Proximal Point Algorithms for Multi-criteria Optimization with the Difference of Convex Objective Functions

Ying Ji (), Mark Goh and Robert Souza
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Ying Ji: University of Shanghai for Science and Technology
Mark Goh: National University of Singapore
Robert Souza: National University of Singapore

Journal of Optimization Theory and Applications, 2016, vol. 169, issue 1, No 13, 280-289

Abstract: Abstract This paper focuses on solving a class of multi-criteria optimization with the difference of convex objective functions. Proximal point algorithms, extensively studied for scalar optimization, are extended to our setting. We show that the proposed algorithms are well posed and globally convergent to a critical point. For an application, the new methods are used to a multi-criteria model arising in portfolio optimization. The numerical results show the efficiency of our methods.

Keywords: Multi-criteria optimization; DC; Proximal point algorithm; Critical point; Portfolio optimization; 90C29; 90C90 (search for similar items in EconPapers)
Date: 2016
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Citations: View citations in EconPapers (2)

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DOI: 10.1007/s10957-015-0847-0

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