Pith Number

pith:OP6HBND7

pith:2026:OP6HBND7GN2D7JZCIQSVHDJMG4

not attested not anchored not stored refs resolved

Speeding Up Nonsmooth Bayesian MCMC Sampling via Inexact Proximal Unadjusted Langevin Algorithm

Alireza Kabgani, Masoud Ahookhosh, Susan Ghaderi, Yves Moreau

An inexact proximal unadjusted Langevin algorithm samples from nonsmooth composite posteriors by using controlled approximations in place of exact proximal operators.

arxiv:2605.17306 v1 · 2026-05-17 · math.OC

Open paper page JSON Open Graph Bundle Merged state Verified badge What is a Pith Number?

Add to your LaTeX paper

\usepackage{pith}
\pithnumber{OP6HBND7GN2D7JZCIQSVHDJMG4}

Prints a linked badge after your title and injects PDF metadata. Compiles on arXiv. Learn more · Embed verified badge

Record completeness

1 Bitcoin timestamp

2 Internet Archive

3 Author claim open · sign in to claim

4 Citations open

5 Replications open

✓ Portable graph bundle live · download bundle · merged state

The bundle contains the canonical record plus signed events. A mirror can host it anywhere and recompute the same current state with the deterministic merge algorithm.

Claims

C1strongest claim

We establish non-asymptotic convergence guarantees for iPULA, explicitly characterizing the impact of inexactness on the sampling error and showing that the inexactness preserves convergence rates up to a quantifiable bias.

C2weakest assumption

The analysis assumes that the inexact proximal computations can be performed with sufficient accuracy control so that the bias remains quantifiable and does not destroy the convergence rate; this enters when the Moreau envelope smoothing is combined with the inexact gradient evaluations.

C3one line summary

iPULA replaces exact proximal steps with inexact approximations in unadjusted Langevin sampling and proves non-asymptotic convergence that holds up to a quantifiable bias from the inexactness.

References

35 extracted · 35 resolved · 0 Pith anchors

[1] M. Ahookhosh, A. Themelis, and P. Patrinos. A Bregman forward-backward linesearch algo- rithm for nonconvex composite optimization: Superlinear convergence to nonisolated local min- ima.SIAM Journal o 2021

[2] H. H. Bauschke and P. L. Combettes.Convex Analysis and Monotone Operator Theory in Hilbert Spaces. Springer, 2nd edition, 2017 2017

[3] S. Brooks, A. Gelman, G. Jones, and X.-L. Meng, editors.Handbook of Markov Chain Monte Carlo. Chapman and Hall/CRC, 2011 2011

[4] N. S. Chatterji, J. Diakonikolas, M. I. Jordan, and P. L. Bartlett. Langevin Monte Carlo without smoothness. InProceedings of the Twenty Third International Conference on Artificial Intelligence and S 2020

[5] F. R. Crucinio, A. Durmus, P. Jim´ enez, and G. O. Roberts. Optimal scaling results for Moreau- Yosida Metropolis-adjusted Langevin algorithms.Bernoulli, 31(3):1889–1907, 2025 1907

Formal links

2 machine-checked theorem links

Receipt and verification

First computed	2026-05-20T00:03:51.315550Z
Builder	pith-number-builder-2026-05-17-v1
Signature	Pith Ed25519 (`pith-v1-2026-05`) · public key
Schema	pith-number/v1.0

Canonical hash

73fc70b47f33743fa7224425538d2c3723143591e00803f7ad0d891e292b1243

Aliases

arxiv: 2605.17306 · arxiv_version: 2605.17306v1 · doi: 10.48550/arxiv.2605.17306 · pith_short_12: OP6HBND7GN2D · pith_short_16: OP6HBND7GN2D7JZC · pith_short_8: OP6HBND7

Agent API

Resolver JSON Graph JSON Events JSON Schema Signing key

Verify this Pith Number yourself

curl -sH 'Accept: application/ld+json' https://pith.science/pith/OP6HBND7GN2D7JZCIQSVHDJMG4 \
  | jq -c '.canonical_record' \
  | python3 -c "import sys,json,hashlib; b=json.dumps(json.loads(sys.stdin.read()), sort_keys=True, separators=(',',':'), ensure_ascii=False).encode(); print(hashlib.sha256(b).hexdigest())"
# expect: 73fc70b47f33743fa7224425538d2c3723143591e00803f7ad0d891e292b1243

Canonical record JSON

{
  "metadata": {
    "abstract_canon_sha256": "75a6606b579c72620a939ba1808777ce3296794cc194e8ce664f09a5723bbca8",
    "cross_cats_sorted": [],
    "license": "http://arxiv.org/licenses/nonexclusive-distrib/1.0/",
    "primary_cat": "math.OC",
    "submitted_at": "2026-05-17T07:48:28Z",
    "title_canon_sha256": "ee6e1e8ae7f632e3f6dfaaa8b6212f872cd10a871694b7e8e855c021ba749395"
  },
  "schema_version": "1.0",
  "source": {
    "id": "2605.17306",
    "kind": "arxiv",
    "version": 1
  }
}