---
_id: '917'
abstract:
- lang: eng
  text: We  propose  a  general  dual  ascent  framework  for  Lagrangean decomposition
    of combinatorial problems.  Although methods of this type have shown their efficiency
    for a number of problems, so far there was no general algorithm applicable to
    multiple problem types. In this work, we propose such a general algorithm. It
    depends on several parameters, which can be used to optimize its performance in
    each particular setting. We demonstrate efficacy of our method on graph matching
    and multicut problems, where it outperforms state-of-the-art solvers including
    those based on subgradient optimization and off-the-shelf linear programming solvers.
article_processing_charge: No
author:
- first_name: Paul
  full_name: Swoboda, Paul
  id: 446560C6-F248-11E8-B48F-1D18A9856A87
  last_name: Swoboda
- first_name: Jan
  full_name: Kuske, Jan
  last_name: Kuske
- first_name: Bogdan
  full_name: Savchynskyy, Bogdan
  last_name: Savchynskyy
citation:
  ama: 'Swoboda P, Kuske J, Savchynskyy B. A dual ascent framework for Lagrangean
    decomposition of combinatorial problems. In: Vol 2017. IEEE; 2017:4950-4960. doi:<a
    href="https://doi.org/10.1109/CVPR.2017.526">10.1109/CVPR.2017.526</a>'
  apa: 'Swoboda, P., Kuske, J., &#38; Savchynskyy, B. (2017). A dual ascent framework
    for Lagrangean decomposition of combinatorial problems (Vol. 2017, pp. 4950–4960).
    Presented at the CVPR: Computer Vision and Pattern Recognition, Honolulu, HA,
    United States: IEEE. <a href="https://doi.org/10.1109/CVPR.2017.526">https://doi.org/10.1109/CVPR.2017.526</a>'
  chicago: Swoboda, Paul, Jan Kuske, and Bogdan Savchynskyy. “A Dual Ascent Framework
    for Lagrangean Decomposition of Combinatorial Problems,” 2017:4950–60. IEEE, 2017.
    <a href="https://doi.org/10.1109/CVPR.2017.526">https://doi.org/10.1109/CVPR.2017.526</a>.
  ieee: 'P. Swoboda, J. Kuske, and B. Savchynskyy, “A dual ascent framework for Lagrangean
    decomposition of combinatorial problems,” presented at the CVPR: Computer Vision
    and Pattern Recognition, Honolulu, HA, United States, 2017, vol. 2017, pp. 4950–4960.'
  ista: 'Swoboda P, Kuske J, Savchynskyy B. 2017. A dual ascent framework for Lagrangean
    decomposition of combinatorial problems. CVPR: Computer Vision and Pattern Recognition
    vol. 2017, 4950–4960.'
  mla: Swoboda, Paul, et al. <i>A Dual Ascent Framework for Lagrangean Decomposition
    of Combinatorial Problems</i>. Vol. 2017, IEEE, 2017, pp. 4950–60, doi:<a href="https://doi.org/10.1109/CVPR.2017.526">10.1109/CVPR.2017.526</a>.
  short: P. Swoboda, J. Kuske, B. Savchynskyy, in:, IEEE, 2017, pp. 4950–4960.
conference:
  end_date: 2017-07-26
  location: Honolulu, HA, United States
  name: 'CVPR: Computer Vision and Pattern Recognition'
  start_date: 2017-07-21
corr_author: '1'
date_created: 2018-12-11T11:49:11Z
date_published: 2017-07-01T00:00:00Z
date_updated: 2024-11-04T13:52:34Z
day: '01'
ddc:
- '000'
department:
- _id: VlKo
doi: 10.1109/CVPR.2017.526
ec_funded: 1
external_id:
  isi:
  - '000418371405005'
file:
- access_level: open_access
  checksum: 72fd291046bd8e5717961bd68f6b6f03
  content_type: application/pdf
  creator: dernst
  date_created: 2019-01-18T12:45:55Z
  date_updated: 2020-07-14T12:48:15Z
  file_id: '5847'
  file_name: 2017_CVPR_Swoboda.pdf
  file_size: 898652
  relation: main_file
file_date_updated: 2020-07-14T12:48:15Z
has_accepted_license: '1'
intvolume: '      2017'
isi: 1
language:
- iso: eng
month: '07'
oa: 1
oa_version: Submitted Version
page: 4950-4960
project:
- _id: 25FBA906-B435-11E9-9278-68D0E5697425
  call_identifier: FP7
  grant_number: '616160'
  name: 'Discrete Optimization in Computer Vision: Theory and Practice'
publication_identifier:
  isbn:
  - 978-153860457-1
publication_status: published
publisher: IEEE
publist_id: '6524'
quality_controlled: '1'
scopus_import: '1'
status: public
title: A dual ascent framework for Lagrangean decomposition of combinatorial problems
type: conference
user_id: c635000d-4b10-11ee-a964-aac5a93f6ac1
volume: 2017
year: '2017'
...