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`http://hdl.handle.net/2077/38634` |

**Files in This Item:**

File | Description | Size | Format | |
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gupea_2077_38634_1.pdf | Thesis frame | 369Kb | Adobe PDF | View/Open |

gupea_2077_38634_2.pdf | Abstract | 44Kb | Adobe PDF | View/Open |

Title: | Topics in convex and mixed binary linear optimization |

Authors: | Gustavsson, Emil |

E-mail: | gusemil@gmail.com |

Issue Date: | 8-May-2015 |

University: | Göteborgs universitet. Naturvetenskapliga fakulteten |

Institution: | Department of Mathematical Sciences ; Institutionen för matematiska vetenskaper |

Parts of work: | I. Gustavsson, E., Patriksson, M., Strömberg, A-.B., Primal convergence from dual subgradient methods for convex optimization, Mathematical Programming. 2015;150(2):365-390. VIEW ARTICLE II. Önnheim, M., Gustavsson, E., Strömberg, A-.B., Patriksson, M., Larsson, T., Ergodic, primal convergence in dual subgradient schemes for convex programming, II---the case of inconsistent primal problems. III. Gustavsson, E., Larsson, T., Patriksson, M., Strömberg, A-.B., Recovery of primal solutions from dual subgradient methods for mixed binary linear programs. IV. Gustavsson, E., Patriksson, M., Strömberg, A-.B., Wojciechowski, A., Önnheim, M., Preventive maintenance scheduling of multi-component systems with interval costs, Computers and Industrial Engineering. 2014;76:390-400. VIEW ARTICLE V. Gustavsson, E., Scheduling tamping operations on railway tracks using mixed integer linear programming, EURO Journal on Transportation and Logistics. 2015;4(1):97-112. VIEW ARTICLE |

Date of Defence: | 2015-05-29 |

Disputation: | Fredagen den 29 maj 2015, kl 13.15, Pascal, Matematiska vetenskaper, Chalmers Tvärgata 3 |

Degree: | Doctor of Philosophy |

Publication type: | Doctoral thesis |

Keywords: | subgradient methods Lagrangian dual recovery of primal solutions inconsistent convex programs ergodic sequences convex optimization mixed binary linear optimization maintenance scheduling preventive maintenance deterioration cost |

Abstract: | This thesis concerns theory, algorithms, and applications for two problem classes within the realm of mathematical optimization; convex optimization and mixed binary linear optimization. To the thesis is appended five papers containing its main contributions. In the first paper a subgradient optimization method is applied to the Lagrangian dual of a general convex and (possibly) nonsmooth optimization problem. The classic dual subgradient method produces primal solutions that are, however, neit... more |

ISBN: | 978-91-628-9410-8 |

URI: | http://hdl.handle.net/2077/38634 |

Appears in Collections: | Doctoral Theses from University of Gothenburg / Doktorsavhandlingar från Göteborgs universitet Doctoral Theses / Doktorsavhandlingar Institutionen för matematiska vetenskaper |