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• W3100861724 startingPage "551" @default.
• W3100861724 abstract "Completely positive (CP) tensors, which correspond to a generalization of CP matrices, allow to reformulate or approximate a general polynomial optimization problem (POP) with a conic optimization problem over the cone of CP tensors. Similarly, completely positive semidefinite (CPSD) tensors, which correspond to a generalization of positive semidefinite (PSD) matrices, can be used to approximate general POPs with a conic optimization problem over the cone of CPSD tensors. In this paper, we study CP and CPSD tensor relaxations for general POPs and compare them with the bounds obtained via a Lagrangian relaxation of the POPs. This shows that existing results in this direction for quadratic POPs extend to general POPs. Also, we provide some tractable approximation strategies for CP and CPSD tensor relaxations. These approximation strategies show that, with a similar computational effort, bounds obtained from them for general POPs can be tighter than bounds for these problems obtained by reformulating the POP as a quadratic POP, which subsequently can be approximated using CP and PSD matrices. To illustrate our results, we numerically compare the bounds obtained from these relaxation approaches on small scale fourth-order degree POPs." @default.
• W3100861724 created "2020-11-23" @default.
• W3100861724 creator A5025392863 @default.
• W3100861724 creator A5049210808 @default.
• W3100861724 date "2017-08-30" @default.
• W3100861724 modified "2023-10-16" @default.
• W3100861724 title "Completely positive and completely positive semidefinite tensor relaxations for polynomial optimization" @default.
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• W3100861724 doi "https://doi.org/10.1007/s10898-017-0558-1" @default.
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