A Sensitivity Assisted Alternating Directions Method of Multipliers for Distributed Optimization

Kód výsledku v IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F68407700%3A21230%2F22%3A00363764" target="_blank" >RIV/68407700:21230/22:00363764 - isvavai.cz</a>

Výsledek na webu

<a href="https://doi.org/10.1109/CDC51059.2022.9993352" target="_blank" >https://doi.org/10.1109/CDC51059.2022.9993352</a>

DOI - Digital Object Identifier

<a href="http://dx.doi.org/10.1109/CDC51059.2022.9993352" target="_blank" >10.1109/CDC51059.2022.9993352</a>

Jazyk výsledku

angličtina

Název v původním jazyce

A Sensitivity Assisted Alternating Directions Method of Multipliers for Distributed Optimization

Popis výsledku v původním jazyce

Alternating Directions Method of Multipliers (ADMM) is a form of decomposition-coordination method that typically requires several iterations/communication rounds between the subproblems and the master problem to converge. Repeatedly solving the subproblems over several iterations add to the total computation time. Noting that the subproblems solved from one iteration to the next differs only by a few variables, this paper proposes a novel sensitivity-assisted ADMM framework for nonlinear programming (NLP) problems, where the subproblems are cheaply approximated using the parametric sensitivities. By exploiting the parametric sensitivities, the computation of the subproblems can be reduced to a single linear solve instead of solving the full NLP problem, thereby reducing the overall computation cost. Different algorithmic variations are discussed and demonstrated using two numerical examples.

Název v anglickém jazyce

A Sensitivity Assisted Alternating Directions Method of Multipliers for Distributed Optimization

Popis výsledku anglicky

Alternating Directions Method of Multipliers (ADMM) is a form of decomposition-coordination method that typically requires several iterations/communication rounds between the subproblems and the master problem to converge. Repeatedly solving the subproblems over several iterations add to the total computation time. Noting that the subproblems solved from one iteration to the next differs only by a few variables, this paper proposes a novel sensitivity-assisted ADMM framework for nonlinear programming (NLP) problems, where the subproblems are cheaply approximated using the parametric sensitivities. By exploiting the parametric sensitivities, the computation of the subproblems can be reduced to a single linear solve instead of solving the full NLP problem, thereby reducing the overall computation cost. Different algorithmic variations are discussed and demonstrated using two numerical examples.

Druh

D - Stať ve sborníku

CEP obor

—

OECD FORD obor

10201 - Computer sciences, information science, bioinformathics (hardware development to be 2.2, social aspect to be 5.8)

Projekt

<a href="/cs/project/EF16_019%2F0000765" target="_blank" >EF16_019/0000765: Výzkumné centrum informatiky</a><br>

Návaznosti

P - Projekt vyzkumu a vyvoje financovany z verejnych zdroju (s odkazem do CEP)

Rok uplatnění

2022

Kód důvěrnosti údajů

S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

Název statě ve sborníku

Proceedings of 61st IEEE Conference on Decision and Control

ISBN

978-1-6654-6761-2

ISSN

0743-1546

e-ISSN

2576-2370

Počet stran výsledku

6

Strana od-do

295-300

Název nakladatele

IEEE

Místo vydání

Piscataway

Místo konání akce

Cancún

Datum konání akce

6. 12. 2022

Typ akce podle státní příslušnosti

WRD - Celosvětová akce

Kód UT WoS článku

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