UKP5: A New Algorithm for the Unbounded Knapsack Problem

Becker, Henrique; Buriol, Luciana S.

doi:10.1007/978-3-319-38851-9_4

Cited by 1 publication

(5 citation statements)

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“…The experiment presented in this section is an updated version of the experiment first presented in Becker & Buriol (2016). In this version GFDP is considered, UKP5 is replaced by TSO, and all runs were executed serially.…”

Section: Results On the Pyasukp Datasetmentioning

confidence: 99%

“…6 The authors of this article reinvented an algorithm from Gilmore & Gomory (1966) and published a paper calling it UKP5 while believing it was novel Becker & Buriol (2016). The…”

Section: Algorithms Deliberately Ignoredmentioning

confidence: 99%

“…The PYAsUKP dataset is described in Becker & Buriol (2016), and comprises 4540 instances from five smaller datasets. The PYAsUKP dataset was heavily based on the datasets presented in Poirriez et al (2009), which were used to compare EDUK2 to other UKP solving algorithms.…”

Section: Pyasukp Datasetmentioning

confidence: 99%

“…The UKP5 algorithm proposed in Becker & Buriol (2016) was found to be very similar to TSO 6 and therefore only TSO was included. 4 The code is available at Henrique Becker master's thesis code repository (https://github.com/henriquebecker91/masters/blob/f5bbabf47d6852816615315c883 9d3f74013af5f/codes/ocaml/pyasukp_mail.tgz).…”

Section: Algorithms Deliberately Ignoredmentioning

confidence: 99%

“…EDUK2 is compared to EDUK. Becker & Buriol (2016) The terminating step-off is reinvented (with the name of UKP5) and outperforms the hybrid method in the updated datasets.…”

Section: Introductionmentioning

confidence: 99%

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An empirical analysis of exact algorithms for the unbounded knapsack problem

Becker

Buriol

2019

European Journal of Operational Research

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This work presents an empirical analysis of exact algorithms for the unbounded knapsack problem, which includes seven algorithms from the literature, two commercial solvers, and more than ten thousand instances. The terminating stepoff, a dynamic programming algorithm from 1966, presented the lowest mean time to solve the most recent benchmark from the literature. The threshold and collective dominances are properties of the unbounded knapsack problem first discussed in 1998, and exploited by the current state-of-the-art algorithms. The terminating step-off algorithm did not exploit such dominances, but has an alternative mechanism for dealing with dominances which does not explicitly exploits collective and threshold dominances. Also, the pricing subproblems found when solving hard cutting stock problems with column generation can cause branch-and-bound algorithms to display worst-case times. The authors present a new class of instances which favors the branch-and-bound approach over the dynamic programming approach but do not have high amounts of simple, multiple and collective dominated items. This behaviour illustrates how the definition of hard instances changes among algorithm approachs. The codes used for solving the unbounded knapsack problem and for instance generation are all available online.

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Section: Results On the Pyasukp Datasetmentioning

confidence: 99%

“…6 The authors of this article reinvented an algorithm from Gilmore & Gomory (1966) and published a paper calling it UKP5 while believing it was novel Becker & Buriol (2016). The…”

Section: Algorithms Deliberately Ignoredmentioning

confidence: 99%

Section: Pyasukp Datasetmentioning

confidence: 99%

Section: Algorithms Deliberately Ignoredmentioning

confidence: 99%

“…EDUK2 is compared to EDUK. Becker & Buriol (2016) The terminating step-off is reinvented (with the name of UKP5) and outperforms the hybrid method in the updated datasets.…”

Section: Introductionmentioning

confidence: 99%

See 3 more Smart Citations