Pith. sign in

Paper Citation Record · LEDGER

Gradient Descent Methods for Regularized Optimization

As of 11 August 2026, this Paper Citation Record lists 26 of 26 outbound references and 0 inbound Pith citation observations for arXiv:2412.20115.

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

pith.paper-citation-record.v1
2412.20115 v1

Coverage vector

measured 26 of 26 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-10T23:37:08.525015Z

measured 26 of 26 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 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

26 of 26 outbound references displayed

  • verified exact4
  • verified fuzzy2
  • unresolved16
  • parse uncertain0
  • malformed identifier0
  • metadata mismatch4

External citation measurements

No source-named external measurement is stored.

Outbound references

Observation 3cc8f341-4feb-42f1-bfc2-68199efcc84c · outbound

This paper cites Beck, First-Order Optimization Methods , MOS-SIAM Series on Optimiza- tion, Society for Industrial and Applied Mathematics, Philadelphia, 2 017.

Gradient Descent Methods for Regularized Optimization Beck, First-Order Optimization Methods , MOS-SIAM Series on Optimiza- tion, Society for Industrial and Applied Mathematics, Philadelphia, 2 017

Reference 1

Resolution
unresolved
no resolver link, observed 2026-08-10T23:37:08.431622Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-10T23:37:08.431622Z digest=sha256:fe391634f888ace22a0d52243b210421f75f1a7bfc5a2261a921eff2fc4a5968

Observation 52d65bf6-cc3f-4ca8-ba4a-a8ed5759aacc · outbound

This paper cites an unresolved cited work.

Gradient Descent Methods for Regularized Optimization Unresolved cited work

Reference 2

Resolution
unresolved
raw_fallback, observed 2026-08-10T23:37:09.504425Z

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.

source=pdf_text observed=2026-08-10T23:37:08.436051Z digest=sha256:e5d094e2f5141b02083e9789bb5d16c8608e2d6cee6066c28ba3234af0b90d22

Observation 64a0d2b9-1616-4354-93f4-540437595bda · outbound

This paper cites an unresolved cited work.

Gradient Descent Methods for Regularized Optimization Unresolved cited work

Reference 3

Resolution
unresolved
raw_fallback, observed 2026-08-10T23:37:09.494903Z

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.

source=pdf_text observed=2026-08-10T23:37:08.439666Z digest=sha256:f0460616d0454ac91b2e19689c3464d2a5248fdcc07e312fff785b3ebfa9757c

Observation fcbf0c65-2327-4941-bd04-a6df34f785ca · outbound

This paper cites Dimovski and I.

Gradient Descent Methods for Regularized Optimization Dimovski and I

Reference 4

Resolution
verified exact
doi, observed 2026-08-10T23:37:08.638084Z

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.

source=pdf_text observed=2026-08-10T23:37:08.443587Z digest=sha256:bfa8f3907d2e4961b8f7f039aa370de75ba1851380ae35ae963f131fc4f16632

Observation 85c84daa-341a-41ec-b39e-1818e537d70b · outbound

This paper cites an unresolved cited work.

Gradient Descent Methods for Regularized Optimization Unresolved cited work

Reference 5

Resolution
unresolved
no resolver link, observed 2026-08-10T23:37:08.447490Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-10T23:37:08.447490Z digest=sha256:1d70e71e8500ada895caaf35b2a7ce8bb6fceeb8fc66a858eddfc7e34e8498cf

Observation 329458dd-eff6-4609-a62c-b5338f4535fc · outbound

This paper cites G¨ artner and M.

Gradient Descent Methods for Regularized Optimization G¨ artner and M

Reference 6

Resolution
verified fuzzy
raw_fallback, observed 2026-08-10T23:37:09.484933Z

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.

source=pdf_text observed=2026-08-10T23:37:08.451144Z digest=sha256:49920b75bd4b8bed1282b6af2f26e0b7baa0d95a7d0cf58e9067911eb73533bc

Observation b458e543-7e4d-4de6-bfc9-ce9c18480277 · outbound

This paper cites an unresolved cited work.

Gradient Descent Methods for Regularized Optimization Unresolved cited work

Reference 7

Resolution
unresolved
no resolver link, observed 2026-08-10T23:37:08.454756Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-10T23:37:08.454756Z digest=sha256:871818278cecffe8083c44d880e1282eaffedeae7c962642caa7b07d8dc417ba

Observation 00ea24b5-47e0-4242-8816-694cf8811866 · outbound

This paper cites Hsu, Identifying key variables and interactions in statistical m odels of building energy consumption using regularization, Elsevier Energy , 83 (2015), pp.

Gradient Descent Methods for Regularized Optimization Hsu, Identifying key variables and interactions in statistical m odels of building energy consumption using regularization, Elsevier Energy , 83 (2015), pp

Reference 8

Resolution
verified exact
doi, observed 2026-08-10T23:37:08.628538Z

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.

source=pdf_text observed=2026-08-10T23:37:08.458653Z digest=sha256:0edf82d384354862aa9cce643c9241837e94988d4aee0f8cd45ec0b894842fda

Observation 2d8be38c-9092-415a-b4ce-9d497472a801 · outbound

This paper cites Adam: A Method for Stochastic Optimization.

Gradient Descent Methods for Regularized Optimization Adam: A Method for Stochastic Optimization

Reference 9

Resolution
unresolved
no resolver link, observed 2026-08-10T23:37:08.462596Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-10T23:37:08.462596Z digest=sha256:39a6eb81d258435bce0e6d71b2e7dc8cdbbec085ba90c24d8ade1b5a3defa4e8

Observation 6542bdb8-b30b-4553-b53e-c3f3168f4bc9 · outbound

This paper cites Khalajmehrabadi, N.

Gradient Descent Methods for Regularized Optimization Khalajmehrabadi, N

Reference 10

Resolution
metadata mismatch
raw_fallback, observed 2026-08-10T23:37:09.298347Z

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.

source=pdf_text observed=2026-08-10T23:37:08.466846Z digest=sha256:292943c7e9cb0c7b1cef30cdfc17dc6bd0485dfd8884a1e4b590a3abf597b782

Observation a543400e-ef74-45d0-a8e2-38b6664c04d9 · outbound

This paper cites an unresolved cited work.

Gradient Descent Methods for Regularized Optimization Unresolved cited work

Reference 11

Resolution
verified exact
doi, observed 2026-08-10T23:37:08.607693Z

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.

source=pdf_text observed=2026-08-10T23:37:08.470362Z digest=sha256:0e10e19196fd406e46b5221de77dee4e94d77851d2195659376e1354bf3d0565

Observation 99e51ccf-bf78-4b3d-8324-df7c8c004fb7 · outbound

This paper cites Adaptive Gradient Descent without Descent.

Gradient Descent Methods for Regularized Optimization Adaptive Gradient Descent without Descent

Reference 12

Resolution
unresolved
no resolver link, observed 2026-08-10T23:37:08.474066Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-10T23:37:08.474066Z digest=sha256:0f9fa9f9e5e21c46de1de4b5764d494391c8a9f6636c3d276ebae8ad3d79101a

Observation b844b0ab-f210-4b6a-addd-ab0aed682511 · outbound

This paper cites Muthukrishnan and R.

Gradient Descent Methods for Regularized Optimization Muthukrishnan and R

Reference 13

Resolution
metadata mismatch
raw_fallback, observed 2026-08-10T23:37:09.221739Z

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.

source=pdf_text observed=2026-08-10T23:37:08.477776Z digest=sha256:dd91bc2af1df9223a44a3f9d70a637367b9ba850606593bd2060871f3bfe0827

Observation d848283d-db06-49f2-8546-de00e1e6500b · outbound

This paper cites Nesterov, Lectures on Convex Optimization (2nd edition) , Springer Optimization and Its Applications (137), Springer, Berlin, 2010.

Gradient Descent Methods for Regularized Optimization Nesterov, Lectures on Convex Optimization (2nd edition) , Springer Optimization and Its Applications (137), Springer, Berlin, 2010

Reference 14

Resolution
unresolved
no resolver link, observed 2026-08-10T23:37:08.481140Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-10T23:37:08.481140Z digest=sha256:604cd79ed7eb8b2a9794a7c23f1842a2001802030179054690153684ef839112

Observation eb7c38df-349b-468b-8c36-b270b7acb6ac · outbound

This paper cites Nocedal and S.

Gradient Descent Methods for Regularized Optimization Nocedal and S

Reference 15

Resolution
unresolved
no resolver link, observed 2026-08-10T23:37:08.484631Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-10T23:37:08.484631Z digest=sha256:38f546405caefc11b5a71ec5819b2dbc9c61edd3b18a3562c740bd71c3628ee6

Observation e3c63e04-6b05-4bd7-8126-29c6243c9de7 · outbound

This paper cites Parikh and S.

Gradient Descent Methods for Regularized Optimization Parikh and S

Reference 16

Resolution
unresolved
no resolver link, observed 2026-08-10T23:37:08.489192Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-10T23:37:08.489192Z digest=sha256:e7e9321b6a1432b03a3fdc7e49ddcf456a744dafc5e0b80fa9f3d2e556468a37

Observation 990d2dfb-a030-4bc4-8f92-536adc578ce6 · outbound

This paper cites Roth, The generalized LASSO, IEEE Transactions on Neural Networks , 15 (1) (2004), pp.

Gradient Descent Methods for Regularized Optimization Roth, The generalized LASSO, IEEE Transactions on Neural Networks , 15 (1) (2004), pp

Reference 17

Resolution
metadata mismatch
raw_fallback, observed 2026-08-10T23:37:09.149277Z

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.

source=pdf_text observed=2026-08-10T23:37:08.492806Z digest=sha256:c4eae8ef2cd9c68420b3906db57f52b5553515e514f591924450f976d1591100

Observation 60aae9d0-3a79-4142-9630-3ea934aca474 · outbound

This paper cites Tibshirani, Regression shrinkage and selection via the lasso, Journal of the Royal Statistical Society Series B: Statistical Methodology , 58 (1) (1996), pp.

Gradient Descent Methods for Regularized Optimization Tibshirani, Regression shrinkage and selection via the lasso, Journal of the Royal Statistical Society Series B: Statistical Methodology , 58 (1) (1996), pp

Reference 18

Resolution
unresolved
no resolver link, observed 2026-08-10T23:37:08.496312Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-10T23:37:08.496312Z digest=sha256:c16e69f27de09e1adf569464d8c8e2f59a1231f0ccbf24a2f7f068478bff9bb2

Observation ed647ea4-8af4-4a52-ba8c-010fd4fdc01c · outbound

This paper cites an unresolved cited work.

Gradient Descent Methods for Regularized Optimization Unresolved cited work

Reference 19

Resolution
unresolved
raw_fallback, observed 2026-08-10T23:37:09.474765Z

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.

source=pdf_text observed=2026-08-10T23:37:08.499984Z digest=sha256:62eb918daa4fe4c70d51db67e7a0b5933cb31827915e9705bb00a3dcb629acb8

Observation f5d2f677-9a02-4fab-9132-256036eb1d65 · outbound

This paper cites Schmitt, House sale prices for King County, version 2.0 [data set] (2019), acc‘essed November 2024.

Gradient Descent Methods for Regularized Optimization Schmitt, House sale prices for King County, version 2.0 [data set] (2019), acc‘essed November 2024

Reference 20

Resolution
verified fuzzy
raw_fallback, observed 2026-08-10T23:37:09.464437Z

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.

source=pdf_text observed=2026-08-10T23:37:08.503709Z digest=sha256:f238ca3c46012aeda231f10ed43a5e37824e4f18a41bed8f4b92aceb57359733

Observation 9f0d8344-48e6-4629-8415-7f99c8406f53 · outbound

This paper cites an unresolved cited work.

Gradient Descent Methods for Regularized Optimization Unresolved cited work

Reference 21

Resolution
verified exact
doi, observed 2026-08-10T23:37:08.565414Z

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.

source=pdf_text observed=2026-08-10T23:37:08.507019Z digest=sha256:12968c149917d15b4229ff2c8091470c27b0f7bda397123aae70f473535f95d0

Observation a1743b62-3956-4ef4-af01-e91b280328c7 · outbound

This paper cites an unresolved cited work.

Gradient Descent Methods for Regularized Optimization Unresolved cited work

Reference 22

Resolution
unresolved
no resolver link, observed 2026-08-10T23:37:08.510896Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-10T23:37:08.510896Z digest=sha256:d2e653b651a2e90ccc062e485f65d44b4809291c07994fe2150b1ad337e877fb

Observation 2e6e24ed-19b9-4ea6-b82e-8955095615a6 · outbound

This paper cites Zhang, J.

Gradient Descent Methods for Regularized Optimization Zhang, J

Reference 23

Resolution
unresolved
no resolver link, observed 2026-08-10T23:37:08.514440Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-10T23:37:08.514440Z digest=sha256:98c289e91f0cee6ba80e1fda0eceba7b8a4f76416bd9ac36c82313bc6eb4f598

Observation cae47bee-ed33-4996-8140-8424560155b9 · outbound

This paper cites an unresolved cited work.

Gradient Descent Methods for Regularized Optimization Unresolved cited work

Reference 24

Resolution
unresolved
no resolver link, observed 2026-08-10T23:37:08.517884Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-10T23:37:08.517884Z digest=sha256:6eb06efb02d389500ee96d8db9961fb3a3b47529557b2a6dff870a5b426a50d3

Observation 4f3e7b4a-e763-4cfc-b8f9-77a13b6b23bb · outbound

This paper cites an unresolved cited work.

Gradient Descent Methods for Regularized Optimization Unresolved cited work

Reference 25

Resolution
unresolved
no resolver link, observed 2026-08-10T23:37:08.521411Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-10T23:37:08.521411Z digest=sha256:4e2dd387d57e9b7772c5b266e8e4c0141999592a9f83215138881900c6663bcd

Observation a645a60d-c283-4621-976b-dff1fb1aa8eb · outbound

This paper cites Zou and L.

Gradient Descent Methods for Regularized Optimization Zou and L

Reference 26

Resolution
metadata mismatch
raw_fallback, observed 2026-08-10T23:37:08.753691Z

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.

source=pdf_text observed=2026-08-10T23:37:08.525015Z digest=sha256:7afcceffdeba76a6323b5b08b3fa1234654f9ee095d820e8b5e3f3b5428b4a35

Pith citing papers

No inbound Pith citation observations are available.