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Topics in convex and mixed binary linear optimization


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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.
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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.
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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.
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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

 

 

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