‎A matrix LSQR algorithm for solving constrained linear operator equations

In this work, an iterative method based on a matrix form of LSQR algorithm is constructed for solving the linear operator equation $mathcal{A}(X)=B$ and the minimum Frobenius norm residual problem $||mathcal{A}(X)-B||_F$ where $Xin mathcal{S}:={Xin textsf{R}^{ntimes n}~|~X=mathcal{G}(X)}$, $mathcal{F $ is the linear operator from $textsf{R}^{ntimes n}$ onto $textsf{R}^{rtimes s}$, $mathcal{G}$ is a linear self-conjugate involution operator and $Bin textsf{R}^{rtimes s}$. Numerical examples are given to verify the efficiency of the constructed method.