Solving infinite-dimensional optimization problems by polynomial approximation

Devolder, Olivier [UCL] Glineur, François [UCL] Nesterov, Yurii [UCL]

We solve a class of convex infinite-dimensional optimization problems using a numerical approximation method that does not rely on discretization. Instead, we restrict the decision variable to a sequence of finite-dimensional linear subspaces of the original infinite-dimensional space and solve the corresponding finite-dimensional problems in an efficient way using structured convex optimization techniques. We prove that, under some reasonable assumptions, the sequence of these optimal values converges to the optimal value of the original infinite-dimensional problem and give an explicit description of the corresponding rate of convergence.

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Document type	Contribution à ouvrage collectif (Book Chapter) – Chapitre
Access type	Accès restreint
Publication date	2010
Language	Anglais
Host document	M. Diehl, F. Glineur, E. Jarlebring and W. Michiels ; "Recent Advances in Optimization and its Applications in Engineering"- p. 37-46 (ISBN : 978-3-642-12597-3)
Publisher	Springer (Heidelberg)
Publication status	Publié
Affiliations	UCL - SSH/LIDAM/CORE - Center for operations research and econometrics UCL - SST/ICTM/INMA - Pôle en ingénierie mathématique
Links	http://hdl.handle.net/2078.1/94925[Handle] https://doi.org/10.1007/978-3-642-12598-0_3[DOI]

See also
	[boreal:33269]	Solving infinite-dimensional optimization problems by polynomial approximation