In SDP relaxations, inaccurate solvers do robust optimization

Jean-Bernard Lasserre 1 Victor Magron 1, 2
1 LAAS-MAC - Équipe Méthodes et Algorithmes en Commande
LAAS - Laboratoire d'analyse et d'architecture des systèmes [Toulouse]
Abstract : We interpret some wrong results (due to numerical inaccuracies) already observed when solving SDP-relaxations for polynomial optimization on a double precision floating point SDP solver. It turns out that this behavior can be explained and justified satisfactorily by a relatively simple paradigm. In such a situation, the SDP solver (and not the user) performs some `robust optimization' without being told to do so. Instead of solving the original optimization problem with nominal criterion $f$, it uses a new criterion $\tilde{f}$ which belongs to a ball $\mathbf{B}_\infty(f,\varepsilon)$ of small radius $\varepsilon>0$, centered at the nominal criterion $f$ in the parameter space. In other words the resulting procedure can be viewed as a `$\max-\min$' robust optimization problem with two players (the solver which maximizes on $\mathbf{B}_\infty(f,\varepsilon)$ and the user who minimizes over the original decision variables). A mathematical rationale behind this `autonomous' behavior is described.
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Contributor : Victor Magron <>
Submitted on : Thursday, November 8, 2018 - 10:33:01 AM
Last modification on : Friday, April 12, 2019 - 4:23:46 PM

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  • HAL Id : hal-01915976, version 1
  • ARXIV : 1811.02879


Jean-Bernard Lasserre, Victor Magron. In SDP relaxations, inaccurate solvers do robust optimization. 2018. ⟨hal-01915976⟩



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