Well-Posedness and Primal-Dual Analysis of Some Convex Separable Optimization Problems
Author(s) -
Stefan M. Stefanov
Publication year - 2013
Publication title -
advances in operations research
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.379
H-Index - 14
eISSN - 1687-9155
pISSN - 1687-9147
DOI - 10.1155/2013/279030
Subject(s) - mathematics , separable space , convex analysis , augmented lagrangian method , hadamard transform , mathematical optimization , regular polygon , saddle point , convex optimization , dual (grammatical number) , set (abstract data type) , optimization problem , subderivative , computer science , mathematical analysis , art , geometry , literature , programming language
We focus on some convex separable optimization problems, considered by the author in previous papers, for which problems, necessary and sufficient conditions or sufficient conditions have been proved, and convergent algorithms of polynomial computational complexity have been proposed for solving these problems. The concepts of well-posedness of optimization problems in the sense of Tychonov, Hadamard, and in a generalized sense, as well as calmness in the sense of Clarke, are discussed. It is shown that the convex separable optimization problems under consideration are calm in the sense of Clarke. The concept of stability of the set of saddle points of the Lagrangian in the sense of Gol'shtein is also discussed, and it is shown that this set is not stable for the “classical” Lagrangian. However, it turns out that despite this instability, due to the specificity of the approach, suggested by the author for solving problems under consideration, it is not necessary to use modified Lagrangians but only the “classical” Lagrangians. Also, a primal-dual analysis for problems under consideration in view of methods for solving them is presented.
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