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A robustification approach in unconstrained quadratic optimization

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Abstract

Unconstrained convex quadratic optimization problems subject to parameter perturbations are considered. A robustification approach is proposed and analyzed which reduces the sensitivity of the optimal function value with respect to the parameter. Since reducing the sensitivity and maintaining a small objective value are competing goals, strategies for balancing these two objectives are discussed. Numerical examples illustrate the approach.

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Authors and Affiliations

  1. Faculty of Mathematics, Chemnitz University of Technology, 09107, Chemnitz, Germany

    Martin K. Bernauer & Roland Griesse

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  1. Martin K. Bernauer
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  2. Roland Griesse
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Correspondence to Martin K. Bernauer.

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Bernauer, M.K., Griesse, R. A robustification approach in unconstrained quadratic optimization. Math. Program. 128, 231–252 (2011). https://doi.org/10.1007/s10107-009-0302-9

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  • DOI: https://doi.org/10.1007/s10107-009-0302-9

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