A Comparison of Feasible Direction Methods for the Stochastic Transportation Problem

Daneva (Mitradjieva), MariaLinköpings universitet,Optimeringslära,Tekniska högskolan,Linköping University (author)

The feasible direction method of Frank and Wolfe has been claimed to be efficient for solving the stochastic transportation problem. While this is true for very moderate accuracy requirements, substantially more efficient algorithms are otherwise diagonalized Newton and conjugate Frank–Wolfe algorithms, which we describe and evaluate. Like the Frank–Wolfe algorithm, these two algorithms take advantage of the structure of the stochastic transportation problem. We also introduce a Frank–Wolfe type algorithm with multi-dimensional search; this search procedure exploits the Cartesian product structure of the problem. Numerical results for two classic test problem sets are given. The three new methods that are considered are shown to be superior to the Frank–Wolfe method, and also to an earlier suggested heuristic acceleration of the Frank–Wolfe method.

Larsson, TorbjörnLinköpings universitet,Optimeringslära,Tekniska högskolan,Linköping University(Swepub:liu)torla64 (author)
Patriksson, Michael,1964Gothenburg University,Göteborgs universitet,Institutionen för matematiska vetenskaper, matematik,Department of Mathematical Sciences, Mathematics,Mathematical Sciences, Chalmers University of Technology and Göteborg University, Gothenburg, Sweden(Swepub:gu)xpatmi (author)
Rydergren, ClasLinköpings universitet,Institutionen för teknik och naturvetenskap,Tekniska högskolan,Linköping University(Swepub:liu)clary35 (author)
Linköpings universitetOptimeringslära (creator_code:org_t)

In:Computational optimization and applications: Springer Science and Business Media LLC46:3, s. 451-4660926-60031573-2894

By the author/editor: Daneva (Mitradji ...; Larsson, Torbjör ...; Patriksson, Mich ...; Rydergren, Clas

About the subject

NATURAL SCIENCES: NATURAL SCIENCES; and Mathematics; and Computational Ma ...

By the university: Linköping University; Chalmers University of Technology; University of Gothenburg

Kungliga biblioteket hanterar dina personuppgifter i enlighet med EU:s dataskyddsförordning (2018), GDPR. Läs mer om hur det funkar här.
Så här hanterar KB dina uppgifter vid användning av denna tjänst.