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Paper Citation Record · LEDGER

Learning Constrained Optimization with Deep Augmented Lagrangian Methods

As of 7 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 6 inbound Pith citation observations for arXiv:2403.03454.

A citation records a reference. It does not transfer a finding from one paper to another.

pith.paper-citation-record.v1
2403.03454 v2

Coverage vector

measured 0 of 0 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links

measured 6 of 6 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-07T06:34:17.273281+00:00

measured 6 of 6 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-07T13:05:03.286104Z

measured 0 of 1 external citation measurements

A source-named dated measurement, never combined with another source.

Source: arxiv_reference, observed 2026-05-11T05:00:55.697698Z

Reference resolution

0 of 0 outbound references displayed

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  • verified fuzzy0
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External citation measurements

No source-named external measurement is stored.

Outbound references

No outbound reference observations are available for this paper version.

Pith citing papers

Observation c9726591-64ee-43e2-880b-e68bbdab996f · inbound

PGLearn -- An Open-Source Learning Toolkit for Optimal Power Flow cites this paper.

PGLearn -- An Open-Source Learning Toolkit for Optimal Power Flow Learning Constrained Optimization with Deep Augmented Lagrangian Methods

Reference 26

Resolution
unresolved
no resolver link, observed 2026-08-07T13:05:03.286104Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-07T13:05:03.286104Z digest=sha256:99981f70e4c80600cd7ab0f0e68015936d64e4534aeb94eeaaa42d03e7dde145

Observation 5ab8a1b4-7959-4af1-8236-2134e6a77607 · inbound

Large-scale portfolio optimization with variational neural annealing cites this paper.

Large-scale portfolio optimization with variational neural annealing Learning Constrained Optimization with Deep Augmented Lagrangian Methods

Reference 85

Resolution
unresolved
no resolver link, observed 2026-08-06T18:55:23.267709Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-06T18:55:23.267709Z digest=sha256:d013a1fe566cbc64d893199d9a8af079e8834a1a108d6e3a93957ff0cf4bef44

Observation 536b88ff-a9fd-41b0-8fa8-ccb0d5de53b7 · inbound

Deep Uzawa for Kinetic Transport with Lagrange-Enforced Boundaries cites this paper.

Deep Uzawa for Kinetic Transport with Lagrange-Enforced Boundaries Learning Constrained Optimization with Deep Augmented Lagrangian Methods

Reference 52

Resolution
unresolved
no resolver link, observed 2026-08-06T14:00:57.246170Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-06T14:00:57.246170Z digest=sha256:47d10c63904dd139e3f7bf83aef685cf0183b7e9fa628de0d03cdfc26b4d890a

Observation bd21077c-8aa1-4e45-ac6b-12ee0781330d · inbound

Learning to Optimize by Differentiable Programming cites this paper.

Learning to Optimize by Differentiable Programming Learning Constrained Optimization with Deep Augmented Lagrangian Methods

Reference 101

Resolution
unresolved
no resolver link, observed 2026-08-03T08:39:36.093364Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-03T08:39:36.093364Z digest=sha256:737d37034242e89707ce73ed7b94aafa34e4753e18563ce3e53cde07f57afb32

Observation 333b7092-e93e-402d-ab35-c7fa40a48e45 · inbound

Training with Hard Constraints: Learning Neural Certificates and Controllers for SDEs cites this paper.

Training with Hard Constraints: Learning Neural Certificates and Controllers for SDEs Learning Constrained Optimization with Deep Augmented Lagrangian Methods

Reference 4

Resolution
unresolved
no resolver link, observed 2026-08-02T20:23:43.710862Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-02T20:23:43.710862Z digest=sha256:bfddfa64994f3455f87304ddde4c9ccf77658ce536c78789fdce6d8a24c2d118

Observation ae148c38-ff85-4b7f-8895-77ff3a989d99 · inbound

Solving Max-Cut to Global Optimality via Feasibility-Preserving Graph Neural Networks cites this paper.

Solving Max-Cut to Global Optimality via Feasibility-Preserving Graph Neural Networks Learning Constrained Optimization with Deep Augmented Lagrangian Methods

Reference 46

Resolution
verified exact
arxiv_id, observed 2026-05-11T05:00:55.699589Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

source=pdf_text observed=2026-05-11T00:55:32.471978Z digest=sha256:11e63dba7361e3eb768454b84d48f4287bd04a720db172febea67180c7513054