Parallelizing Subgradient Methods for the Lagrangian Dual in Stochastic Mixed-Integer Programming | Zendy

Cong Han Lim | Zendy; Jeff Linderoth | Zendy; James Luedtke | Zendy; Stephen J. Wright | Zendy

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Parallelizing Subgradient Methods for the Lagrangian Dual in Stochastic Mixed-Integer Programming

Author(s) -

Cong Han Lim,

Jeff Linderoth,

James Luedtke,

Stephen J. Wright

Publication year - 2021

Publication title -

informs journal on optimization

Language(s) - English

Resource type - Journals

eISSN - 2575-1492

pISSN - 2575-1484

DOI - 10.1287/ijoo.2019.0029

Subject(s) - subgradient method , computer science , mathematical optimization , convergence (economics) , integer programming , lagrangian relaxation , dual (grammatical number) , stochastic programming , integer (computer science) , function (biology) , decomposition , algorithm , mathematics , art , ecology , literature , evolutionary biology , economics , biology , programming language , economic growth

The dual decomposition of stochastic mixed-integer programs can be solved by the projected subgradient algorithm. We show how to make this algorithm more amenable to parallelization in a master-wor...

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