dc.contributor
Mateo Fornés, Jordi
dc.contributor
Pagès Bernaus, Adela
dc.contributor
Universitat de Lleida. Escola Politècnica Superior
dc.contributor.author
Castells Gasia, Joan Pau
dc.date.accessioned
2024-12-05T23:04:41Z
dc.date.available
2024-12-05T23:04:41Z
dc.date.issued
2019-12-19T19:04:30Z
dc.date.issued
2019-12-19T19:04:30Z
dc.identifier
http://hdl.handle.net/10459.1/67761
dc.identifier.uri
http://hdl.handle.net/10459.1/67761
dc.description.abstract
This work presents a general parallelisation of the Progressive Hedging algorithm to coordinate the resolution of two-stage and multi-stage stochastic
mixed-integer problems without (binary or integer) variables in the first stage.
We report a benchmark study between the computational improvements using our proposal and the parallel version (using pyro) of the Pyomo integrated
Progressive Hedging. Moreover, we study the influence of a quadratic term to
accelerate the convergence, different scenario-cluster formation and several step
update policies by solving different instances using our proposal.
dc.rights
info:eu-repo/semantics/openAccess
dc.rights
http://creativecommons.org/licenses/by-nc-nd/4.0/
dc.subject
Progressive Hedging Algorithm
dc.subject
Stochastic mixed-integer optimization
dc.subject
Parallelization
dc.subject
Paral·lelisme (Informàtica)
dc.title
A scalable parallel Progressive Hedging Algorithm for stochastic cluster-scenario-based mixed-integer models
dc.type
info:eu-repo/semantics/bachelorThesis
