL-SAMPLER · source snapshot b280bbe6689edc53