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Articles

L0-convex compactness and its applications to random convex optimization and random variational inequalities

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Pages 937-971 | Received 22 Jul 2019, Accepted 25 Jan 2020, Published online: 13 Feb 2020
 

ABSTRACT

First, this paper introduces the notion of L0-convex compactness for a special class of closed convex subsets–closed L0-convex subsets of a Hausdorff topological module over the topological algebra L0(F,K), where L0(F,K) is the algebra of equivalence classes of random variables from a probability space (Ω,F,P) to the scalar field K of real numbers or complex numbers, endowed with the topology of convergence in probability. Then, this paper continues to develop the theory of L0-convex compactness by establishing various kinds of characterization theorems on L0-convex compactness for closed L0-convex subsets of a class of important topological modules – complete random normed modules, in particular, we make full use of the theory of random conjugate spaces to establish the characterization theorem of James type on L0-convex compactness for a closed L0-convex subset of a complete random normed module, which also surprisingly implies that our notion of L0-convex compactness coincides with Gordan Žitković's notion of convex compactness in the context of a closed L0-convex subset of a complete random normed module. As the first application of our results, we give a fundamental theorem on random convex optimization (or, L0-convex optimization), which includes Hansen and Richard's famous result as a special case. As the second application, we give an existence theorem of solutions of random variational inequalities, which generalizes H. Brezis' classical result from a reflexive Banach space to a random reflexive complete random normed module. It should be emphasized that a new method, namely the L0-convex compactness method, is presented for the second application since the usual weak compactness method is no longer applicable in the present case. Besides, our fundamental theorem on random convex optimization can be also applied in the study of optimization problems of conditional convex risk measures, which will be given in our future papers.

2010 MATHEMATICS SUBJECT CLASSIFICATIONS:

Acknowledgments

The authors would also like to thank Professors Hongkun Xu and George Yuan for some valuable suggestions which considerably improve the readability of this paper.

Disclosure statement

No potential conflict of interest was reported by the author(s).

Additional information

Funding

The authors are supported by the NNSF of China [grant number 11571369], [grant number 11701531].

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