Log-Concave Sampling · reader guide

Study Guide

The book is the main route. Use the companion when a chapter moves too quickly; use the Proof Atlas only when you want to see the reusable formal structure underneath.

01Read the textbookCanonical chapter order 02Open a companionPrerequisites and hidden steps 03Open the Proof AtlasShared roots before exact Lean

Start here

Chapter 1 builds the common language.

1.1Stochastic calculusBrownian motion · Itô · stopping 1.2Markov semigroupsGenerator · Γ · PI/LSI 1.3Optimal transportCouplings · W₂ · geodesics 1.4Gradient flowDynamics meet Wasserstein geometry

Whole book

Twelve chapters, collapsed by part.

Part 1 · Diffusions in Continuous Time

01The Langevin Diffusion in Continuous Time 02Functional Inequalities 03Additional Topics in Stochastic Analysis

Part 2 · Complexity of Sampling

04Analysis of Langevin Monte Carlo 05Faster Low-Accuracy Samplers 06Convergence in Rényi Divergence 07High-Accuracy Samplers 08The Proximal Sampler 09Lower Bounds for Sampling 10Structured Sampling 11Non-Log-Concave Sampling 12Diffusion Generative Models

Long declaration lists intentionally do not live on this page. They belong in the Underlying Lean Graph of Libraries and Implementation Map.