Paper Citation Record · LEDGER

Optimization Learning

As of 11 August 2026, this Paper Citation Record lists 49 of 49 outbound references and 4 inbound Pith citation observations for arXiv:2501.03443.

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

pith.paper-citation-record.v1
2501.03443 v1

Coverage vector

measured 49 of 49 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-10T21:58:10.618646Z

measured 53 of 53 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-10T06:31:04.303077+00:00

measured 4 of 4 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-06-29T09:56:30.448334Z

measured 0 of 1 external citation measurements

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

Source: arxiv_reference, observed 2026-07-04T19:30:07.014273Z

Reference resolution

49 of 49 outbound references displayed

verified exact1
verified fuzzy38
unresolved9
parse uncertain0
malformed identifier1
metadata mismatch0

External citation measurements

No source-named external measurement is stored.

Outbound references

Observation 6343479d-082a-4b0a-9894-bdf7caf6ba21 · outbound

This paper cites Agrawal, Amos B., S.

Optimization Learning Agrawal, Amos B., S

Reference 1

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T21:58:11.392646Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation bb152c21-e981-45e3-aa8c-d1b203aae3c2 · outbound

This paper cites Optnet: Differentiable optimization as a layer in neural networks.

Optimization Learning Optnet: Differentiable optimization as a layer in neural networks

Reference 2

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T21:58:11.378721Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation 2821c03d-499c-4ae9-99a8-6336e2088cd2 · outbound

This paper cites Recent Developments in Security-Constrained AC Optimal Power Flow: Overview of Challenge 1 in the ARPA-E Grid Optimization Competition.

Optimization Learning Recent Developments in Security-Constrained AC Optimal Power Flow: Overview of Challenge 1 in the ARPA-E Grid Optimization Competition

Reference 3

Resolution: unresolved; no resolver link, observed 2026-08-10T21:58:10.438028Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 7a558172-1f30-4a9a-8778-d1d8fd84a51f · outbound

This paper cites Layer Normalization.

Optimization Learning Layer Normalization

Reference 4

Resolution: unresolved; no resolver link, observed 2026-08-10T21:58:10.442476Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 257fd8c0-2d99-4b60-94fd-1b4eba8d3f8d · outbound

This paper cites The Power Grid Library for Benchmarking AC Optimal Power Flow Algorithms.

Optimization Learning The Power Grid Library for Benchmarking AC Optimal Power Flow Algorithms

Reference 5

Resolution: unresolved; no resolver link, observed 2026-08-10T21:58:10.446654Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation a2a7ec43-e772-4614-bf77-21576483859b · outbound

This paper cites an unresolved cited work.

Optimization Learning Unresolved cited work

Reference 6

Resolution: unresolved; raw_fallback, observed 2026-08-10T21:58:11.363715Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation 9a95de33-26f0-4585-aae7-2b39a85ee726 · outbound

This paper cites Machine learning for combinatorial optimiza- tion: A methodological tour d’horizon.

Optimization Learning Machine learning for combinatorial optimiza- tion: A methodological tour d’horizon

Reference 7

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T21:58:11.343308Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation 9e907dce-a80e-4911-a481-3fe81f3d1d9b · outbound

This paper cites Two-Stage Learning For the Flexible Job Shop Scheduling Problem.

Optimization Learning Two-Stage Learning For the Flexible Job Shop Scheduling Problem

Reference 8

Resolution: verified exact; local_arxiv, observed 2026-08-10T21:58:10.660213Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation 528b9d90-1ff1-4efb-9a39-8de76c6564ca · outbound

This paper cites End-to-end feasible optimization proxies for large-scale economic dispatch.

Optimization Learning End-to-end feasible optimization proxies for large-scale economic dispatch

Reference 9

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T21:58:11.329342Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation 0d843c1a-e418-407a-8432-48fad11e1144 · outbound

This paper cites End-to-End Feasible Optimization Proxies for Large-Scale Economic Dispatch.

Optimization Learning End-to-End Feasible Optimization Proxies for Large-Scale Economic Dispatch

Reference 10

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T21:58:11.314992Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation 158bc24d-0d16-4a18-abc1-97896e1a73ed · outbound

This paper cites Real-time risk analysis with opti- mization proxies.

Optimization Learning Real-time risk analysis with opti- mization proxies

Reference 11

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T21:58:11.299395Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation 49e28767-206f-489d-bbe1-ab8a0afad6a8 · outbound

This paper cites Donti, David Rolnick, and J Zico Kolter.

Optimization Learning Donti, David Rolnick, and J Zico Kolter

Reference 12

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T21:58:11.281701Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation 0f13b330-0bd3-4ef2-899f-1220114ba9b6 · outbound

This paper cites Optimizing primary response in preventive security-constrained optimal power flow.IEEE Systems Journal, 12(1):414– 423, 2016.

Optimization Learning Optimizing primary response in preventive security-constrained optimal power flow.IEEE Systems Journal, 12(1):414– 423, 2016

Reference 13

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T21:58:11.268012Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation eba62ef8-6ff7-4fe8-990d-11cb2f90704d · outbound

This paper cites an unresolved cited work.

Optimization Learning Unresolved cited work

Reference 14

Resolution: unresolved; raw_fallback, observed 2026-08-10T21:58:11.252740Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation 113b1a99-a469-4d00-94a4-bb5c9353ff0e · outbound

This paper cites an unresolved cited work.

Optimization Learning Unresolved cited work

Reference 15

Resolution: unresolved; raw_fallback, observed 2026-08-10T21:58:11.235681Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation 0659c7c4-3521-4be7-934a-62b47f6f527a · outbound

This paper cites Gould, R.

Optimization Learning Gould, R

Reference 16

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T21:58:11.218875Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation 70a5a715-e7ad-4924-b7ca-7ed1d1a584ac · outbound

This paper cites “Grid optimization competition datasets, 2018.

Optimization Learning “Grid optimization competition datasets, 2018

Reference 17

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T21:58:11.202731Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation 71ff05b7-a1c9-4b9d-8763-c5ca9d73ec4e · outbound

This paper cites Direct calculation of line outage distribution factors.

Optimization Learning Direct calculation of line outage distribution factors

Reference 18

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T21:58:11.183042Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation c6749e94-a66d-4cbd-8d48-a0b9354c123d · outbound

This paper cites Fast Simultaneous Feasibility Test for Security Constrained Unit Commitment, 2022.

Optimization Learning Fast Simultaneous Feasibility Test for Security Constrained Unit Commitment, 2022

Reference 19

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T21:58:11.164749Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation e5a9c947-ad9d-496c-a69c-f85d9aac0ed7 · outbound

This paper cites Multilayer feedforward networks are universal approximators.

Optimization Learning Multilayer feedforward networks are universal approximators

Reference 20

Resolution: unresolved; no resolver link, observed 2026-08-10T21:58:10.505314Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation a5a15082-3853-49db-b8a7-27dc3853c38d · outbound

This paper cites DeepOPF-NGT: Fast No Ground Truth Deep Learning- Based Approach for AC-OPF Problems.

Optimization Learning DeepOPF-NGT: Fast No Ground Truth Deep Learning- Based Approach for AC-OPF Problems

Reference 21

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T21:58:11.138158Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation 0f1dffb4-b8ca-4f64-892a-1299ff927b26 · outbound

This paper cites AC Power Flow Data in MATPOWER and QCQP Format: iTesla, RTE Snapshots, and PEGASE, 2016.

Optimization Learning AC Power Flow Data in MATPOWER and QCQP Format: iTesla, RTE Snapshots, and PEGASE, 2016

Reference 22

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T21:58:11.124892Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation fd3af7c7-3af7-4eed-b729-54eee43363b4 · outbound

This paper cites Dual interior-point optimiza- tion learning, 2024.

Optimization Learning Dual interior-point optimiza- tion learning, 2024

Reference 23

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T21:58:11.111636Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation f228f8a6-1087-4e63-9644-c92e36d6dc7f · outbound

This paper cites A new computationally simple approach for implement- ing neural networks with output hard constraints.

Optimization Learning A new computationally simple approach for implement- ing neural networks with output hard constraints

Reference 24

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T21:58:11.093469Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation 52e240b2-6c5c-40ff-b143-cf973a2ea99d · outbound

This paper cites Fast approximations for job shop scheduling: A lagrangian dual deep learning method.

Optimization Learning Fast approximations for job shop scheduling: A lagrangian dual deep learning method

Reference 25

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T21:58:11.076823Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation 71b2666b-55d6-485b-beda-d9ba8c56a568 · outbound

This paper cites End-to-end con- strained optimization learning: A survey.

Optimization Learning End-to-end con- strained optimization learning: A survey

Reference 26

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T21:58:11.060249Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation 58a4b10b-1fd2-48ad-b98b-16e20cb8c7bf · outbound

This paper cites The Implicit Function Theorem.

Optimization Learning The Implicit Function Theorem

Reference 27

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T21:58:11.045398Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation 69fc4a50-1353-4333-a6e2-4d808d3933fa · outbound

This paper cites Learning to Solve Optimization Problems With Hard Linear Constraints.

Optimization Learning Learning to Solve Optimization Problems With Hard Linear Constraints

Reference 28

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T21:58:11.030586Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation b98d835f-2b48-441e-8a77-b21bc9721eeb · outbound

This paper cites The security-constrained commitment and dispatch for midwest iso day-ahead co-optimized energy and ancillary service market.

Optimization Learning The security-constrained commitment and dispatch for midwest iso day-ahead co-optimized energy and ancillary service market

Reference 29

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T21:58:11.014786Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation 645707fa-8053-4322-9bbc-6c8daf35de58 · outbound

This paper cites Schedule 28A – Demand Curves for Transmission Constraints, 2019.

Optimization Learning Schedule 28A – Demand Curves for Transmission Constraints, 2019

Reference 30

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T21:58:10.996646Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation c1f89028-fc2f-4fed-9e1e-161a1adaf73c · outbound

This paper cites Real-time energy and operating reserve market software formulations and business logic,.

Optimization Learning Real-time energy and operating reserve market software formulations and business logic,

Reference 31

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T21:58:10.983068Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation 8a183c1a-4e5d-44e1-ae0a-8e329d2d54c9 · outbound

This paper cites Schedule 28 – Demand Curves for TOperating Reserves, 2023.

Optimization Learning Schedule 28 – Demand Curves for TOperating Reserves, 2023

Reference 32

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T21:58:10.952880Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation 9f5baa79-d708-4f78-9e2b-a63ab339074c · outbound

This paper cites MOSEK Optimizer API for Julia 10.1.24 , 2022.

Optimization Learning MOSEK Optimizer API for Julia 10.1.24 , 2022

Reference 33

Resolution: malformed identifier; raw_fallback, observed 2026-08-10T21:58:10.939716Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation 856b87ca-724b-4233-bcad-2427bfb20d09 · outbound

This paper cites Optimization-based learning for dynamic load planning in trucking service networks, 2024.

Optimization Learning Optimization-based learning for dynamic load planning in trucking service networks, 2024

Reference 34

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T21:58:10.927664Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation 54088d95-321f-4c0d-a2f4-df9c51fc5f3f · outbound

This paper cites DeepOPF: A Deep Neural Network Approach for Security-Constrained DC OPF.

Optimization Learning DeepOPF: A Deep Neural Network Approach for Security-Constrained DC OPF

Reference 35

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T21:58:10.914763Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation 0a57959a-3308-4734-95cd-76071e652c99 · outbound

This paper cites Self-supervised primal-dual learning for constrained optimization.

Optimization Learning Self-supervised primal-dual learning for constrained optimization

Reference 36

Resolution: unresolved; no resolver link, observed 2026-08-10T21:58:10.573155Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 51952381-a42d-4448-aa8c-2030e5de9ff0 · outbound

This paper cites Self-supervised learning for large-scale preventive security constrained dc optimal power flow.

Optimization Learning Self-supervised learning for large-scale preventive security constrained dc optimal power flow

Reference 37

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T21:58:10.893334Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation 94193905-e9f7-44af-a2e3-cfe547a2c1b6 · outbound

This paper cites Dual Conic Proxies for AC Optimal Power Flow.

Optimization Learning Dual Conic Proxies for AC Optimal Power Flow

Reference 38

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T21:58:10.878092Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation 86cb164f-c9ad-4a72-a799-c7f4b9e2242c · outbound

This paper cites Dual lagrangian learning for conic optimization, 2024.

Optimization Learning Dual lagrangian learning for conic optimization, 2024

Reference 39

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T21:58:10.864934Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation e77ee8ac-2359-4a01-9c42-fbbf953ddf5c · outbound

This paper cites Security constrained unit commitment using line outage distribution factors.

Optimization Learning Security constrained unit commitment using line outage distribution factors

Reference 40

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T21:58:10.851441Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation 8656e5dc-f1ad-48c6-a6b0-186b970b8917 · outbound

This paper cites Rayen: Imposition of hard convex con- straints on neural networks.

Optimization Learning Rayen: Imposition of hard convex con- straints on neural networks

Reference 41

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T21:58:10.836964Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation fa81bb8e-997c-4605-9639-f192232c6b0b · outbound

This paper cites of Electrical Engineering.

Optimization Learning of Electrical Engineering

Reference 42

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T21:58:10.822464Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation 39a7c57b-a1cb-4ce5-8aec-67d734224851 · outbound

This paper cites Machine Learning for Optimal Power Flows.

Optimization Learning Machine Learning for Optimal Power Flows

Reference 43

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T21:58:10.806020Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation 3a710414-41e8-4ba8-87bd-6aba02259e6d · outbound

This paper cites Combining deep learning and optimization for preventive security-constrained DC optimal power flow.

Optimization Learning Combining deep learning and optimization for preventive security-constrained DC optimal power flow

Reference 44

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T21:58:10.786652Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation 770fe2fc-df27-48e2-a1a3-2bbcc1f539ae · outbound

This paper cites An exact and scalable problem decomposition for security-constrained optimal power flow.

Optimization Learning An exact and scalable problem decomposition for security-constrained optimal power flow

Reference 45

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T21:58:10.771579Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation 37f1bb3c-2d71-4c28-af08-c37830c05422 · outbound

This paper cites Fast optimal power flow with guarantees via an unsu- pervised generative model.

Optimization Learning Fast optimal power flow with guarantees via an unsu- pervised generative model

Reference 46

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T21:58:10.755152Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation 41f08120-0282-4db7-9a17-371e117f8da3 · outbound

This paper cites Melding the data-decisions pipeline: Decision- focused learning for combinatorial optimization.

Optimization Learning Melding the data-decisions pipeline: Decision- focused learning for combinatorial optimization

Reference 47

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T21:58:10.739849Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation c1d41ee4-5035-4bc6-969d-c024160a7573 · outbound

This paper cites Reinforcement learning from optimiza- tion proxy for ride-hailing vehicle relocation.

Optimization Learning Reinforcement learning from optimiza- tion proxy for ride-hailing vehicle relocation

Reference 48

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T21:58:10.722316Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation 99ae6262-e142-4f15-9fac-e261e78b969e · outbound

This paper cites an unresolved cited work.

Optimization Learning Unresolved cited work

Reference 2022

Resolution: unresolved; raw_fallback, observed 2026-08-10T21:58:10.968190Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Pith citing papers

Observation 2eafa899-4877-496f-84df-9c095a029944 · inbound

SnareNet: Flexible Repair Layers for Neural Networks with Hard Constraints cites this paper.

SnareNet: Flexible Repair Layers for Neural Networks with Hard Constraints Optimization Learning

Reference 37

Resolution: verified exact; arxiv_id, observed 2026-05-16T03:37:13.739729Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation 6b252443-76a9-462f-a1e9-e4d4e14ebfe2 · inbound

Improving Feasibility via Fast Autoencoder-Based Projections cites this paper.

Improving Feasibility via Fast Autoencoder-Based Projections Optimization Learning

Reference 12

Resolution: verified exact; arxiv_id, observed 2026-05-13T19:33:10.272191Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation 6350a1fb-1c97-4a52-a3ac-2dadd440618f · inbound

Decision-focused learning for optimal PV-Battery scheduling cites this paper.

Decision-focused learning for optimal PV-Battery scheduling Optimization Learning

Reference 24

Resolution: verified exact; arxiv_id, observed 2026-06-29T10:03:17.202884Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation 787c4451-0c12-4ecb-9074-9087c25f30f7 · inbound

Learning Optimization Proxies for Sequential Contextual Stochastic Programs: An Order Fulfillment Application cites this paper.

Learning Optimization Proxies for Sequential Contextual Stochastic Programs: An Order Fulfillment Application Optimization Learning

Reference 25

Resolution: metadata mismatch; arxiv_id, observed 2026-07-04T19:30:07.016155Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.