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pith:AF6JWETJ

pith:2026:AF6JWETJLXAD67NDCBMLHL42UZ

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Learning Scenario Reduction for Two-Stage Robust Optimization with Discrete Uncertainty

Jianan Zhou, Jieyi Bi, Jie Zhang, Tianjue Lin, Wen Song, Yaoxin Wu, Zhiguang Cao

A GNN-Transformer trained to imitate a lookahead heuristic selects reduced scenarios for two-stage robust optimization while matching quality at 7-200x higher speed.

arxiv:2605.14494 v1 · 2026-05-14 · cs.AI · cs.LG

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Claims

C1strongest claim

NeurPRISE consistently achieves competitive regret relative to comprehensive methods, maintains strong scalability with varying numbers of scenarios, and delivers 7-200x speedup over PRISE. NeurPRISE also exhibits strong zero-shot generalization, effectively handling instances with larger problem scales (up to 5x), more scenarios (up to 4x), and distribution shifts.

C2weakest assumption

That the marginal impact of each scenario on the recourse cost can be accurately approximated by a GNN-Transformer trained only on PRISE's selections, without needing to solve the full subproblems at inference time, and that this approximation transfers across problem scales and distributions.

C3one line summary

NeurPRISE trains a GNN-Transformer via imitation learning to mimic a lookahead heuristic for scenario reduction in 2RO, delivering 7-200x speedups with competitive regret on three test problems and zero-shot generalization.

References

37 extracted · 37 resolved · 1 Pith anchors

[1] Robust solutions of linear programming problems contaminated with uncertain data.Mathematical programming, 88(3):411–424, 2000 2000

[2] Adjustable robust solutions of uncertain linear programs.Mathematical programming, 99(2):351–376, 2004 2004

[3] Princeton University Press 2009

[4] Finite adaptability in multistage linear optimization.IEEE Transactions on Automatic Control, 55(12):2751–2766, 2010 2010

[5] Design of near optimal decision rules in multistage adaptive mixed-integer optimization.Operations Research, 63(3):610–627, 2015 2015

Receipt and verification

First computed	2026-05-17T23:39:06.397775Z
Builder	pith-number-builder-2026-05-17-v1
Signature	Pith Ed25519 (`pith-v1-2026-05`) · public key
Schema	pith-number/v1.0

Canonical hash

017c9b12695dc03f7da31058b3af9aa6721e19271ef55e9fa5ab0402f64edf90

Aliases

arxiv: 2605.14494 · arxiv_version: 2605.14494v1 · doi: 10.48550/arxiv.2605.14494 · pith_short_12: AF6JWETJLXAD · pith_short_16: AF6JWETJLXAD67ND · pith_short_8: AF6JWETJ

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Verify this Pith Number yourself

curl -sH 'Accept: application/ld+json' https://pith.science/pith/AF6JWETJLXAD67NDCBMLHL42UZ \
  | 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: 017c9b12695dc03f7da31058b3af9aa6721e19271ef55e9fa5ab0402f64edf90

Canonical record JSON

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      "cs.LG"
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    "license": "http://arxiv.org/licenses/nonexclusive-distrib/1.0/",
    "primary_cat": "cs.AI",
    "submitted_at": "2026-05-14T07:34:13Z",
    "title_canon_sha256": "ec451db23e60b61ad1cdd981c72a5581de602871af5773a5a00e2d7288e53a0a"
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  "schema_version": "1.0",
  "source": {
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