In this paper, we consider finding a low-rank approximation to the solution of a large-scale generalized Lyapunov matrix equation in the form of \(A X M + M X A = C\) , where A and M are symmetric positive definite matrices. An algorithm called an Increasing Rank Riemannian Method for Generalized Lyapunov Equation (IRRLyap) is proposed by merging the increasing rank technique and Riemannian optimization techniques on the quotient manifold \({\mathbb {R}}_*^{n \times p} / {\mathcal {O}}_p\) . To efficiently solve the optimization problem on \({\mathbb {R}}_*^{n \times p} / {\mathcal {O}}_p\) , a line-search-based Riemannian inexact Newton’s method is developed with its global convergence and local superlinear convergence rate guaranteed. Moreover, we derive a preconditioner which takes \(M \ne I\) into consideration. Numerical experiments show that the proposed Riemannian inexact Newton’s method and the preconditioner have superior performance, and that IRRLyap is preferable compared to the tested state-of-the-art methods when the lowest rank solution is desired.
