Fast construction of hierarchical matrix representation from matrix-vector multiplication

We develop a hierarchical matrix construction algorithm using matrix-vector multiplications, based on the randomized singular value decomposition of low-rank matrices. The algorithm uses $\mathcal{O}(\log n)$ applications of the matrix on structured random test vectors and $\mathcal{O}(n\log n)$ extra computational cost, where $n$ is the dimension of the unknown matrix. Numerical examples on constructing Green’s functions for elliptic operators in two dimensions show efficiency and accuracy of the proposed algorithm.