Approximate Joint Singular Value Decomposition Algorithm Based on Givens-Like Rotation

An approximate joint singular value decomposition algorithm is proposed for a set of K(K ≥ 2) complex matrices. It can be seen as an orthogonal non-Hermitian approximate joint diagonalization algorithm. We exploit a Givens-like rotation method based on a special parameterization of the updating matrices and a reasonable approximation. The main points consist of the presentation of the new parameter structure, analytical derivation of the elementary updating matrices. High accuracy and fast convergence rate are obtained. Numerical simulations illustrate the overall good performance of the proposed algorithm.