Mathematical Programming

, Volume 146, Issue 1–2, pp 77–96 | Cite as

Sobolev seminorm of quadratic functions with applications to derivative-free optimization

Full Length Paper Series A

Abstract

This paper studies the \(H^1\) Sobolev seminorm of quadratic functions. The research is motivated by the least-norm interpolation that is widely used in derivative-free optimization. We express the \(H^1\) seminorm of a quadratic function explicitly in terms of the Hessian and the gradient when the underlying domain is a ball. The seminorm gives new insights into least-norm interpolation. It clarifies the analytical and geometrical meaning of the objective function in least-norm interpolation. We employ the seminorm to study the extended symmetric Broyden update proposed by Powell. Numerical results show that the new thoery helps improve the performance of the update. Apart from the theoretical results, we propose a new method of comparing derivative-free solvers, which is more convincing than merely counting the numbers of function evaluations.

Keywords

Sobolev seminorm Least-norm interpolation Derivative-free optimization Extended symmetric Broyden update 

Mathematics Subject Classification (2010)

90C56 90C30 65K05 

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Copyright information

© Springer-Verlag Berlin Heidelberg and Mathematical Optimization Society 2013

Authors and Affiliations

  1. 1.Institute of Computational Mathematics and Scientific/Engineering ComputingAcademy of Mathematics and Systems Science, Chinese Academy of SciencesBeijingChina

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