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Paper Citation Record · LEDGER

A Single-Loop Bilevel Deep Learning Method for Optimal Control of Obstacle Problems

As of 5 August 2026, this Paper Citation Record lists 59 of 59 outbound references and 1 inbound Pith citation observation for arXiv:2601.04120.

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

pith.paper-citation-record.v1
2601.04120 v2

Coverage vector

measured 59 of 59 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-03T12:12:35.266721Z

measured 60 of 60 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-05T06:32:48.257954+00:00

measured 1 of 1 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-06-28T18:10:57.897497Z

measured 0 of 1 external citation measurements

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

Source: pith, observed 2026-07-01T20:46:12.922754Z

Reference resolution

59 of 59 outbound references displayed

  • verified exact0
  • verified fuzzy0
  • unresolved59
  • parse uncertain0
  • malformed identifier0
  • metadata mismatch0

External citation measurements

No source-named external measurement is stored.

Outbound references

Observation 3b0b0930-a2ce-4866-9594-a0c0d6739270 · outbound

This paper cites A neural network approach to learning solutions of a class of elliptic variational inequalities.

A Single-Loop Bilevel Deep Learning Method for Optimal Control of Obstacle Problems A neural network approach to learning solutions of a class of elliptic variational inequalities

Reference 1

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source=arxiv_source observed=2026-08-03T12:12:35.100491Z digest=sha256:42095466bc00b51c7b339a9acf6859ac541303201f89de09d8ba6be06b0c3242

Observation 680d821d-234b-430e-a998-2c52a5c7a7fb · outbound

This paper cites Barbu , Optimal control of variational inequalities , Research Notes in Math., 100 (1984).

A Single-Loop Bilevel Deep Learning Method for Optimal Control of Obstacle Problems Barbu , Optimal control of variational inequalities , Research Notes in Math., 100 (1984)

Reference 2

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source=arxiv_source observed=2026-08-03T12:12:35.107703Z digest=sha256:4ae29bbf40f92064f6c292567ac7e4e565e3891b21840f59f1ef58f7de6cb91c

Observation 0308c88a-6520-4d7f-b7ec-c939d25cd25a · outbound

This paper cites Barry-Straume, A.

A Single-Loop Bilevel Deep Learning Method for Optimal Control of Obstacle Problems Barry-Straume, A

Reference 3

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source=arxiv_source observed=2026-08-03T12:12:35.111105Z digest=sha256:1ffdf98e68f76410fd8824a946ea007d8dbe5867e074281ed1fbafd2d14493cf

Observation 23b33946-d518-4119-ad7e-162c0d39a3be · outbound

This paper cites Bergounioux , Use of augmented Lagrangian methods for the optimal control of obstacle problems , Journal of Optimization Theory and Applications, 95 (1997), pp.

A Single-Loop Bilevel Deep Learning Method for Optimal Control of Obstacle Problems Bergounioux , Use of augmented Lagrangian methods for the optimal control of obstacle problems , Journal of Optimization Theory and Applications, 95 (1997), pp

Reference 4

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source=arxiv_source observed=2026-08-03T12:12:35.113838Z digest=sha256:c0ab4780e579fa1424566331658e04bca7ce559e46b7351cfc2acb216e8e6472

Observation 56834ca3-745a-46f5-9274-1d901b33495f · outbound

This paper cites Bergounioux and S.

A Single-Loop Bilevel Deep Learning Method for Optimal Control of Obstacle Problems Bergounioux and S

Reference 5

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source=arxiv_source observed=2026-08-03T12:12:35.116976Z digest=sha256:b2be982bb2a6294691c42dd7b9be354fa88174ffb681809e482e48aeffe1b885

Observation f1ed8994-9ff0-4737-8d15-078da11a0b96 · outbound

This paper cites Bourgat and G.

A Single-Loop Bilevel Deep Learning Method for Optimal Control of Obstacle Problems Bourgat and G

Reference 6

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source=arxiv_source observed=2026-08-03T12:12:35.120041Z digest=sha256:e346e883a7e0492f99b508da50c9a7bfd87d3896863a06f01b6ab6a91382ed35

Observation 5e79392d-0ac4-4624-a641-a7ff82535f80 · outbound

This paper cites u ller, and C. L \.

A Single-Loop Bilevel Deep Learning Method for Optimal Control of Obstacle Problems u ller, and C. L \

Reference 7

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source=arxiv_source observed=2026-08-03T12:12:35.123077Z digest=sha256:13eaee37ff75863a1dc7759ba485ddb7c0af054496680a3083b19f686367b91f

Observation 12393b1d-ec99-45c0-8a2c-65ce9472b35a · outbound

This paper cites Brezis and G.

A Single-Loop Bilevel Deep Learning Method for Optimal Control of Obstacle Problems Brezis and G

Reference 8

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source=arxiv_source observed=2026-08-03T12:12:35.126070Z digest=sha256:3a6361a0f37c51b1d17cb96532235040ad0d941093ec65752cf9da9afe52e88f

Observation 72f2133c-225c-4865-b922-25d0fdceddd3 · outbound

This paper cites an unresolved cited work.

A Single-Loop Bilevel Deep Learning Method for Optimal Control of Obstacle Problems Unresolved cited work

Reference 9

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source=arxiv_source observed=2026-08-03T12:12:35.129101Z digest=sha256:ec280ff1e67fec8b888c1fc4135e163943ff305758f6b8ad080eb15d186ec7c2

Observation d0078e6f-b63e-457b-b18d-a310061ce871 · outbound

This paper cites an unresolved cited work.

A Single-Loop Bilevel Deep Learning Method for Optimal Control of Obstacle Problems Unresolved cited work

Reference 10

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source=arxiv_source observed=2026-08-03T12:12:35.131959Z digest=sha256:7860b207f33379fedd973e61361c54ebc5cbe2a9a477cbfe7f7503445c90cc42

Observation 01e8a779-3b2f-449b-9236-af5baa36b37b · outbound

This paper cites Cheng, X.

A Single-Loop Bilevel Deep Learning Method for Optimal Control of Obstacle Problems Cheng, X

Reference 11

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source=arxiv_source observed=2026-08-03T12:12:35.134980Z digest=sha256:9335a5eda9d2f011ebaeab11e95e747fa1e39df28080973ce28051a0a6984e67

Observation 1330c10a-9ca1-48d3-b47d-5cead05c32f0 · outbound

This paper cites an unresolved cited work.

A Single-Loop Bilevel Deep Learning Method for Optimal Control of Obstacle Problems Unresolved cited work

Reference 12

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source=arxiv_source observed=2026-08-03T12:12:35.138070Z digest=sha256:ed781c4d4fb8651e97e24eb39da515fcaa15554c3270ce3a413f5977c48c98c4

Observation e539858d-78e8-4ef9-a780-1c64441491a5 · outbound

This paper cites an unresolved cited work.

A Single-Loop Bilevel Deep Learning Method for Optimal Control of Obstacle Problems Unresolved cited work

Reference 13

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source=arxiv_source observed=2026-08-03T12:12:35.140986Z digest=sha256:23aed087b69fe3a136fe85df05acb1d4a6e52199ab93f932073104f84c4ef63a

Observation 37ea1749-75ce-45c4-aafb-b87a43f43024 · outbound

This paper cites Cybenko , Approximation by superpositions of a sigmoidal function , Mathematics of Control, Signals and Systems, 2 (1989), pp.

A Single-Loop Bilevel Deep Learning Method for Optimal Control of Obstacle Problems Cybenko , Approximation by superpositions of a sigmoidal function , Mathematics of Control, Signals and Systems, 2 (1989), pp

Reference 14

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source=arxiv_source observed=2026-08-03T12:12:35.143584Z digest=sha256:af8a1091831991489f2a01ffbd280d9ce820ecfb786b853f12b5a13101bc10bd

Observation ce92c83d-d14e-4175-bcb5-22e87e8e2692 · outbound

This paper cites Darehmiraki , A deep learning approach for the obstacle problem , in Proceedings of Academia-Industry Consortium for Data Science: AICDS 2020, Springer, 2022, pp.

A Single-Loop Bilevel Deep Learning Method for Optimal Control of Obstacle Problems Darehmiraki , A deep learning approach for the obstacle problem , in Proceedings of Academia-Industry Consortium for Data Science: AICDS 2020, Springer, 2022, pp

Reference 15

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source=arxiv_source observed=2026-08-03T12:12:35.146107Z digest=sha256:929d3b2562b801725e60dbc07a2980b2456b9fb99006d99dca29d4b08ffb9c96

Observation 65d74692-c8ae-4811-a4ff-29d556986add · outbound

This paper cites De Los Reyes , On the optimal control of some nonsmooth distributed parameter systems arising in mechanics , GAMM-Mitteilungen, 40 (2018), pp.

A Single-Loop Bilevel Deep Learning Method for Optimal Control of Obstacle Problems De Los Reyes , On the optimal control of some nonsmooth distributed parameter systems arising in mechanics , GAMM-Mitteilungen, 40 (2018), pp

Reference 16

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source=arxiv_source observed=2026-08-03T12:12:35.148690Z digest=sha256:8e05a265e3cead40f9264723a394468b2f75fbe539284a1abc4064c3bc8e24f0

Observation 8160d55d-23c9-4510-b198-b92b580d0a25 · outbound

This paper cites an unresolved cited work.

A Single-Loop Bilevel Deep Learning Method for Optimal Control of Obstacle Problems Unresolved cited work

Reference 17

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source=arxiv_source observed=2026-08-03T12:12:35.151657Z digest=sha256:6f24de1fe516c798cfaa28636b41ebfd74dbc120b521d95a0628e0342c5b7c63

Observation 1e4db76c-8e35-4353-b4cf-3bb57cfe847e · outbound

This paper cites El Bahja, J.

A Single-Loop Bilevel Deep Learning Method for Optimal Control of Obstacle Problems El Bahja, J

Reference 18

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source=arxiv_source observed=2026-08-03T12:12:35.154516Z digest=sha256:d637217919954961199da55ea41013200a440fa6a4844e05505b3e0885612a00

Observation 1d47120f-c0ff-464a-a46f-b98b3d91a30a · outbound

This paper cites Friedman , Variational Principles and Free-Boundary Problems , Dover Books on Mathematics, Dover Publications, 2010.

A Single-Loop Bilevel Deep Learning Method for Optimal Control of Obstacle Problems Friedman , Variational Principles and Free-Boundary Problems , Dover Books on Mathematics, Dover Publications, 2010

Reference 19

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source=arxiv_source observed=2026-08-03T12:12:35.157552Z digest=sha256:f02adb291a395290bcb57eea7d3070e93ab6ce4d28b2cbd7e4fc9db25cdd9d7d

Observation d7fbf860-c3e2-4710-b45e-d340db4bca77 · outbound

This paper cites u ller, R. H. Hoppe, and C. L \.

A Single-Loop Bilevel Deep Learning Method for Optimal Control of Obstacle Problems u ller, R. H. Hoppe, and C. L \

Reference 20

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Observation f5c8d39d-4277-40d8-9148-5f611aed5a7a · outbound

This paper cites Moreau Envelope Based Difference-of-weakly-Convex Reformulation and Algorithm for Bilevel Programs.

A Single-Loop Bilevel Deep Learning Method for Optimal Control of Obstacle Problems Moreau Envelope Based Difference-of-weakly-Convex Reformulation and Algorithm for Bilevel Programs

Reference 21

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source=arxiv_source observed=2026-08-03T12:12:35.163024Z digest=sha256:0153dd97d4748b9aa84064b566d7f81902db49c3526aefc7abd6261a995ee104

Observation 6f2a216b-c70e-4114-880e-5123179cd282 · outbound

This paper cites an unresolved cited work.

A Single-Loop Bilevel Deep Learning Method for Optimal Control of Obstacle Problems Unresolved cited work

Reference 22

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source=arxiv_source observed=2026-08-03T12:12:35.166098Z digest=sha256:8e97abbfa397ab4ce2e29dcd3d193eba6c87ceb331e2a167e20d452e3bbcd284

Observation 0bf1e6e5-2aca-4b91-af37-360b5678f168 · outbound

This paper cites Glowinski , Lectures on Numerical Methods for Non-Linear Variational Problems , Springer Science & Business Media, 2008.

A Single-Loop Bilevel Deep Learning Method for Optimal Control of Obstacle Problems Glowinski , Lectures on Numerical Methods for Non-Linear Variational Problems , Springer Science & Business Media, 2008

Reference 23

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source=arxiv_source observed=2026-08-03T12:12:35.168658Z digest=sha256:56a909188d4e2097b622e50c7d809805ce29d49a8fcfd0ae6fac23af0e574d91

Observation 8445aa9e-5113-4ab9-a109-1ef25a4e1129 · outbound

This paper cites Glowinski , Variational Methods for the Numerical Solution of Nonlinear Elliptic Problems , SIAM, 2015.

A Single-Loop Bilevel Deep Learning Method for Optimal Control of Obstacle Problems Glowinski , Variational Methods for the Numerical Solution of Nonlinear Elliptic Problems , SIAM, 2015

Reference 24

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source=arxiv_source observed=2026-08-03T12:12:35.171015Z digest=sha256:749610f852f799b4a6cf87c3620a0e0716f3f919c7c9f45daa87ffe8a965e0aa

Observation 86cf702f-5116-4c61-a028-12eae0b55cfb · outbound

This paper cites an unresolved cited work.

A Single-Loop Bilevel Deep Learning Method for Optimal Control of Obstacle Problems Unresolved cited work

Reference 25

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source=arxiv_source observed=2026-08-03T12:12:35.173462Z digest=sha256:14824904f49212c43e7a4c8ec9534826c9b3523286257bcd0265b2485364778a

Observation 869990dd-5c4d-45c2-bc01-fb316995a7d6 · outbound

This paper cites an unresolved cited work.

A Single-Loop Bilevel Deep Learning Method for Optimal Control of Obstacle Problems Unresolved cited work

Reference 26

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source=arxiv_source observed=2026-08-03T12:12:35.175918Z digest=sha256:d18e7952b1c43e016c400a23e8ef34d3f4b963aaf936c968fa719a85e438cb84

Observation 93687cef-e66e-4bd7-980c-05a7ab6ade05 · outbound

This paper cites u ller, R. H. Hoppe, and C. L \.

A Single-Loop Bilevel Deep Learning Method for Optimal Control of Obstacle Problems u ller, R. H. Hoppe, and C. L \

Reference 27

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source=arxiv_source observed=2026-08-03T12:12:35.178357Z digest=sha256:dd07e01c27890e3be5bf1a42c96b9d1d1d15d00dbac9cbcd9a0920844204393e

Observation 192e9d38-fb10-4507-b0dd-f2e62aa15f29 · outbound

This paper cites Hinterm \"u ller and I.

A Single-Loop Bilevel Deep Learning Method for Optimal Control of Obstacle Problems Hinterm \"u ller and I

Reference 28

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source=arxiv_source observed=2026-08-03T12:12:35.181469Z digest=sha256:248d530a7efedfe4fc178507690bb00bbcd75bc4e7649415720e7438f492c5d0

Observation 7b2b5723-5f4d-43f2-baf6-8c9dad9e9ccd · outbound

This paper cites Hinterm \"u ller and I.

A Single-Loop Bilevel Deep Learning Method for Optimal Control of Obstacle Problems Hinterm \"u ller and I

Reference 29

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source=arxiv_source observed=2026-08-03T12:12:35.184411Z digest=sha256:244e4ef7093ddd3b8ebbbeb04e9005d623cb13d324e05e1d02e2eb8e6f089820

Observation 60e37f33-7e63-414a-90fe-41fc80557b2a · outbound

This paper cites u ller, C. L \.

A Single-Loop Bilevel Deep Learning Method for Optimal Control of Obstacle Problems u ller, C. L \

Reference 30

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source=arxiv_source observed=2026-08-03T12:12:35.186774Z digest=sha256:56d81bbf8f3167dcd81c3590325146509aceb07789a710aa0452946032f70f19

Observation 717eea34-d515-4310-9b8b-47325517f2a5 · outbound

This paper cites Hinterm \"u ller and T.

A Single-Loop Bilevel Deep Learning Method for Optimal Control of Obstacle Problems Hinterm \"u ller and T

Reference 31

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source=arxiv_source observed=2026-08-03T12:12:35.189787Z digest=sha256:614fc5a8cfdedfd59ef36802d6039816665923db3cccbe5fc11c66d68f9eada1

Observation 220e4a1a-c20f-47de-a4b5-4e99fbedd0d8 · outbound

This paper cites Hornik , Approximation capabilities of multilayer feedforward networks , Neural Networks, 4 (1991), pp.

A Single-Loop Bilevel Deep Learning Method for Optimal Control of Obstacle Problems Hornik , Approximation capabilities of multilayer feedforward networks , Neural Networks, 4 (1991), pp

Reference 32

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source=arxiv_source observed=2026-08-03T12:12:35.192605Z digest=sha256:c3b9264fe5bd1ea64e025993ac22d58ac239a085be9517f2793c832819990bd0

Observation 2d03d6b3-45c4-4d09-ac22-523374ff9da3 · outbound

This paper cites Hornik, M.

A Single-Loop Bilevel Deep Learning Method for Optimal Control of Obstacle Problems Hornik, M

Reference 33

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source=arxiv_source observed=2026-08-03T12:12:35.195181Z digest=sha256:415ee5fac41b32215e8d3f8e22395457a627fba8f8248f5777fced19fe5d4a55

Observation 3f4e00c5-0b7b-4d07-9128-0015fdf6f0c0 · outbound

This paper cites Ito and K.

A Single-Loop Bilevel Deep Learning Method for Optimal Control of Obstacle Problems Ito and K

Reference 34

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source=arxiv_source observed=2026-08-03T12:12:35.198142Z digest=sha256:1eab1610c300b5665537c5717637044b153e7633f8f1100b8c8c2783377c0988

Observation 5a45fc49-e70d-42de-bbd7-769d1ba2b0c0 · outbound

This paper cites Ito and K.

A Single-Loop Bilevel Deep Learning Method for Optimal Control of Obstacle Problems Ito and K

Reference 35

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source=arxiv_source observed=2026-08-03T12:12:35.200962Z digest=sha256:d082d2f9eef9cca073806bc440e8351ac1a4709ad469e8ebf2948984bb694191

Observation 75877fec-2d36-4c0c-93f4-fd6e411fc364 · outbound

This paper cites Jaillet, D.

A Single-Loop Bilevel Deep Learning Method for Optimal Control of Obstacle Problems Jaillet, D

Reference 36

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source=arxiv_source observed=2026-08-03T12:12:35.203608Z digest=sha256:eef020a2ef53439d9abd77814b697b67b4bb2bec7028a1e3f7321d67d856653e

Observation 1d5b406d-5e85-4bd9-9ba5-6d6e817dc259 · outbound

This paper cites Kinderlehrer and G.

A Single-Loop Bilevel Deep Learning Method for Optimal Control of Obstacle Problems Kinderlehrer and G

Reference 37

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source=arxiv_source observed=2026-08-03T12:12:35.206164Z digest=sha256:8f406d3f15188c7764605ce3c64589a164bb4d424f3cd95ff1ee3eb29cb55e08

Observation 49bebea6-d4c6-4968-ac3f-9339c8425878 · outbound

This paper cites an unresolved cited work.

A Single-Loop Bilevel Deep Learning Method for Optimal Control of Obstacle Problems Unresolved cited work

Reference 38

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Observation afd09c21-27a1-4270-9023-2529437c5ea0 · outbound

This paper cites an unresolved cited work.

A Single-Loop Bilevel Deep Learning Method for Optimal Control of Obstacle Problems Unresolved cited work

Reference 39

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Observation 171fd8a2-103d-4579-b1c8-025f47c35cd0 · outbound

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A Single-Loop Bilevel Deep Learning Method for Optimal Control of Obstacle Problems Unresolved cited work

Reference 40

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This paper cites an unresolved cited work.

A Single-Loop Bilevel Deep Learning Method for Optimal Control of Obstacle Problems Unresolved cited work

Reference 41

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This paper cites Meyer, A.

A Single-Loop Bilevel Deep Learning Method for Optimal Control of Obstacle Problems Meyer, A

Reference 42

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Observation 50f24ba1-e469-40f5-a0d5-57c82b29939e · outbound

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A Single-Loop Bilevel Deep Learning Method for Optimal Control of Obstacle Problems Meyer and O

Reference 43

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Observation deaa777a-dcaf-437e-82fa-24bdedee3059 · outbound

This paper cites Mignot , Contr \^o le dans les in \'e quations variationelles elliptiques , Journal of Functional Analysis, 22 (1976), pp.

A Single-Loop Bilevel Deep Learning Method for Optimal Control of Obstacle Problems Mignot , Contr \^o le dans les in \'e quations variationelles elliptiques , Journal of Functional Analysis, 22 (1976), pp

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Observation 4e3576ec-af58-4cf6-9686-cde9bba7302a · outbound

This paper cites Mignot and J.

A Single-Loop Bilevel Deep Learning Method for Optimal Control of Obstacle Problems Mignot and J

Reference 45

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Observation b3623220-3523-4361-9224-6a931d605b9f · outbound

This paper cites Mowlavi and S.

A Single-Loop Bilevel Deep Learning Method for Optimal Control of Obstacle Problems Mowlavi and S

Reference 46

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This paper cites Nemirovski, A.

A Single-Loop Bilevel Deep Learning Method for Optimal Control of Obstacle Problems Nemirovski, A

Reference 47

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Observation 6ce18784-99d9-4877-a520-f34c32fcbb9a · outbound

This paper cites Raissi, P.

A Single-Loop Bilevel Deep Learning Method for Optimal Control of Obstacle Problems Raissi, P

Reference 48

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Observation 23030fc8-3b1b-4472-b9a7-1ea3d997a0ea · outbound

This paper cites Schiela and D.

A Single-Loop Bilevel Deep Learning Method for Optimal Control of Obstacle Problems Schiela and D

Reference 49

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Observation 236e3a03-c6cc-41f4-956f-336893409252 · outbound

This paper cites Sirignano and K.

A Single-Loop Bilevel Deep Learning Method for Optimal Control of Obstacle Problems Sirignano and K

Reference 50

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Observation 00e4faa3-a265-44d0-a2c1-cc3f53e326bc · outbound

This paper cites Accelerated primal-dual methods with enlarged step sizes and operator learning for nonsmooth optimal control problems.

A Single-Loop Bilevel Deep Learning Method for Optimal Control of Obstacle Problems Accelerated primal-dual methods with enlarged step sizes and operator learning for nonsmooth optimal control problems

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Observation 55c69864-7c92-4716-a3a3-9558a83d3baa · outbound

This paper cites an unresolved cited work.

A Single-Loop Bilevel Deep Learning Method for Optimal Control of Obstacle Problems Unresolved cited work

Reference 52

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This paper cites An Operator Learning Approach to Nonsmooth Optimal Control of Nonlinear PDEs.

A Single-Loop Bilevel Deep Learning Method for Optimal Control of Obstacle Problems An Operator Learning Approach to Nonsmooth Optimal Control of Nonlinear PDEs

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Observation 872c1119-1a50-409f-95ac-1bf137bfb9e8 · outbound

This paper cites an unresolved cited work.

A Single-Loop Bilevel Deep Learning Method for Optimal Control of Obstacle Problems Unresolved cited work

Reference 54

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Observation 45cfaf1d-2cf5-46a9-9bd5-28e7a0a5d83e · outbound

This paper cites Wachsmuth , Strong stationarity for optimal control of the obstacle problem with control constraints , SIAM Journal on Optimization, 24 (2014), pp.

A Single-Loop Bilevel Deep Learning Method for Optimal Control of Obstacle Problems Wachsmuth , Strong stationarity for optimal control of the obstacle problem with control constraints , SIAM Journal on Optimization, 24 (2014), pp

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source=arxiv_source observed=2026-08-03T12:12:35.256021Z digest=sha256:67226b20d185823a39a90e5bcd863425eb7722715d91a6f97da4109db78c7f3d

Observation 9576372e-3b8b-4e8c-825a-cbec1fd7076a · outbound

This paper cites Wachsmuth , Towards M-stationarity for optimal control of the obstacle problem with control constraints , SIAM Journal on Control and Optimization, 54 (2016), pp.

A Single-Loop Bilevel Deep Learning Method for Optimal Control of Obstacle Problems Wachsmuth , Towards M-stationarity for optimal control of the obstacle problem with control constraints , SIAM Journal on Control and Optimization, 54 (2016), pp

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Observation 9a354286-a7e2-4e1b-abb1-225a344e6662 · outbound

This paper cites Fast PDE-constrained optimization via self-supervised operator learning.

A Single-Loop Bilevel Deep Learning Method for Optimal Control of Obstacle Problems Fast PDE-constrained optimization via self-supervised operator learning

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Observation b956511c-2959-4f5a-a349-ae3e8b6a0760 · outbound

This paper cites an unresolved cited work.

A Single-Loop Bilevel Deep Learning Method for Optimal Control of Obstacle Problems Unresolved cited work

Reference 58

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Observation 49480ada-1780-4653-b076-1cabe03ad23b · outbound

This paper cites write newline.

A Single-Loop Bilevel Deep Learning Method for Optimal Control of Obstacle Problems write newline

Reference 59

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source=arxiv_source observed=2026-08-03T12:12:35.266721Z digest=sha256:de77ea2dadfcdf7acb8add2e1acd8211629a838fccd4caba556f58df5305b00f

Pith citing papers

Observation bc51574e-fa65-4ea2-9ed9-73c4a070dc99 · inbound

Constrained Neural Parameterization for Optimization in Function Spaces cites this paper.

Constrained Neural Parameterization for Optimization in Function Spaces A Single-Loop Bilevel Deep Learning Method for Optimal Control of Obstacle Problems

Reference 37

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local_arxiv, observed 2026-07-01T20:46:12.924653Z

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No event found in the named queried sources as of 2026-08-05T06:32:48.257954+00:00.

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