Pith Number

pith:SXPCRMA5

pith:2026:SXPCRMA5U7GC4KPPMV3PQZETNF

not attested not anchored not stored refs resolved

Approximate Distributed Coded Computing: Polynomial Codes and Randomized Sketching

Arya Mazumdar, Neophytos Charalambides

Combining polynomial codes and randomized sketching speeds up distributed optimization and machine learning despite slow servers.

arxiv:2605.16744 v1 · 2026-05-16 · cs.DC · cs.IR · eess.SP

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\usepackage{pith}
\pithnumber{SXPCRMA5U7GC4KPPMV3PQZETNF}

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Record completeness

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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

Distributed schemes that combine polynomial codes and randomized sketching can speed up optimization and machine learning algorithms in the presence of slow or non-responsive servers.

C2weakest assumption

The integration of coding-based redundancy with sketching-based approximation preserves enough accuracy for the target optimization and learning tasks while still delivering net speedup.

C3one line summary

Combines polynomial codes and randomized sketching into approximate distributed schemes that mitigate stragglers during optimization and machine learning tasks.

References

28 extracted · 28 resolved · 2 Pith anchors

[1] Speeding Up Distributed Machine Learning Using Codes, 2018

[2] Coded Computing: Miti- gating Fundamental Bottlenecks in Large-Scale Dis- tributed Computing and Machine Learning, 2020

[3] A Practical Guide to Randomized Matrix Computations with MATLAB Implementations 2015 · arXiv:1505.07570

[4] Ran- domized Numerical Linear Algebra: A Perspective on the Field With an Eye to Software, 2023

[5] Numerically stable coded matrix computations via circulant and rota- tion matrix embeddings, 2021

Formal links

2 machine-checked theorem links

Receipt and verification

First computed	2026-05-20T00:02:39.426969Z
Builder	pith-number-builder-2026-05-17-v1
Signature	Pith Ed25519 (`pith-v1-2026-05`) · public key
Schema	pith-number/v1.0

Canonical hash

95de28b01da7cc2e29ef6576f86493696c4e3cddb5baa2c02d680dd2a83a9373

Aliases

arxiv: 2605.16744 · arxiv_version: 2605.16744v1 · doi: 10.48550/arxiv.2605.16744 · pith_short_12: SXPCRMA5U7GC · pith_short_16: SXPCRMA5U7GC4KPP · pith_short_8: SXPCRMA5

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/SXPCRMA5U7GC4KPPMV3PQZETNF \
  | 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: 95de28b01da7cc2e29ef6576f86493696c4e3cddb5baa2c02d680dd2a83a9373

Canonical record JSON

{
  "metadata": {
    "abstract_canon_sha256": "20cce2442f26a222ba6d02f06e95a9942e76863bd7df5dc334d0544a5584f0ec",
    "cross_cats_sorted": [
      "cs.IR",
      "eess.SP"
    ],
    "license": "http://arxiv.org/licenses/nonexclusive-distrib/1.0/",
    "primary_cat": "cs.DC",
    "submitted_at": "2026-05-16T01:50:27Z",
    "title_canon_sha256": "60461c770663e3515cbbbfce1fc9ff397c8f9d8dd818d7a88af6140442375020"
  },
  "schema_version": "1.0",
  "source": {
    "id": "2605.16744",
    "kind": "arxiv",
    "version": 1
  }
}