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Peer-reviewedLicence not statedMathematical Programming Computation

PDLP: a practical first-order method for large-scale linear programming

  • David Applegate1
  • Mateo Díaz2
  • Oliver Hinder3
  • Haihao Lu4
  • Miles Lubin5
  • Brendan O’Donoghue6
  • Warren Schudy1
6 affiliations
  1. 1Google Research, New York, USA
  2. 2Johns Hopkins University, Baltimore, USA
  3. 3University of Pittsburgh, Pittsburgh, USA
  4. 4MIT and Google Research, Cambridge, USA
  5. 5Hudson River Trading, New York, USA
  6. 6Google DeepMind, London, UK
Open at sourcePDFDOICite

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Cite

BibTeXRIS
BibTeX
@article{applegate2026pdlp,
  title = {{PDLP: a practical first-order method for large-scale linear programming}},
  author = {David Applegate and Mateo Díaz and Oliver Hinder and Haihao Lu and Miles Lubin and Brendan O’Donoghue and Warren Schudy},
  journal = {Mathematical Programming Computation},
  year = {2026},
  month = may,
  doi = {10.1007/s12532-026-00309-2},
  url = {http://arxiv.org/abs/2501.07018},
}
RIS
TY  - JOUR
TI  - PDLP: a practical first-order method for large-scale linear programming
AU  - David Applegate
AU  - Mateo Díaz
AU  - Oliver Hinder
AU  - Haihao Lu
AU  - Miles Lubin
AU  - Brendan O’Donoghue
AU  - Warren Schudy
PY  - 2026
DA  - 2026/05/15
JO  - Mathematical Programming Computation
DO  - 10.1007/s12532-026-00309-2
UR  - http://arxiv.org/abs/2501.07018
SN  - 1867-2949
SN  - 1867-2957
ER  -