Paper Citation Record · LEDGER

BPQP: A Differentiable Convex Optimization Framework for Efficient End-to-End Learning

As of 19 August 2026, this Paper Citation Record lists 44 of 44 outbound references and 0 inbound Pith citation observations for arXiv:2411.19285.

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

pith.paper-citation-record.v1
2411.19285 v2

Coverage vector

measured 44 of 44 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-12T10:28:09.808244Z

measured 44 of 44 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-19T06:32:44.657259+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

44 of 44 outbound references displayed

verified exact2
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External citation measurements

No source-named external measurement is stored.

Outbound references

Observation 50ed1ea5-b59b-403b-9910-eb5f1cf9c18f · outbound

This paper cites Differentiable convex optimization layers.

BPQP: A Differentiable Convex Optimization Framework for Efficient End-to-End Learning Differentiable convex optimization layers

Reference 1

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T10:28:10.184435Z

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Observation b2c3b5e7-0ba5-4f06-a293-9fea29037cbd · outbound

This paper cites Deep declarative networks.

BPQP: A Differentiable Convex Optimization Framework for Efficient End-to-End Learning Deep declarative networks

Reference 2

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T10:28:10.175957Z

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Observation eb5ed0ac-d350-4782-a68f-2125bc3b797f · outbound

This paper cites A tutorial on energy- based learning.

BPQP: A Differentiable Convex Optimization Framework for Efficient End-to-End Learning A tutorial on energy- based learning

Reference 3

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Observation a4c59564-d72a-4a0f-814c-2829c973ff3f · outbound

This paper cites Generic methods for optimization-based modeling.

BPQP: A Differentiable Convex Optimization Framework for Efficient End-to-End Learning Generic methods for optimization-based modeling

Reference 4

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Observation 828504b7-dd93-4f1a-bbb5-4664a8505e97 · outbound

This paper cites Smart predict-and-optimize for hard combina- torial optimization problems.

BPQP: A Differentiable Convex Optimization Framework for Efficient End-to-End Learning Smart predict-and-optimize for hard combina- torial optimization problems

Reference 5

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Observation ce008269-e05d-4462-bb4b-3be278a19fa2 · outbound

This paper cites predict, then optimize.

BPQP: A Differentiable Convex Optimization Framework for Efficient End-to-End Learning predict, then optimize

Reference 6

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Source-reported events for the cited work

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Observation a4252219-0f16-409e-87c8-ccafa3c219f8 · outbound

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

BPQP: A Differentiable Convex Optimization Framework for Efficient End-to-End Learning Melding the data-decisions pipeline: Decision- focused learning for combinatorial optimization

Reference 7

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T10:28:10.140232Z

Source-reported events for the cited work

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Observation 6f00b34b-0188-406b-8564-6ea5ab108b42 · outbound

This paper cites Stochastic distribution control system design: a convex optimization approach.

BPQP: A Differentiable Convex Optimization Framework for Efficient End-to-End Learning Stochastic distribution control system design: a convex optimization approach

Reference 8

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T10:28:10.130693Z

Source-reported events for the cited work

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Observation 66234480-3de4-4790-9fb9-3c536b729428 · outbound

This paper cites Real-time convex optimization in signal processing.

BPQP: A Differentiable Convex Optimization Framework for Efficient End-to-End Learning Real-time convex optimization in signal processing

Reference 9

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T10:28:10.121757Z

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Observation bb93583c-870e-44ff-af0c-02726c468b5e · outbound

This paper cites Efficient and Modular Implicit Differentiation.

BPQP: A Differentiable Convex Optimization Framework for Efficient End-to-End Learning Efficient and Modular Implicit Differentiation

Reference 10

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Source-reported events for the cited work

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Observation 408c34ed-0c6a-49f1-b447-9424964f1731 · outbound

This paper cites Efficient multiple hyperparameter learning for log-linear models.

BPQP: A Differentiable Convex Optimization Framework for Efficient End-to-End Learning Efficient multiple hyperparameter learning for log-linear models

Reference 11

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T10:28:10.113169Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation 69fac1d9-fa44-453c-9879-54a469bbe55c · outbound

This paper cites Alternating differentiation for optimization layers.

BPQP: A Differentiable Convex Optimization Framework for Efficient End-to-End Learning Alternating differentiation for optimization layers

Reference 12

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T10:28:10.104310Z

Source-reported events for the cited work

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Observation e4af22a0-94a4-4b0d-8654-0b18e83fa3f0 · outbound

This paper cites An implicit function theorem.

BPQP: A Differentiable Convex Optimization Framework for Efficient End-to-End Learning An implicit function theorem

Reference 13

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T10:28:10.095857Z

Source-reported events for the cited work

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Observation 22560b55-7b23-4b7d-9605-52ac2f7078d5 · outbound

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

BPQP: A Differentiable Convex Optimization Framework for Efficient End-to-End Learning Optnet: Differentiable optimization as a layer in neural networks

Reference 14

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Source-reported events for the cited work

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Observation ffdbd41f-8d72-43d0-8442-e25e270c268c · outbound

This paper cites Differentiating Through a Cone Program.

BPQP: A Differentiable Convex Optimization Framework for Efficient End-to-End Learning Differentiating Through a Cone Program

Reference 15

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Source-reported events for the cited work

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Observation 51ed9700-9b12-4449-950f-12826076b98d · outbound

This paper cites Osqp: An operator splitting solver for quadratic programs.

BPQP: A Differentiable Convex Optimization Framework for Efficient End-to-End Learning Osqp: An operator splitting solver for quadratic programs

Reference 16

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Observation 5ea14657-3046-416c-811e-63761090aae6 · outbound

This paper cites The simplex method for quadratic programming.

BPQP: A Differentiable Convex Optimization Framework for Efficient End-to-End Learning The simplex method for quadratic programming

Reference 17

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T10:28:10.077546Z

Source-reported events for the cited work

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Observation e9cc218c-1780-47df-802e-d806c326e693 · outbound

This paper cites Efficient differentiable quadratic programming layers: an admm approach.

BPQP: A Differentiable Convex Optimization Framework for Efficient End-to-End Learning Efficient differentiable quadratic programming layers: an admm approach

Reference 18

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T10:28:10.067981Z

Source-reported events for the cited work

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Observation dd98e02b-ee3a-4067-b4fa-ba320d0aa2fb · outbound

This paper cites Cvxpy: A python-embedded modeling language for convex optimization.

BPQP: A Differentiable Convex Optimization Framework for Efficient End-to-End Learning Cvxpy: A python-embedded modeling language for convex optimization

Reference 19

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T10:28:10.059231Z

Source-reported events for the cited work

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Observation da301481-067d-47d3-ad13-45382fec22a1 · outbound

This paper cites Conic optimization via operator splitting and homogeneous self-dual embedding.

BPQP: A Differentiable Convex Optimization Framework for Efficient End-to-End Learning Conic optimization via operator splitting and homogeneous self-dual embedding

Reference 20

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T10:28:10.050287Z

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Observation c33bc2df-c993-4a7e-8906-1b2b6736dc6e · outbound

This paper cites Mipaal: Mixed integer program as a layer.

BPQP: A Differentiable Convex Optimization Framework for Efficient End-to-End Learning Mipaal: Mixed integer program as a layer

Reference 21

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T10:28:10.041299Z

Source-reported events for the cited work

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Observation 981aed01-08fa-4730-bec8-58902d5e9a58 · outbound

This paper cites Implicit mle: backpropagating through discrete exponential family distributions.

BPQP: A Differentiable Convex Optimization Framework for Efficient End-to-End Learning Implicit mle: backpropagating through discrete exponential family distributions

Reference 22

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T10:28:10.032566Z

Source-reported events for the cited work

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Observation 80462051-89a7-407b-9970-34ccb2fbdb73 · outbound

This paper cites Satnet: Bridging deep learning and logical reasoning using a differentiable satisfiability solver.

BPQP: A Differentiable Convex Optimization Framework for Efficient End-to-End Learning Satnet: Bridging deep learning and logical reasoning using a differentiable satisfiability solver

Reference 23

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Source-reported events for the cited work

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Observation 110192dc-3630-4991-8896-6c25126cf0de · outbound

This paper cites Qlib: An AI-oriented Quantitative Investment Platform.

BPQP: A Differentiable Convex Optimization Framework for Efficient End-to-End Learning Qlib: An AI-oriented Quantitative Investment Platform

Reference 24

Resolution: unresolved; no resolver link, observed 2026-08-12T10:28:09.750815Z

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Observation 84bf68e6-f765-456b-81c0-6cfad4de791c · outbound

This paper cites Robust linear programming discrimination of two linearly inseparable sets.

BPQP: A Differentiable Convex Optimization Framework for Efficient End-to-End Learning Robust linear programming discrimination of two linearly inseparable sets

Reference 25

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T10:28:10.019035Z

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Observation d45b4f59-a245-46de-bf67-ec43600ff18c · outbound

This paper cites End-to-end risk budgeting portfolio optimiza- tion with neural networks.

BPQP: A Differentiable Convex Optimization Framework for Efficient End-to-End Learning End-to-end risk budgeting portfolio optimiza- tion with neural networks

Reference 26

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T10:28:10.010576Z

Source-reported events for the cited work

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Observation 34746748-10f7-4729-9858-02c5bf38e89e · outbound

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BPQP: A Differentiable Convex Optimization Framework for Efficient End-to-End Learning Unresolved cited work

Reference 27

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Source-reported events for the cited work

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Observation 5a935b40-1154-450f-8849-082677753e4e · outbound

This paper cites V olatility clustering in financial markets: a microsimulation of interacting agents.

BPQP: A Differentiable Convex Optimization Framework for Efficient End-to-End Learning V olatility clustering in financial markets: a microsimulation of interacting agents

Reference 28

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T10:28:09.992569Z

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Observation 8ac0f6b5-d4ee-4622-b152-38ded76e27c0 · outbound

This paper cites Improved svrg for non-strongly-convex or sum-of-non- convex objectives.

BPQP: A Differentiable Convex Optimization Framework for Efficient End-to-End Learning Improved svrg for non-strongly-convex or sum-of-non- convex objectives

Reference 29

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Observation bab0d5cd-f0b3-4f9e-9044-69e012e25d9a · outbound

This paper cites DC3: A learning method for optimization with hard constraints.

BPQP: A Differentiable Convex Optimization Framework for Efficient End-to-End Learning DC3: A learning method for optimization with hard constraints

Reference 30

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Observation 33e84044-ec79-456a-91c0-255d1dbb6e2d · outbound

This paper cites End-to-end learning for optimization via constraint-enforcing approximators.

BPQP: A Differentiable Convex Optimization Framework for Efficient End-to-End Learning End-to-end learning for optimization via constraint-enforcing approximators

Reference 31

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T10:28:09.975229Z

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Observation 30ac6d32-b376-4d2e-a8f0-fa8ae06d4152 · outbound

This paper cites End-to-end stochastic optimization with energy-based model.

BPQP: A Differentiable Convex Optimization Framework for Efficient End-to-End Learning End-to-end stochastic optimization with energy-based model

Reference 32

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T10:28:09.966729Z

Source-reported events for the cited work

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Observation bb5ee0ab-b4f4-4fb3-b84f-737f21c8ceec · outbound

This paper cites Learning the travelling salesperson problem requires rethinking generalization.

BPQP: A Differentiable Convex Optimization Framework for Efficient End-to-End Learning Learning the travelling salesperson problem requires rethinking generalization

Reference 33

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T10:28:09.957988Z

Source-reported events for the cited work

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Observation 0bab6cc3-5aec-43bf-a57e-bf7935ae5fb9 · outbound

This paper cites Learning combinatorial optimization algorithms over graphs.

BPQP: A Differentiable Convex Optimization Framework for Efficient End-to-End Learning Learning combinatorial optimization algorithms over graphs

Reference 34

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Source-reported events for the cited work

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Observation 2a4a682e-e25f-412b-b90a-1b4abe196fdd · outbound

This paper cites Combinatorial Optimization by Graph Pointer Networks and Hierarchical Reinforcement Learning.

BPQP: A Differentiable Convex Optimization Framework for Efficient End-to-End Learning Combinatorial Optimization by Graph Pointer Networks and Hierarchical Reinforcement Learning

Reference 35

Resolution: unresolved; no resolver link, observed 2026-08-12T10:28:09.782242Z

Source-reported events for the cited work

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Observation a27d4acb-eb4a-4c57-9284-0341f290bc1a · outbound

This paper cites Attention, Learn to Solve Routing Problems!.

BPQP: A Differentiable Convex Optimization Framework for Efficient End-to-End Learning Attention, Learn to Solve Routing Problems!

Reference 36

Resolution: unresolved; no resolver link, observed 2026-08-12T10:28:09.785733Z

Source-reported events for the cited work

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Observation 41348664-65aa-4b6c-9a88-a4a736221a89 · outbound

This paper cites End-to-End Risk Budgeting Portfolio Optimization with Neural Networks.

BPQP: A Differentiable Convex Optimization Framework for Efficient End-to-End Learning End-to-End Risk Budgeting Portfolio Optimization with Neural Networks

Reference 37

Resolution: verified exact; local_arxiv, observed 2026-08-12T10:28:09.848673Z

Source-reported events for the cited work

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Observation 0ed05b92-fd6e-4dd1-b77d-af02ad2567de · outbound

This paper cites Decision-Focused Learning without Differentiable Optimization: Learning Locally Optimized Decision Losses.

BPQP: A Differentiable Convex Optimization Framework for Efficient End-to-End Learning Decision-Focused Learning without Differentiable Optimization: Learning Locally Optimized Decision Losses

Reference 38

Resolution: verified exact; local_arxiv, observed 2026-08-12T10:28:09.836455Z

Source-reported events for the cited work

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Observation b8a473e8-3b00-4f7e-881f-bedbdc546b9c · outbound

This paper cites Automatically learning compact quality-aware surrogates for optimization problems.

BPQP: A Differentiable Convex Optimization Framework for Efficient End-to-End Learning Automatically learning compact quality-aware surrogates for optimization problems

Reference 39

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T10:28:09.944680Z

Source-reported events for the cited work

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Observation 651149af-6964-4b3a-916a-5946ee735d59 · outbound

This paper cites Surco: Learning linear surrogates for combinatorial nonlinear optimization problems.

BPQP: A Differentiable Convex Optimization Framework for Efficient End-to-End Learning Surco: Learning linear surrogates for combinatorial nonlinear optimization problems

Reference 40

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T10:28:09.935407Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation 5f742917-6aa3-4652-a63d-55bb65d3d1e8 · outbound

This paper cites Landscape surrogate: Learning decision losses for mathematical optimization under partial information.

BPQP: A Differentiable Convex Optimization Framework for Efficient End-to-End Learning Landscape surrogate: Learning decision losses for mathematical optimization under partial information

Reference 41

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T10:28:09.926348Z

Source-reported events for the cited work

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Observation baf1c3b5-9fbc-402c-8967-f4cb61f5e59d · outbound

This paper cites Conic optimization via operator splitting and homogeneous self-dual embedding.

BPQP: A Differentiable Convex Optimization Framework for Efficient End-to-End Learning Conic optimization via operator splitting and homogeneous self-dual embedding

Reference 42

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T10:28:09.917755Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation 6b6f60eb-b377-414b-86a9-54f1f09ed754 · outbound

This paper cites Operator splitting for a homogeneous embedding of the linear comple- mentarity problem.

BPQP: A Differentiable Convex Optimization Framework for Efficient End-to-End Learning Operator splitting for a homogeneous embedding of the linear comple- mentarity problem

Reference 43

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T10:28:09.909097Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation 518d21ac-a4a2-4352-81ff-3dfaed8d7f75 · outbound

This paper cites Comparing technical and fun- damental indicators in stock price forecasting.

BPQP: A Differentiable Convex Optimization Framework for Efficient End-to-End Learning Comparing technical and fun- damental indicators in stock price forecasting

Reference 44

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T10:28:09.900404Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Pith citing papers

No inbound Pith citation observations are available.