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Pré-Publication, Document De Travail Année : 2021

A hierarchy of spectral relaxations for polynomial optimization

Résumé

We show that (i) any constrained polynomial optimization problem (POP) has an equivalent formulation on a variety contained in an Euclidean sphere and (ii) the resulting semidefinite relaxations in the moment-SOS hierarchy have the constant trace property (CTP) for the involved matrices. We then exploit the CTP to avoid solving the semidefinite relaxations via interior-point methods and rather use ad-hoc spectral methods that minimize the largest eigenvalue of a matrix pencil. Convergence to the optimal value of the semidefinite relaxation is guaranteed. As a result we obtain a hierarchy of nonsmooth "spectral relaxations" of the initial POP. Efficiency and robustness of this spectral hierarchy is tested against several equality constrained POPs on a sphere as well as on a sample of randomly generated quadratically constrained quadratic problems (QCQPs).

Dates et versions

hal-03149938 , version 1 (23-02-2021)

Identifiants

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Ngoc Hoang Anh Mai, Victor Magron, Jean-Bernard Lasserre. A hierarchy of spectral relaxations for polynomial optimization. 2021. ⟨hal-03149938⟩
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