Coordinate Optimality Reformulation for Mixed-Integer Convex Programs with Indicators | Paper Citation Record

Observation 3f70a1ca-ccde-47f4-b4a7-d82e5292f7f7 · outbound

This paper cites A strong conic quadratic reformulation for machine- job assignment with controllable processing times.Operations Research Letters, 37(3):187–191, 2009.

Coordinate Optimality Reformulation for Mixed-Integer Convex Programs with Indicators A strong conic quadratic reformulation for machine- job assignment with controllable processing times.Operations Research Letters, 37(3):187–191, 2009

Reference 1

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This paper cites Strong formulations for quadratic optimization with m-matrices and indicator variables.Mathematical Programming, 170(1):141–176, 2018.

Coordinate Optimality Reformulation for Mixed-Integer Convex Programs with Indicators Strong formulations for quadratic optimization with m-matrices and indicator variables.Mathematical Programming, 170(1):141–176, 2018

Reference 2

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Observation 3293092d-226f-45fd-91f5-6c754f9b92a5 · outbound

This paper cites Rank-one convexification for sparse regression.Journal of Machine Learning Research, 26(35):1–50, 2025.

Coordinate Optimality Reformulation for Mixed-Integer Convex Programs with Indicators Rank-one convexification for sparse regression.Journal of Machine Learning Research, 26(35):1–50, 2025

Reference 3

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Observation d2341214-8ac3-408a-971e-3e66bc8eeeaf · outbound

This paper cites Disjunctive programming: Properties of the convex hull of feasible points.Discrete Applied Mathematics, 89(1-3):3–44, 1998.

Coordinate Optimality Reformulation for Mixed-Integer Convex Programs with Indicators Disjunctive programming: Properties of the convex hull of feasible points.Discrete Applied Mathematics, 89(1-3):3–44, 1998

Reference 4

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Observation c7605d8b-4872-4e14-b842-80de08d76028 · outbound

This paper cites Bestsubsetselectionviaamodernoptimization lens.Annals of Statistics, pages 813–852, 2016.

Coordinate Optimality Reformulation for Mixed-Integer Convex Programs with Indicators Bestsubsetselectionviaamodernoptimization lens.Annals of Statistics, pages 813–852, 2016

Reference 5

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This paper cites Sparse high-dimensional regression.The Annals of Statistics, 48(1):300–323, 2020.

Coordinate Optimality Reformulation for Mixed-Integer Convex Programs with Indicators Sparse high-dimensional regression.The Annals of Statistics, 48(1):300–323, 2020

Reference 6

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Observation fdcf0dc3-fdfb-4cd0-a9d6-35008755d3a7 · outbound

This paper cites A parametric approach for solving convex quadratic optimization with indicators over trees.Mathematical Programming, pages 1–46, 2025.

Coordinate Optimality Reformulation for Mixed-Integer Convex Programs with Indicators A parametric approach for solving convex quadratic optimization with indicators over trees.Mathematical Programming, pages 1–46, 2025

Reference 7

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Observation d995221b-177a-4858-8ab2-a82021a93f51 · outbound

This paper cites Solving convex quadratic optimization with indicators over structured graphs.arXiv preprint arXiv:2603.02103, 2026.

Coordinate Optimality Reformulation for Mixed-Integer Convex Programs with Indicators Solving convex quadratic optimization with indicators over structured graphs.arXiv preprint arXiv:2603.02103, 2026

Reference 8

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Observation f1038d93-6faf-4107-a227-6e3a57612480 · outbound

This paper cites Computational study of a family of mixed-integer quadratic programming problems.

Coordinate Optimality Reformulation for Mixed-Integer Convex Programs with Indicators Computational study of a family of mixed-integer quadratic programming problems

Reference 9

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Observation ea54bfd4-8f0f-40bf-8ae6-5095dbace30e · outbound

This paper cites Solving convex QPs with structured sparsity under indicator conditions.

Coordinate Optimality Reformulation for Mixed-Integer Convex Programs with Indicators Solving convex QPs with structured sparsity under indicator conditions

Reference 10

Resolution: verified exact; local_arxiv, observed 2026-08-06T00:23:48.881928Z

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Observation 532a0d15-7c1b-4b98-a36f-e89b5652daf2 · outbound

This paper cites LP formulations for polynomial optimization problems.SIAM Journal on Optimization, 28(2):1121–1150, 2018.

Coordinate Optimality Reformulation for Mixed-Integer Convex Programs with Indicators LP formulations for polynomial optimization problems.SIAM Journal on Optimization, 28(2):1121–1150, 2018

Reference 11

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Observation 34809195-6008-4409-afb9-ce1691a32b11 · outbound

This paper cites An algorithmic framework for convex mixed integer nonlinear programs.Discrete optimization, 5(2):186–204, 2008.

Coordinate Optimality Reformulation for Mixed-Integer Convex Programs with Indicators An algorithmic framework for convex mixed integer nonlinear programs.Discrete optimization, 5(2):186–204, 2008

Reference 12

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T00:23:59.828859Z

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Observation cd4cebf0-eed1-4a6b-9f45-6c5de9e7ba5d · outbound

This paper cites Algorithms and software for convex mixed integer nonlinear programs.

Coordinate Optimality Reformulation for Mixed-Integer Convex Programs with Indicators Algorithms and software for convex mixed integer nonlinear programs

Reference 13

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Observation 04f3d67e-0dcf-4084-9438-9e933d0b9f3c · outbound

This paper cites Human activity recognition from accelerometer data using a wearable device.

Coordinate Optimality Reformulation for Mixed-Integer Convex Programs with Indicators Human activity recognition from accelerometer data using a wearable device

Reference 14

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T00:23:59.363395Z

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Observation 604e3909-8aaa-4db6-b8e1-120b961e4986 · outbound

This paper cites Personalization and user verification in wearable systems using biometric walking patterns.Personal and Ubiquitous Computing, 16(5):563–580, 2012.

Coordinate Optimality Reformulation for Mixed-Integer Convex Programs with Indicators Personalization and user verification in wearable systems using biometric walking patterns.Personal and Ubiquitous Computing, 16(5):563–580, 2012

Reference 15

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T00:23:59.121549Z

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Observation 8cc4f19a-b937-423b-b510-683139156e82 · outbound

This paper cites Convex programming for disjunctive convex optimization.Mathemat- ical Programming, 86(3):595–614, 1999.

Coordinate Optimality Reformulation for Mixed-Integer Convex Programs with Indicators Convex programming for disjunctive convex optimization.Mathemat- ical Programming, 86(3):595–614, 1999

Reference 16

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Observation ebba2b49-00a6-421b-846e-9bcf356b3366 · outbound

This paper cites Complexity of unconstrained minimization.

Coordinate Optimality Reformulation for Mixed-Integer Convex Programs with Indicators Complexity of unconstrained minimization

Reference 17

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Observation 1b99d233-2856-4672-a516-c7372e4906d0 · outbound

This paper cites Outer approximation with conic certificates for mixed-integer convex problems.Mathematical Programming Computation, 12:249–293, 2020.

Coordinate Optimality Reformulation for Mixed-Integer Convex Programs with Indicators Outer approximation with conic certificates for mixed-integer convex problems.Mathematical Programming Computation, 12:249–293, 2020

Reference 18

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Observation 78908bc9-056e-44d1-b87e-586ce75d0560 · outbound

This paper cites Learning sparse classifiers: Continuous and mixed integer optimization perspectives.Journal of Machine Learning Research, 22(135):1–47, 2021.

Coordinate Optimality Reformulation for Mixed-Integer Convex Programs with Indicators Learning sparse classifiers: Continuous and mixed integer optimization perspectives.Journal of Machine Learning Research, 22(135):1–47, 2021

Reference 19

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Observation af3c8936-bce0-4480-8d55-bbcfe4ab868c · outbound

This paper cites Subset selection in sparse matrices.SIAM Journal on Optimization, 30(2):1173–1190, 2020.

Coordinate Optimality Reformulation for Mixed-Integer Convex Programs with Indicators Subset selection in sparse matrices.SIAM Journal on Optimization, 30(2):1173–1190, 2020

Reference 20

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T00:23:58.364751Z

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Observation 171a92c0-15e6-4fde-90d7-bb9eb727e76c · outbound

This paper cites Regularization vs. Relaxation: A conic optimization perspective of statistical variable selection.

Coordinate Optimality Reformulation for Mixed-Integer Convex Programs with Indicators Regularization vs. Relaxation: A conic optimization perspective of statistical variable selection

Reference 21

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Observation eca884ec-1c06-43f6-bb30-7f9ddb1c805d · outbound

This paper cites An outer-approximation algorithm for a class of mixed- integer nonlinear programs.Mathematical programming, 36(3):307–339, 1986.

Coordinate Optimality Reformulation for Mixed-Integer Convex Programs with Indicators An outer-approximation algorithm for a class of mixed- integer nonlinear programs.Mathematical programming, 36(3):307–339, 1986

Reference 22

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T00:23:58.099088Z

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Observation 1d36b91a-863f-4f92-a7ec-4b7a052f4f0b · outbound

This paper cites Scalable inference of sparsely-changing Gaussian Markov random fields.Advances in Neural Information Processing Systems, 34:6529–6541, 2021.

Coordinate Optimality Reformulation for Mixed-Integer Convex Programs with Indicators Scalable inference of sparsely-changing Gaussian Markov random fields.Advances in Neural Information Processing Systems, 34:6529–6541, 2021

Reference 23

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T00:23:57.813132Z

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Observation ce71938e-a0a5-4e33-9682-7fba3e788a75 · outbound

This paper cites Solution Path of Time-varying Markov Random Fields with Discrete Regularization.

Coordinate Optimality Reformulation for Mixed-Integer Convex Programs with Indicators Solution Path of Time-varying Markov Random Fields with Discrete Regularization

Reference 24

Resolution: verified exact; local_arxiv, observed 2026-08-06T00:23:48.640359Z

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Observation ac38facf-0ad2-4124-82be-bc4ac9e8a310 · outbound

This paper cites Approximated perspective relaxations: a project and lift approach.Computational Optimization and Applications, 63(3):705–735, 2016.

Coordinate Optimality Reformulation for Mixed-Integer Convex Programs with Indicators Approximated perspective relaxations: a project and lift approach.Computational Optimization and Applications, 63(3):705–735, 2016

Reference 25

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Observation b220d591-d1c0-4e78-bad3-d903fd16052e · outbound

This paper cites Improving the approximated projected perspec- tive reformulation by dual information.Operations Research Letters, 45(5):519–524, 2017.

Coordinate Optimality Reformulation for Mixed-Integer Convex Programs with Indicators Improving the approximated projected perspec- tive reformulation by dual information.Operations Research Letters, 45(5):519–524, 2017

Reference 26

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Observation a4383bb2-9536-4219-aa77-a1b4d1c11a0d · outbound

This paper cites Perspective cuts for a class of convex 0–1 mixed integer pro- grams.Mathematical Programming, 106(2):225–236, 2006.

Coordinate Optimality Reformulation for Mixed-Integer Convex Programs with Indicators Perspective cuts for a class of convex 0–1 mixed integer pro- grams.Mathematical Programming, 106(2):225–236, 2006

Reference 27

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T00:23:56.982111Z

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Observation 8efffd9b-84ed-4b5e-8729-7bb4e851683c · outbound

This paper cites SDP diagonalizations and perspective cuts for a class of non- separable MIQP.Operations Research Letters, 35(2):181–185, 2007.

Coordinate Optimality Reformulation for Mixed-Integer Convex Programs with Indicators SDP diagonalizations and perspective cuts for a class of non- separable MIQP.Operations Research Letters, 35(2):181–185, 2007

Reference 28

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T00:23:56.785371Z

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Observation 05ac338e-efcc-496f-995f-d28c574e2ef8 · outbound

This paper cites Projected perspective refor- mulations with applications in design problems.Operations research, 59(5):1225–1232, 2011.

Coordinate Optimality Reformulation for Mixed-Integer Convex Programs with Indicators Projected perspective refor- mulations with applications in design problems.Operations research, 59(5):1225–1232, 2011

Reference 29

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Observation 1f3f38bb-a415-4d38-b26b-4ff05f48982d · outbound

This paper cites Decompositions of semidefinite matrices and the perspective reformulation of nonseparable quadratic programs.Mathematics of Operations Research, 45(1):15–33, 2020.

Coordinate Optimality Reformulation for Mixed-Integer Convex Programs with Indicators Decompositions of semidefinite matrices and the perspective reformulation of nonseparable quadratic programs.Mathematics of Operations Research, 45(1):15–33, 2020

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Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T00:23:56.261969Z

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Observation 0549295a-9e5a-4485-9147-535520240bb9 · outbound

This paper cites The number of maximal independent sets in connected graphs.Journal of Graph Theory, 11(4):463–470, 1987.

Coordinate Optimality Reformulation for Mixed-Integer Convex Programs with Indicators The number of maximal independent sets in connected graphs.Journal of Graph Theory, 11(4):463–470, 1987

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Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T00:23:55.999231Z

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Observation eb8a3e92-1ba7-4d52-b19a-174ee9e2835c · outbound

This paper cites Improved linear integer programming formulations of nonlinear integer problems.Man- agement Science, 22(4):455–460, 1975.

Coordinate Optimality Reformulation for Mixed-Integer Convex Programs with Indicators Improved linear integer programming formulations of nonlinear integer problems.Man- agement Science, 22(4):455–460, 1975

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Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T00:23:55.705263Z

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Observation 20d56d26-b333-4d1c-a542-4b2239955e4c · outbound

This paper cites Outlier detection in time series via mixed-integer conic quadratic optimization.SIAM Journal on Optimization, 31(3):1897–1925, 2021.

Coordinate Optimality Reformulation for Mixed-Integer Convex Programs with Indicators Outlier detection in time series via mixed-integer conic quadratic optimization.SIAM Journal on Optimization, 31(3):1897–1925, 2021

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Observation d6de9149-a95e-4185-b7ca-ca0ad986b21e · outbound

This paper cites Real-time solution of quadratic optimization problems with banded matrices and indicator variables.

Coordinate Optimality Reformulation for Mixed-Integer Convex Programs with Indicators Real-time solution of quadratic optimization problems with banded matrices and indicator variables

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Observation e2de60c7-fdcb-4c8f-8998-aeba1113be29 · outbound

This paper cites Outlier detection in regression: conic quadratic formulations.INFORMS Journal on Computing, 2025.

Coordinate Optimality Reformulation for Mixed-Integer Convex Programs with Indicators Outlier detection in regression: conic quadratic formulations.INFORMS Journal on Computing, 2025

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Observation 57d8e72e-940e-4298-8b1f-6775f04f3a38 · outbound

This paper cites Generalized convex disjunctive programming: Nonlinear convex hull relaxation.Computational optimization and applications, 26(1):83–100, 2003.

Coordinate Optimality Reformulation for Mixed-Integer Convex Programs with Indicators Generalized convex disjunctive programming: Nonlinear convex hull relaxation.Computational optimization and applications, 26(1):83–100, 2003

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Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T00:23:55.041001Z

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Observation c9d051bf-5b2b-425c-837f-c419b2806ec6 · outbound

This paper cites Perspective reformulations of mixed integer nonlinear programs with indicator variables.Mathematical Programming, 124(1):183–205, 2010.

Coordinate Optimality Reformulation for Mixed-Integer Convex Programs with Indicators Perspective reformulations of mixed integer nonlinear programs with indicator variables.Mathematical Programming, 124(1):183–205, 2010

Reference 37

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Observation 8b6d2724-7276-4bd0-a64d-3b3809700072 · outbound

This paper cites 2×2-convexifications for convex quadratic opti- mization with indicator variables.Mathematical Programming, 202(1):95–134, 2023.

Coordinate Optimality Reformulation for Mixed-Integer Convex Programs with Indicators 2×2-convexifications for convex quadratic opti- mization with indicator variables.Mathematical Programming, 202(1):95–134, 2023

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Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T00:23:54.771574Z

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Observation 6d2756d6-df30-4c13-8ca0-44207992e1c7 · outbound

This paper cites Fast best subset selection: Coordinate descent and local combinatorial optimization algorithms.Operations Research, 68(5):1517–1537, 2020.

Coordinate Optimality Reformulation for Mixed-Integer Convex Programs with Indicators Fast best subset selection: Coordinate descent and local combinatorial optimization algorithms.Operations Research, 68(5):1517–1537, 2020

Reference 39

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Observation 5b46efa5-62be-4523-a478-10ac7ad54892 · outbound

This paper cites Sparse regression at scale: Branch-and-bound rooted in first-order optimization.Mathematical Programming, 196(1):347–388, 2022.

Coordinate Optimality Reformulation for Mixed-Integer Convex Programs with Indicators Sparse regression at scale: Branch-and-bound rooted in first-order optimization.Mathematical Programming, 196(1):347–388, 2022

Reference 40

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T00:23:54.662977Z

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Observation e7fdb70d-be36-493d-a891-ccec3ca13635 · outbound

This paper cites Comparing solution paths of sparse quadratic minimization with a Stieltjes matrix.Mathematical Programming, 204(1):517–566, 2024.

Coordinate Optimality Reformulation for Mixed-Integer Convex Programs with Indicators Comparing solution paths of sparse quadratic minimization with a Stieltjes matrix.Mathematical Programming, 204(1):517–566, 2024

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Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T00:23:54.543955Z

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Observation 73f90c3b-f9d3-424b-88af-6f42929cdaca · outbound

This paper cites A combinatorial approach for small and strong formulations of disjunctive constraints.Mathematics of Operations Research, 44(3):793–820, 2019.

Coordinate Optimality Reformulation for Mixed-Integer Convex Programs with Indicators A combinatorial approach for small and strong formulations of disjunctive constraints.Mathematics of Operations Research, 44(3):793–820, 2019

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Observation 72adfca8-3e03-4617-9684-921790300d27 · outbound

This paper cites A geometric way to build strong mixed-integer programming formulations.Operations Research Letters, 47(6):601–606, 2019.

Coordinate Optimality Reformulation for Mixed-Integer Convex Programs with Indicators A geometric way to build strong mixed-integer programming formulations.Operations Research Letters, 47(6):601–606, 2019

Reference 43

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Observation d89d626e-7bcc-4d50-807e-7bfe745bb936 · outbound

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Coordinate Optimality Reformulation for Mixed-Integer Convex Programs with Indicators Unresolved cited work

Reference 44

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Observation c61f2bcd-9602-46a9-8987-80c856326d1c · outbound

This paper cites On minimal valid inequalities for mixed integer conic programs.Mathematics of Operations Research, 41(2):477–510, 2016.

Coordinate Optimality Reformulation for Mixed-Integer Convex Programs with Indicators On minimal valid inequalities for mixed integer conic programs.Mathematics of Operations Research, 41(2):477–510, 2016

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Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T00:23:53.979091Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation 9af3cdb0-4de6-4161-8fb9-42198595fb72 · outbound

This paper cites Two-term disjunctions on the second-order cone.Mathematical Programming, 154(1):463–491, 2015.

Coordinate Optimality Reformulation for Mixed-Integer Convex Programs with Indicators Two-term disjunctions on the second-order cone.Mathematical Programming, 154(1):463–491, 2015

Reference 46

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T00:23:53.763156Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation 9bc985bf-e29f-4482-adb3-b236f99c95c0 · outbound

This paper cites Consistent second-order conic integer programming for learning Bayesian networks.Journal of Machine Learning Research, 24(322):1–38, 2023.

Coordinate Optimality Reformulation for Mixed-Integer Convex Programs with Indicators Consistent second-order conic integer programming for learning Bayesian networks.Journal of Machine Learning Research, 24(322):1–38, 2023

Reference 47

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T00:23:53.603248Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation 2ed38401-ffe7-4d99-96e1-0a7ef07c232c · outbound

This paper cites Polyhedral analysis of quadratic optimization problems with Stieltjes matrices and indicators.Mathematical Programming, pages 1–27,.

Coordinate Optimality Reformulation for Mixed-Integer Convex Programs with Indicators Polyhedral analysis of quadratic optimization problems with Stieltjes matrices and indicators.Mathematical Programming, pages 1–27,

Reference 48

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T00:23:53.451100Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation f5ea6fcc-c84e-4efb-899d-e40367c5e3aa · outbound

This paper cites A graph-based decomposition method for convex quadratic optimization with indicators.Mathematical Programming, 200(2):669– 701, 2023.

Coordinate Optimality Reformulation for Mixed-Integer Convex Programs with Indicators A graph-based decomposition method for convex quadratic optimization with indicators.Mathematical Programming, 200(2):669– 701, 2023

Reference 49

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T00:23:53.323231Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation 72284a4c-13fd-4dc9-a642-c46a1aec5e40 · outbound

This paper cites Polyhedral approximation in mixed-integer convex optimization.Mathematical Programming, 172(1):139–168, 2018.

Coordinate Optimality Reformulation for Mixed-Integer Convex Programs with Indicators Polyhedral approximation in mixed-integer convex optimization.Mathematical Programming, 172(1):139–168, 2018

Reference 50

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T00:23:53.200988Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation a8794e2c-84de-4ae6-ac17-f9fd45293cdd · outbound

This paper cites Finding low-rank solutions of sparse linear matrix inequalities using convex optimization.SIAM Journal on Optimization, 27(2):725– 758, 2017.

Coordinate Optimality Reformulation for Mixed-Integer Convex Programs with Indicators Finding low-rank solutions of sparse linear matrix inequalities using convex optimization.SIAM Journal on Optimization, 27(2):725– 758, 2017

Reference 51

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T00:23:53.060154Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation 9b0ba320-3fcb-4b79-a9a1-5647c717f17e · outbound

This paper cites Integer programming for learning directed acyclic graphs from continuous data.INFORMS Journal on Optimization, 3(1):46–73, 2021.

Coordinate Optimality Reformulation for Mixed-Integer Convex Programs with Indicators Integer programming for learning directed acyclic graphs from continuous data.INFORMS Journal on Optimization, 3(1):46–73, 2021

Reference 52

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T00:23:52.937021Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation 32681e66-d2a6-49c0-b7df-29c115501430 · outbound

This paper cites Subset selection with shrinkage: Sparse linear modeling when the snr is low.Operations Research, 71(1):129–147, 2023.

Coordinate Optimality Reformulation for Mixed-Integer Convex Programs with Indicators Subset selection with shrinkage: Sparse linear modeling when the snr is low.Operations Research, 71(1):129–147, 2023

Reference 53

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T00:23:52.809264Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation 9335cccd-f0f2-433a-9236-93f9ceba698a · outbound

This paper cites Computation of Least Trimmed Squares: A Branch-and-Bound framework with Hyperplane Arrangement Enhancements.

Coordinate Optimality Reformulation for Mixed-Integer Convex Programs with Indicators Computation of Least Trimmed Squares: A Branch-and-Bound framework with Hyperplane Arrangement Enhancements

Reference 54

Resolution: verified exact; local_arxiv, observed 2026-08-06T00:23:48.129219Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation a52a9637-425d-44af-b58e-b8546f21c74f · outbound

This paper cites A mixed- integer programming approachfor unit commitment inmicro-grid with incentive-based demand response and battery energy storage system.Energies, 15(19):7192, 2022.

Coordinate Optimality Reformulation for Mixed-Integer Convex Programs with Indicators A mixed- integer programming approachfor unit commitment inmicro-grid with incentive-based demand response and battery energy storage system.Energies, 15(19):7192, 2022

Reference 55

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T00:23:52.650022Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation 4bc78d2c-f758-4472-8a45-a98fbe07ba2e · outbound

This paper cites Bayesian network learning via topological order.Journal of Machine Learning Research, 18(99):1–32, 2017.

Coordinate Optimality Reformulation for Mixed-Integer Convex Programs with Indicators Bayesian network learning via topological order.Journal of Machine Learning Research, 18(99):1–32, 2017

Reference 56

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T00:23:52.496032Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation ed7d872a-6c85-4b87-bfd6-13353fcbb0b9 · outbound

This paper cites An LP/NLP based branch and bound algorithm for convex minlp optimization problems.Computers & chemical engineering, 16(10-11):937–947, 1992.

Coordinate Optimality Reformulation for Mixed-Integer Convex Programs with Indicators An LP/NLP based branch and bound algorithm for convex minlp optimization problems.Computers & chemical engineering, 16(10-11):937–947, 1992

Reference 57

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T00:23:52.281148Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation 887b3c2a-d877-41f5-860c-81855ee5fdf1 · outbound

This paper cites Efficient inference of dynamic gene regulatory networks using discrete penalty.

Coordinate Optimality Reformulation for Mixed-Integer Convex Programs with Indicators Efficient inference of dynamic gene regulatory networks using discrete penalty

Reference 58

Resolution: verified exact; local_arxiv, observed 2026-08-06T00:23:47.894340Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation 5bf2cb95-52cd-415d-80ea-2ed71ffb92a2 · outbound

This paper cites Mixed integer linear programming formulation techniques.SIAM Review, 57(1):3– 57, 2015.

Coordinate Optimality Reformulation for Mixed-Integer Convex Programs with Indicators Mixed integer linear programming formulation techniques.SIAM Review, 57(1):3– 57, 2015

Reference 59

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T00:23:51.994355Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation 89fa6651-500f-4475-9e91-1273f779b53a · outbound

This paper cites Sums of squares and semidefinite program relaxations for polynomial optimization problems with structured sparsity.SIAM Journal on Optimization, 17(1):218–242, 2006.

Coordinate Optimality Reformulation for Mixed-Integer Convex Programs with Indicators Sums of squares and semidefinite program relaxations for polynomial optimization problems with structured sparsity.SIAM Journal on Optimization, 17(1):218–242, 2006

Reference 60

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T00:23:51.698224Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation 439ed746-aa97-429c-afd4-4aa2ea45594a · outbound

This paper cites On the convexification of constrained quadratic optimization problems with indicator variables.

Coordinate Optimality Reformulation for Mixed-Integer Convex Programs with Indicators On the convexification of constrained quadratic optimization problems with indicator variables

Reference 61

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T00:23:51.481319Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation eaaebb1d-da93-435e-a540-494881e7c2e9 · outbound

This paper cites Ideal formulations for constrained convex optimization problems with indicator variables.Mathematical Programming, 192(1):57–88, 2022.

Coordinate Optimality Reformulation for Mixed-Integer Convex Programs with Indicators Ideal formulations for constrained convex optimization problems with indicator variables.Mathematical Programming, 192(1):57–88, 2022

Reference 62

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T00:23:51.226242Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation 18751a55-d563-448d-a3f6-acb5a9a3c52e · outbound

This paper cites The number of maximal independent sets in a tree.SIAM Journal on Algebraic Discrete Methods, 7(1):125–130, 1986.

Coordinate Optimality Reformulation for Mixed-Integer Convex Programs with Indicators The number of maximal independent sets in a tree.SIAM Journal on Algebraic Discrete Methods, 7(1):125–130, 1986

Reference 63

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T00:23:50.955429Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation 6291e9b4-d7ec-4b59-bfac-e2273b6e949a · outbound

This paper cites Scalable algorithms for the sparse ridge regression.SIAM Journal on Optimization, 30(4):3359–3386, 2020.

Coordinate Optimality Reformulation for Mixed-Integer Convex Programs with Indicators Scalable algorithms for the sparse ridge regression.SIAM Journal on Optimization, 30(4):3359–3386, 2020

Reference 64

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T00:23:50.734196Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation 046e1998-ec27-4501-b6f7-3131cb03af6f · outbound

This paper cites An asymptotically optimal coordinate descent algorithm for learning Bayesian networks from Gaussian models.Journal of Machine Learning Research, 26(250):1–30, 2025.

Coordinate Optimality Reformulation for Mixed-Integer Convex Programs with Indicators An asymptotically optimal coordinate descent algorithm for learning Bayesian networks from Gaussian models.Journal of Machine Learning Research, 26(250):1–30, 2025

Reference 65

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T00:23:50.508108Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation 1fb3aab4-7065-4f0a-8dcc-656752fe3440 · outbound

This paper cites Integer programming for learning directed acyclic graphs from nonidentifiable Gaussian models.Biometrika, 112(3):asaf032, 04 2025.

Coordinate Optimality Reformulation for Mixed-Integer Convex Programs with Indicators Integer programming for learning directed acyclic graphs from nonidentifiable Gaussian models.Biometrika, 112(3):asaf032, 04 2025

Reference 66

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T00:23:50.230207Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation 3404e7e1-7271-42ca-bc44-07f5f7bad69d · outbound

This paper cites Facing up to arrangements: Face-count formulas for partitions of space by hyper- planes.Memoirs of the American Mathematical Society, 1(154):1–102, 1975.

Coordinate Optimality Reformulation for Mixed-Integer Convex Programs with Indicators Facing up to arrangements: Face-count formulas for partitions of space by hyper- planes.Memoirs of the American Mathematical Society, 1(154):1–102, 1975

Reference 67

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T00:23:49.968861Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation 3bdf1fda-1499-470e-ac08-5f9bfb1d3c3f · outbound

This paper cites an unresolved cited work.

Coordinate Optimality Reformulation for Mixed-Integer Convex Programs with Indicators Unresolved cited work

Reference 68

Resolution: unresolved; raw_fallback, observed 2026-08-06T00:23:49.732713Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation 4ad5742a-9e7e-4abd-8e27-04808a2f2145 · outbound

This paper cites Deleting outliers in robust regression with mixed integer programming.Acta Mathematicae Applicatae Sinica, 21(2):323–334, 2005.

Coordinate Optimality Reformulation for Mixed-Integer Convex Programs with Indicators Deleting outliers in robust regression with mixed integer programming.Acta Mathematicae Applicatae Sinica, 21(2):323–334, 2005

Reference 69

Resolution: malformed identifier; raw_fallback, observed 2026-08-06T00:23:49.439400Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

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