An algorithm for computing the sparse QR decomposition of a specially ordered rectangular matrix is proposed. This decomposition is based on the block sparse Householder transformations. For ordering computations, the nested dissection ordering is used for the matrix A^TA, where A is the original rectangular matrix. For mesh based problems, the ordering can be constructed starting from an appropriate volume partitioning of the computational mesh. Parallel computations are based on sparse QR decomposition for sets of rows with an additional initial zero block.

Keywords: sparse rectangular matrix, upper quasi-triangular matrix, volume partitioning, nested dissection, QR decomposition, Householder transformations, parallel algorithm.

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