Jacobi-type algorithms for homogeneous polynomial optimization on Stiefel manifolds with applications to tensor approximations
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In this paper, we mainly study the gradient based Jacobi-type algorithms to maximize two classes of homogeneous polynomials with orthogonality constraints, and establish their convergence properties. For the first class of homogeneous polynomials subject to a constraint on a Stiefel manifold, we reformulate it as an optimization problem on a unitary group, which makes it possible to apply the gradient based Jacobi-type (Jacobi-G) algorithm. Then, if the subproblem can be always represented as a quadratic form, we establish the global convergence of Jacobi-G under any one of the three conditions (A1), (A2) and (A3). The convergence result for (A1) is an easy extension of the result in [Usevich et al. SIOPT 2020], while (A2) and (A3) are two new ones. This algorithm and the convergence properties apply to the well-known joint approximate symmetric tensor diagonalization and joint approximate symmetric trace maximization. For the second class of homogeneous polynomials subject to constraints on the product of Stiefel manifolds, we similarly reformulate it as an optimization problem on the product of unitary groups, and then develop a new gradient based multi-block Jacobi-type (Jacobi-MG) algorithm to solve it. We similarly establish the global convergence of Jacobi-MG under any one of the three conditions (A1), (A2) and (A3), if the subproblem can be always represented as a quadratic form. This algorithm and the convergence properties apply to the well-known joint approximate tensor diagonalization and joint approximate tensor compression. As the proximal variants of Jacobi-G and Jacobi-MG, we also propose the Jacobi-GP and Jacobi-MGP algorithms, and establish their global convergence without any further condition. Some numerical results are provided indicating the efficiency of the proposed algorithms.
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