Paper Citation Record · LEDGER

DASH: A Dimensionality Reduction Method for Large-scale Convex MIQP with Applications in Subset Portfolio Selection

As of 9 August 2026, this Paper Citation Record lists 45 of 45 outbound references and 0 inbound Pith citation observations for arXiv:2606.20141.

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

pith.paper-citation-record.v1
2606.20141 v1

Coverage vector

measured 45 of 45 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-06-26T15:02:00.766425Z

measured 45 of 45 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-09T06:31:02.800959+00:00

measured 0 of 0 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links

measured 0 of 1 external citation measurements

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

Source: cited_works

Reference resolution

45 of 45 outbound references displayed

verified exact4
verified fuzzy0
unresolved41
parse uncertain0
malformed identifier0
metadata mismatch0

External citation measurements

No source-named external measurement is stored.

Outbound references

Observation f1d59ce7-110b-4f26-a710-2d55b7d83c27 · outbound

This paper cites The Journal of Finance , year =.

DASH: A Dimensionality Reduction Method for Large-scale Convex MIQP with Applications in Subset Portfolio Selection The Journal of Finance , year =

Reference 1

Resolution: unresolved; no resolver link, observed 2026-06-26T15:02:00.766425Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation c58249f3-21b6-401e-8277-7caf6f9af61c · outbound

This paper cites 2003 , type =.

DASH: A Dimensionality Reduction Method for Large-scale Convex MIQP with Applications in Subset Portfolio Selection 2003 , type =

Reference 2

Resolution: unresolved; no resolver link, observed 2026-06-26T15:02:00.766425Z

Source-reported events for the cited work

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Observation 74e2f318-f1ea-4aa1-80ba-5290efb19c3e · outbound

This paper cites INFORMS Journal on Computing , volume =.

DASH: A Dimensionality Reduction Method for Large-scale Convex MIQP with Applications in Subset Portfolio Selection INFORMS Journal on Computing , volume =

Reference 3

Resolution: unresolved; no resolver link, observed 2026-06-26T15:02:00.766425Z

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Observation 9b3ccbaa-a8b9-41d7-a113-3296b922ef3a · outbound

This paper cites A unified approach to mixed-integer optimization problems with logical constraints.

DASH: A Dimensionality Reduction Method for Large-scale Convex MIQP with Applications in Subset Portfolio Selection A unified approach to mixed-integer optimization problems with logical constraints

Reference 4

Resolution: verified exact; arxiv_id, observed 2026-07-04T05:59:37.730164Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

Observation 6e971546-7021-4531-9806-a75d472d294f · outbound

This paper cites , title =.

DASH: A Dimensionality Reduction Method for Large-scale Convex MIQP with Applications in Subset Portfolio Selection , title =

Reference 5

Resolution: unresolved; no resolver link, observed 2026-06-26T15:02:00.766425Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation fb2aa128-5d56-4fe0-b2d8-17a479f304f1 · outbound

This paper cites Advances in Neural Information Processing Systems (NIPS) , year =.

DASH: A Dimensionality Reduction Method for Large-scale Convex MIQP with Applications in Subset Portfolio Selection Advances in Neural Information Processing Systems (NIPS) , year =

Reference 6

Resolution: unresolved; no resolver link, observed 2026-06-26T15:02:00.766425Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 5eb0a165-88d9-4fd7-bcaa-94c3c9911e3d · outbound

This paper cites Proceedings of the 30th International Conference on Machine Learning (ICML) , year =.

DASH: A Dimensionality Reduction Method for Large-scale Convex MIQP with Applications in Subset Portfolio Selection Proceedings of the 30th International Conference on Machine Learning (ICML) , year =

Reference 7

Resolution: unresolved; no resolver link, observed 2026-06-26T15:02:00.766425Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 78f297eb-4e96-4a9f-819f-aa1d2d8bb87b · outbound

This paper cites 2011 , type =.

DASH: A Dimensionality Reduction Method for Large-scale Convex MIQP with Applications in Subset Portfolio Selection 2011 , type =

Reference 8

Resolution: unresolved; no resolver link, observed 2026-06-26T15:02:00.766425Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 2ad52b76-d46c-446b-98ac-8042ba6611b6 · outbound

This paper cites An Overview of Bilevel Optimization , journal =.

DASH: A Dimensionality Reduction Method for Large-scale Convex MIQP with Applications in Subset Portfolio Selection An Overview of Bilevel Optimization , journal =

Reference 9

Resolution: unresolved; no resolver link, observed 2026-06-26T15:02:00.766425Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 78046882-a9ae-470b-85e2-b1e5bd6d7584 · outbound

This paper cites Proceedings of the 25th International Conference on Machine Learning (ICML) , year =.

DASH: A Dimensionality Reduction Method for Large-scale Convex MIQP with Applications in Subset Portfolio Selection Proceedings of the 25th International Conference on Machine Learning (ICML) , year =

Reference 10

Resolution: unresolved; no resolver link, observed 2026-06-26T15:02:00.766425Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 2a1605d6-1a35-45df-9f29-79f34681f0fc · outbound

This paper cites A Review on Bilevel Optimization: From Classical to Evolutionary Approaches and Applications.

DASH: A Dimensionality Reduction Method for Large-scale Convex MIQP with Applications in Subset Portfolio Selection A Review on Bilevel Optimization: From Classical to Evolutionary Approaches and Applications

Reference 11

Resolution: verified exact; arxiv_id, observed 2026-07-04T05:59:37.734164Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

Observation 3d03eace-f985-40ee-b0eb-138adfa22bef · outbound

This paper cites , title =.

DASH: A Dimensionality Reduction Method for Large-scale Convex MIQP with Applications in Subset Portfolio Selection , title =

Reference 12

Resolution: unresolved; no resolver link, observed 2026-06-26T15:02:00.766425Z

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Observation fa453ea6-22d0-4b82-88ff-9b38c2a32040 · outbound

This paper cites Optimization Letters , volume =.

DASH: A Dimensionality Reduction Method for Large-scale Convex MIQP with Applications in Subset Portfolio Selection Optimization Letters , volume =

Reference 13

Resolution: unresolved; no resolver link, observed 2026-06-26T15:02:00.766425Z

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Observation e78968ea-dc0f-4600-bd7b-d9ee08cc6128 · outbound

This paper cites an unresolved cited work.

DASH: A Dimensionality Reduction Method for Large-scale Convex MIQP with Applications in Subset Portfolio Selection Unresolved cited work

Reference 14

Resolution: unresolved; no resolver link, observed 2026-06-26T15:02:00.766425Z

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Observation c87c8e4c-ae28-4c66-aa1a-d6825fd06e73 · outbound

This paper cites , title =.

DASH: A Dimensionality Reduction Method for Large-scale Convex MIQP with Applications in Subset Portfolio Selection , title =

Reference 15

Resolution: unresolved; no resolver link, observed 2026-06-26T15:02:00.766425Z

Source-reported events for the cited work

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Observation 4b9403e9-8ab4-4d44-8710-4cac53d92661 · outbound

This paper cites Markowitz Portfolio Construction at Seventy.

DASH: A Dimensionality Reduction Method for Large-scale Convex MIQP with Applications in Subset Portfolio Selection Markowitz Portfolio Construction at Seventy

Reference 16

Resolution: verified exact; arxiv_id, observed 2026-07-04T05:59:37.738481Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

Observation 2bb4ce07-00f0-4d01-845e-d0c064a367ba · outbound

This paper cites an unresolved cited work.

DASH: A Dimensionality Reduction Method for Large-scale Convex MIQP with Applications in Subset Portfolio Selection Unresolved cited work

Reference 17

Resolution: unresolved; no resolver link, observed 2026-06-26T15:02:00.766425Z

Source-reported events for the cited work

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Observation 16e11590-3500-433e-a211-0f1d52aee22e · outbound

This paper cites Financial Analysts Journal , year =.

DASH: A Dimensionality Reduction Method for Large-scale Convex MIQP with Applications in Subset Portfolio Selection Financial Analysts Journal , year =

Reference 18

Resolution: unresolved; no resolver link, observed 2026-06-26T15:02:00.766425Z

Source-reported events for the cited work

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Observation 55fb8300-93dd-4f54-80ea-97ce01d2b476 · outbound

This paper cites Journal of Portfolio Management , year =.

DASH: A Dimensionality Reduction Method for Large-scale Convex MIQP with Applications in Subset Portfolio Selection Journal of Portfolio Management , year =

Reference 19

Resolution: unresolved; no resolver link, observed 2026-06-26T15:02:00.766425Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 1aadfdd9-7a16-458b-9457-857792d40e67 · outbound

This paper cites an unresolved cited work.

DASH: A Dimensionality Reduction Method for Large-scale Convex MIQP with Applications in Subset Portfolio Selection Unresolved cited work

Reference 20

Resolution: unresolved; no resolver link, observed 2026-06-26T15:02:00.766425Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation c0d1eb39-3c70-4883-ab4d-78bcad6909d1 · outbound

This paper cites Mathematical Programming , volume =.

DASH: A Dimensionality Reduction Method for Large-scale Convex MIQP with Applications in Subset Portfolio Selection Mathematical Programming , volume =

Reference 21

Resolution: unresolved; no resolver link, observed 2026-06-26T15:02:00.766425Z

Source-reported events for the cited work

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Observation e2f75016-4ebc-494e-af6a-6bb5bd2331f4 · outbound

This paper cites Journal of Multivariate Analysis , volume =.

DASH: A Dimensionality Reduction Method for Large-scale Convex MIQP with Applications in Subset Portfolio Selection Journal of Multivariate Analysis , volume =

Reference 22

Resolution: unresolved; no resolver link, observed 2026-06-26T15:02:00.766425Z

Source-reported events for the cited work

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Observation dfa887c4-10d1-4ca1-afbb-8088a36ea432 · outbound

This paper cites and Uppal, Raman , title =.

DASH: A Dimensionality Reduction Method for Large-scale Convex MIQP with Applications in Subset Portfolio Selection and Uppal, Raman , title =

Reference 23

Resolution: unresolved; no resolver link, observed 2026-06-26T15:02:00.766425Z

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Observation 60f2cc2b-1e34-499f-b8a4-6f868a680d0e · outbound

This paper cites The Journal of Finance , volume =.

DASH: A Dimensionality Reduction Method for Large-scale Convex MIQP with Applications in Subset Portfolio Selection The Journal of Finance , volume =

Reference 24

Resolution: unresolved; no resolver link, observed 2026-06-26T15:02:00.766425Z

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Unavailable: canonical work link unavailable.

Observation 3858f76e-95a5-4b4d-8588-a38d37284e13 · outbound

This paper cites A Scalable Gradient-Based Optimization Framework for Sparse Minimum-Variance Portfolio Selection.

DASH: A Dimensionality Reduction Method for Large-scale Convex MIQP with Applications in Subset Portfolio Selection A Scalable Gradient-Based Optimization Framework for Sparse Minimum-Variance Portfolio Selection

Reference 25

Resolution: verified exact; arxiv_id, observed 2026-07-04T05:59:37.726228Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

Observation d81620ed-4bd6-4d52-8b27-5d8b31f09a65 · outbound

This paper cites 1996 , publisher=.

DASH: A Dimensionality Reduction Method for Large-scale Convex MIQP with Applications in Subset Portfolio Selection 1996 , publisher=

Reference 26

Resolution: unresolved; no resolver link, observed 2026-06-26T15:02:00.766425Z

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Unavailable: canonical work link unavailable.

Observation 1b251fcb-a9d6-4e20-a48d-771625743703 · outbound

This paper cites Naval Research Logistics Quarterly , volume=.

DASH: A Dimensionality Reduction Method for Large-scale Convex MIQP with Applications in Subset Portfolio Selection Naval Research Logistics Quarterly , volume=

Reference 27

Resolution: unresolved; no resolver link, observed 2026-06-26T15:02:00.766425Z

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Unavailable: canonical work link unavailable.

Observation 5b0c6f03-a25e-4f52-80a2-b2b0ded7a634 · outbound

This paper cites Journal of the Royal Statistical Society: Series B (Methodological) , volume=.

DASH: A Dimensionality Reduction Method for Large-scale Convex MIQP with Applications in Subset Portfolio Selection Journal of the Royal Statistical Society: Series B (Methodological) , volume=

Reference 28

Resolution: unresolved; no resolver link, observed 2026-06-26T15:02:00.766425Z

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Observation c2e9e66f-c354-41fd-a8c0-c50d54170467 · outbound

This paper cites 2009 , publisher=.

DASH: A Dimensionality Reduction Method for Large-scale Convex MIQP with Applications in Subset Portfolio Selection 2009 , publisher=

Reference 29

Resolution: unresolved; no resolver link, observed 2026-06-26T15:02:00.766425Z

Source-reported events for the cited work

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Observation 96e15b07-3a33-49ce-8f8a-7e446b2016bf · outbound

This paper cites International Conference on Learning Representations (ICLR) , year=.

DASH: A Dimensionality Reduction Method for Large-scale Convex MIQP with Applications in Subset Portfolio Selection International Conference on Learning Representations (ICLR) , year=

Reference 30

Resolution: unresolved; no resolver link, observed 2026-06-26T15:02:00.766425Z

Source-reported events for the cited work

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Observation 95303dae-df8d-490b-a8bc-c596510e7cfd · outbound

This paper cites Proceedings of Machine Learning and Systems , volume=.

DASH: A Dimensionality Reduction Method for Large-scale Convex MIQP with Applications in Subset Portfolio Selection Proceedings of Machine Learning and Systems , volume=

Reference 31

Resolution: unresolved; no resolver link, observed 2026-06-26T15:02:00.766425Z

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Observation 6768e91f-679a-4296-808e-b9d2544e1d3a · outbound

This paper cites Econometrica , volume=.

DASH: A Dimensionality Reduction Method for Large-scale Convex MIQP with Applications in Subset Portfolio Selection Econometrica , volume=

Reference 32

Resolution: unresolved; no resolver link, observed 2026-06-26T15:02:00.766425Z

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Observation cb918b3c-91c9-495f-92b4-4e9a1ad5b0d4 · outbound

This paper cites Journal of Banking & Finance , volume=.

DASH: A Dimensionality Reduction Method for Large-scale Convex MIQP with Applications in Subset Portfolio Selection Journal of Banking & Finance , volume=

Reference 33

Resolution: unresolved; no resolver link, observed 2026-06-26T15:02:00.766425Z

Source-reported events for the cited work

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Observation e187f334-462f-4080-be53-e71acac1cee8 · outbound

This paper cites The Journal of Portfolio Management , volume=.

DASH: A Dimensionality Reduction Method for Large-scale Convex MIQP with Applications in Subset Portfolio Selection The Journal of Portfolio Management , volume=

Reference 34

Resolution: unresolved; no resolver link, observed 2026-06-26T15:02:00.766425Z

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Observation f43f4f5e-7479-4d6a-ac5a-cfe2cb5191d7 · outbound

This paper cites Proceedings of the National Academy of Sciences , volume=.

DASH: A Dimensionality Reduction Method for Large-scale Convex MIQP with Applications in Subset Portfolio Selection Proceedings of the National Academy of Sciences , volume=

Reference 35

Resolution: unresolved; no resolver link, observed 2026-06-26T15:02:00.766425Z

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Unavailable: canonical work link unavailable.

Observation 7a8bca14-5f73-4e6b-b22e-81bfcba0684f · outbound

This paper cites The Journal of Finance , volume=.

DASH: A Dimensionality Reduction Method for Large-scale Convex MIQP with Applications in Subset Portfolio Selection The Journal of Finance , volume=

Reference 36

Resolution: unresolved; no resolver link, observed 2026-06-26T15:02:00.766425Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation a8da95e2-16c8-4f61-a4a7-1f2eb045217a · outbound

This paper cites Computers & Operations Research , volume=.

DASH: A Dimensionality Reduction Method for Large-scale Convex MIQP with Applications in Subset Portfolio Selection Computers & Operations Research , volume=

Reference 37

Resolution: unresolved; no resolver link, observed 2026-06-26T15:02:00.766425Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 2900261f-7e33-471b-86f8-068194c069a0 · outbound

This paper cites Mathematical Programming , volume =.

DASH: A Dimensionality Reduction Method for Large-scale Convex MIQP with Applications in Subset Portfolio Selection Mathematical Programming , volume =

Reference 38

Resolution: unresolved; no resolver link, observed 2026-06-26T15:02:00.766425Z

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Observation 26aa8a9e-9c31-410e-aa02-3270b50dc96c · outbound

This paper cites and Lee, Sangkyun and Bogdan, Ma.

DASH: A Dimensionality Reduction Method for Large-scale Convex MIQP with Applications in Subset Portfolio Selection and Lee, Sangkyun and Bogdan, Ma

Reference 39

Resolution: unresolved; no resolver link, observed 2026-06-26T15:02:00.766425Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 57f0018a-0007-4d83-a934-079bf7161d7a · outbound

This paper cites Operations Research , volume =.

DASH: A Dimensionality Reduction Method for Large-scale Convex MIQP with Applications in Subset Portfolio Selection Operations Research , volume =

Reference 40

Resolution: unresolved; no resolver link, observed 2026-06-26T15:02:00.766425Z

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Observation 3b31f791-9a62-40a8-9a6a-13bb8f3979aa · outbound

This paper cites Matematički Vesnik , volume =.

DASH: A Dimensionality Reduction Method for Large-scale Convex MIQP with Applications in Subset Portfolio Selection Matematički Vesnik , volume =

Reference 41

Resolution: unresolved; no resolver link, observed 2026-06-26T15:02:00.766425Z

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Observation b30bad2a-273a-48ca-bb34-a08d510f0cd3 · outbound

This paper cites The Annals of Statistics , volume=.

DASH: A Dimensionality Reduction Method for Large-scale Convex MIQP with Applications in Subset Portfolio Selection The Annals of Statistics , volume=

Reference 42

Resolution: unresolved; no resolver link, observed 2026-06-26T15:02:00.766425Z

Source-reported events for the cited work

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Observation e4fe4e7e-a6bf-467a-9754-0238ea9c94d0 · outbound

This paper cites Physical Review Letters , volume =.

DASH: A Dimensionality Reduction Method for Large-scale Convex MIQP with Applications in Subset Portfolio Selection Physical Review Letters , volume =

Reference 43

Resolution: unresolved; no resolver link, observed 2026-06-26T15:02:00.766425Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation ac7d2d71-61ae-4459-ad38-6482a3f9fe8c · outbound

This paper cites Operations Research , volume =.

DASH: A Dimensionality Reduction Method for Large-scale Convex MIQP with Applications in Subset Portfolio Selection Operations Research , volume =

Reference 44

Resolution: unresolved; no resolver link, observed 2026-06-26T15:02:00.766425Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation edc62745-b6a6-4e01-bd36-3fdcac74cd74 · outbound

This paper cites 2004 , publisher =.

DASH: A Dimensionality Reduction Method for Large-scale Convex MIQP with Applications in Subset Portfolio Selection 2004 , publisher =

Reference 45

Resolution: unresolved; no resolver link, observed 2026-06-26T15:02:00.766425Z

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Unavailable: canonical work link unavailable.

Pith citing papers

No inbound Pith citation observations are available.