Pith Number

pith:Z23LILDF

pith:2026:Z23LILDF5U5HBO4NVL424HDENB

not attested not anchored not stored refs resolved

Stochastic Optimization and Data Science

Alexander Gasnikov, Arutyun Avetisyan, Darina Dvinskikh, Denis Turdakov, Nazarii Tupitsa, Vladimir Temlyakov

Stochastic optimization problems arise when maximizing log-likelihood or minimizing population risk in statistical estimation and learning.

arxiv:2605.16875 v1 · 2026-05-16 · math.OC

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\usepackage{pith}
\pithnumber{Z23LILDF5U5HBO4NVL424HDENB}

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2 Internet Archive

3 Author claim open · sign in to claim

4 Citations open

5 Replications open

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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

Stochastic optimization problems can be motivated from a statistical perspective and a statistical learning perspective, where the goal is to maximize the log-likelihood or minimize the population risk, using offline (Monte Carlo / Sample Average Approximation) and online (Stochastic Approximation) approaches.

C2weakest assumption

The assumption that the two described approaches (offline Monte Carlo/SAA and online SA) are the primary or sufficient ways to solve the expectation minimization problems arising in statistical settings, without needing additional context or comparisons.

C3one line summary

The paper motivates stochastic optimization problems from statistical perspectives and describes offline and online approaches to solve expectation minimization problems.

References

162 extracted · 162 resolved · 5 Pith anchors

[1] Journal of machine learning research , volume=

[2] Mathematical Programming , volume= 2025

[3] Optimization Methods and Software , volume= 2024

[4] The Twelfth International Conference on Learning Representations , year=

[5] Robust Stochastic Approximation Approach to Stochastic Programming 2009 · doi:10.1137/070704277

Formal links

2 machine-checked theorem links

Receipt and verification

First computed	2026-05-20T00:03:27.693041Z
Builder	pith-number-builder-2026-05-17-v1
Signature	Pith Ed25519 (`pith-v1-2026-05`) · public key
Schema	pith-number/v1.0

Canonical hash

ceb6b42c65ed3a70bb8daaf9ae1c64684aa770497ebbd16da98b908baf7f8b3f

Aliases

arxiv: 2605.16875 · arxiv_version: 2605.16875v1 · doi: 10.48550/arxiv.2605.16875 · pith_short_12: Z23LILDF5U5H · pith_short_16: Z23LILDF5U5HBO4N · pith_short_8: Z23LILDF

Agent API

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Verify this Pith Number yourself

curl -sH 'Accept: application/ld+json' https://pith.science/pith/Z23LILDF5U5HBO4NVL424HDENB \
  | 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: ceb6b42c65ed3a70bb8daaf9ae1c64684aa770497ebbd16da98b908baf7f8b3f

Canonical record JSON

{
  "metadata": {
    "abstract_canon_sha256": "56d9e3118eed16e76690e7aa531fecc3c45b977cc6e9ed9dacd0da206f424be1",
    "cross_cats_sorted": [],
    "license": "http://creativecommons.org/licenses/by/4.0/",
    "primary_cat": "math.OC",
    "submitted_at": "2026-05-16T08:36:29Z",
    "title_canon_sha256": "3b5bcbd3289987dd578d97322cc21c896a6c00806f15d43636cba49b9042c8eb"
  },
  "schema_version": "1.0",
  "source": {
    "id": "2605.16875",
    "kind": "arxiv",
    "version": 1
  }
}