Solving Lagrangian variational inequalities with applications to stochastic programming

Lagrangian variational inequalities feature both primal and dual elements in expressing first-order conditions for optimality in a wide variety of settings where “multipliers” in a very general sense need to be brought in. Their stochastic version relates to problems of stochastic programming and co...

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Main Authors: Rockafellar, R.T., Sun, Jie
Format: Journal Article
Language:English
Published: SPRINGER HEIDELBERG 2020
Subjects:
Online Access:http://hdl.handle.net/20.500.11937/91433
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author Rockafellar, R.T.
Sun, Jie
author_facet Rockafellar, R.T.
Sun, Jie
author_sort Rockafellar, R.T.
building Curtin Institutional Repository
collection Online Access
description Lagrangian variational inequalities feature both primal and dual elements in expressing first-order conditions for optimality in a wide variety of settings where “multipliers” in a very general sense need to be brought in. Their stochastic version relates to problems of stochastic programming and covers not only classical formats with inequality constraints but also composite models with nonsmooth objectives. The progressive hedging algorithm, as a means of solving stochastic programming problems, has however focused so far only on optimality conditions that correspond to variational inequalities in primal variables alone. Here that limitation is removed by appealing to a recent extension of progressive hedging to multistage stochastic variational inequalities in general.
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institution Curtin University Malaysia
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publishDate 2020
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spelling curtin-20.500.11937-914332023-04-20T06:12:53Z Solving Lagrangian variational inequalities with applications to stochastic programming Rockafellar, R.T. Sun, Jie Science & Technology Technology Physical Sciences Computer Science, Software Engineering Operations Research & Management Science Mathematics, Applied Computer Science Mathematics Stochastic variational inequality problems Stochastic programming problems Lagrangian variational inequalities Lagrange multipliers Progressive hedging algorithm Proximal point algorithm Composite optimization Lagrangian variational inequalities feature both primal and dual elements in expressing first-order conditions for optimality in a wide variety of settings where “multipliers” in a very general sense need to be brought in. Their stochastic version relates to problems of stochastic programming and covers not only classical formats with inequality constraints but also composite models with nonsmooth objectives. The progressive hedging algorithm, as a means of solving stochastic programming problems, has however focused so far only on optimality conditions that correspond to variational inequalities in primal variables alone. Here that limitation is removed by appealing to a recent extension of progressive hedging to multistage stochastic variational inequalities in general. 2020 Journal Article http://hdl.handle.net/20.500.11937/91433 10.1007/s10107-019-01458-0 English SPRINGER HEIDELBERG fulltext
spellingShingle Science & Technology
Technology
Physical Sciences
Computer Science, Software Engineering
Operations Research & Management Science
Mathematics, Applied
Computer Science
Mathematics
Stochastic variational inequality problems
Stochastic programming problems
Lagrangian variational inequalities
Lagrange multipliers
Progressive hedging algorithm
Proximal point algorithm
Composite optimization
Rockafellar, R.T.
Sun, Jie
Solving Lagrangian variational inequalities with applications to stochastic programming
title Solving Lagrangian variational inequalities with applications to stochastic programming
title_full Solving Lagrangian variational inequalities with applications to stochastic programming
title_fullStr Solving Lagrangian variational inequalities with applications to stochastic programming
title_full_unstemmed Solving Lagrangian variational inequalities with applications to stochastic programming
title_short Solving Lagrangian variational inequalities with applications to stochastic programming
title_sort solving lagrangian variational inequalities with applications to stochastic programming
topic Science & Technology
Technology
Physical Sciences
Computer Science, Software Engineering
Operations Research & Management Science
Mathematics, Applied
Computer Science
Mathematics
Stochastic variational inequality problems
Stochastic programming problems
Lagrangian variational inequalities
Lagrange multipliers
Progressive hedging algorithm
Proximal point algorithm
Composite optimization
url http://hdl.handle.net/20.500.11937/91433