Multi-ordered Lasserre hierarchy for large scale polynomial optimization in real and complex variables

Cédric Josz 1 Daniel Molzahn 2
1 LAAS-MAC - Équipe Méthodes et Algorithmes en Commande
LAAS - Laboratoire d'analyse et d'architecture des systèmes [Toulouse]
Abstract : We propose general notions to deal with large scale polynomial optimization problems and demonstrate their efficiency on a key industrial problem of the twenty first century, namely the optimal power flow problem. These notions enable us to find global minimizers on instances with up to 4,500 variables and 14,500 constraints. First, we generalize the Lasserre hierarchy from real to complex to numbers in order to enhance its tractability when dealing with complex polynomial optimization. Complex numbers are typically used to represent oscillatory phenomena, which are omnipresent in physical systems. Using the notion of hyponormality in operator theory, we provide a finite convergence criterion which generalizes the Curto-Fialkow conditions of the real Lasserre hierarchy. Second, we introduce the multi-ordered Lasserre hierarchy in order to exploit sparsity in polynomial optimization problems (in real or complex variables) while preserving global convergence. It is based on two ideas: 1) to use a different relaxation order for each constraint, and 2) to iteratively seek a closest measure to the truncated moment data until a measure matches the truncated data. Third and last, we exhibit a block diagonal structure of the Lasserre hierarchy in the presence of commonly encountered symmetries.
Type de document :
Pré-publication, Document de travail
Rapport LAAS n° 17349. 2017
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https://hal.laas.fr/hal-01592093
Contributeur : Jean Bernard Lasserre <>
Soumis le : vendredi 22 septembre 2017 - 15:48:32
Dernière modification le : mercredi 28 février 2018 - 10:23:11

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  • HAL Id : hal-01592093, version 1
  • ARXIV : 1709.04376

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Cédric Josz, Daniel Molzahn. Multi-ordered Lasserre hierarchy for large scale polynomial optimization in real and complex variables. Rapport LAAS n° 17349. 2017. 〈hal-01592093〉

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