A proximal alternating minimization algorithm for the largest C-eigenvalue of piezoelectric-type tensors

C-eigenvalues of piezoelectric-type tensors play an important role in piezoelectric effect and converse piezoelectric effect. While the largest C-eigenvalue of a given piezoelectric-type tensor has concrete physical meaning which determines the highest piezoelectric coupling constant. In this paper,...

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Vydáno v:Journal of global optimization Ročník 87; číslo 2-4; s. 405 - 422
Hlavní autoři: Wang, Wenjie, Chen, Haibin, Wang, Yiju, Zhou, Guanglu
Médium: Journal Article
Jazyk:angličtina
Vydáno: New York Springer US 01.11.2023
Springer
Témata:
Algorithms
Computer Science
Mathematics
Mathematics and Statistics
Operations Research/Decision Theory
Optimization
Real Functions
15H18
C-eigenvalue
90C30
Polynomial optimization
Piezoelectric-type tensor
Alternating minimization method
65H17
ISSN:0925-5001, 1573-2916
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Shrnutí:C-eigenvalues of piezoelectric-type tensors play an important role in piezoelectric effect and converse piezoelectric effect. While the largest C-eigenvalue of a given piezoelectric-type tensor has concrete physical meaning which determines the highest piezoelectric coupling constant. In this paper, we focus on computing the maximum C-eigenvalue of piezoelectric-type tensors which is a third degree polynomial problem. To do that, we first establish the equivalence between the proposed polynomial optimization problem (POP) and a multi-linear optimization problem (MOP) under conditions that the original objective function is concave. Then, an augmented POP (which can also be regarded as a regularized POP) is introduced for the purpose to guarantee the concavity of the underlying objective function. Theoretically, both the augmented POP and the original problem share the same optimal solutions when the compact sets are specified as unit spheres. By exploiting the multi-block structure of the resulting MOP, we accordingly propose a proximal alternating minimization algorithm to get an approximate optimal value of the maximum C-eigenvalue. Furthermore, convergence of the proposed algorithm is established under mild conditions. Finally, some preliminary computational results on synthetic data sets are reported to show the efficiency of the proposed algorithm.
ISSN:0925-5001
1573-2916
DOI:10.1007/s10898-022-01180-w