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In a least squares problem we wish to oeffic jjRx f jj22 QR factorizations are computationally expensive, but when elements are added to or removed from A it is not alwa o incorpor m on a GPU CUDA. These algorithms have been shown to outperform full QR factorization in a serial environment [1], and we ha viously demonstrated that their implementation on a GPU can outperform a serial implementation by a wide mar Other papers have investigated implementing full QR factorization on GPUs, for example by using blocked House transformations [3] or a tile-based approach across multicore CPUs and multiple GPUs [4,5]. Another study achieve