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Approximate solutions of convex semi-infinite optimization problems in finitely many iterations

Approximate solutions of convex semi-infinite optimization problems in finitely many iterations

Authors :
Schmid, Jochen
Poursanidis, Miltiadis
Publication Year :
2021

Abstract

We develop two adaptive discretization algorithms for convex semi-infinite optimization, which terminate after finitely many iterations at approximate solutions of arbitrary precision. In particular, they terminate at a feasible point of the considered optimization problem. Compared to the existing finitely feasible algorithms for general semi-infinite optimization problems, our algorithms work with considerably smaller discretizations and are thus computationally favorable. Also, our algorithms terminate at approximate solutions of arbitrary precision, while for general semi-infinite optimization problems the best possible approximate-solution precision can be arbitrarily bad. All occurring finite optimization subproblems in our algorithms have to be solved only approximately, and continuity is the only regularity assumption on our objective and constraint functions. Applications to parametric and non-parametric regression problems under shape constraints are discussed.<br />Comment: 24 pages

Details

Database :
arXiv
Publication Type :
Report
Accession number :
edsarx.2105.08417
Document Type :
Working Paper