Handling hard affine SDP shape constraints in RKHSs

Pierre-Cyril Aubin-Frankowski and Zoltan Szabo

LSE Research Online Documents on Economics from London School of Economics and Political Science, LSE Library

Abstract: Shape constraints, such as non-negativity, monotonicity, convexity or supermodularity, play a key role in various applications of machine learning and statistics. However, incorporating this side information into predictive models in a hard way (for example at all points of an interval) for rich function classes is a notoriously challenging problem. We propose a unified and modular convex optimization framework, relying on second-order cone (SOC) tightening, to encode hard affine SDP constraints on function derivatives, for models belonging to vector-valued reproducing kernel Hilbert spaces (vRKHSs). The modular nature of the proposed approach allows to simultaneously handle multiple shape constraints, and to tighten an infinite number of constraints into finitely many. We prove the convergence of the proposed scheme and that of its adaptive variant, leveraging geometric properties of vRKHSs. Due to the covering-based construction of the tightening, the method is particularly well-suited to tasks with small to moderate input dimensions. The efficiency of the approach is illustrated in the context of shape optimization, robotics and econometrics.

Keywords: vector-valued reproducing kernel Hilbert space; shape-constrained optimization; matrix-valued kernel; kernel derivatives (search for similar items in EconPapers)
JEL-codes: C1 (search for similar items in EconPapers)
Pages: 51 pages
Date: 2022-10
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Published in Journal of Machine Learning Research, October, 2022. ISSN: 1532-4435

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Persistent link: https://EconPapers.repec.org/RePEc:ehl:lserod:115724

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