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The presence of rounding errors is frequently inevitable when performing arithmetic operations in computers due to the use of floating-point number system whose elements have finite precision. Consequently, error-free transformation algorithms are proposed as solutions, and a particular instance of these is the error-free transformation algorithm for real matrix multiplication. Based on the previously mentioned algorithm, this study proposes two error-error free transformation algorithms for complex matrix multiplication. By conducting the numerical experiment, one finds that the combination of both proposed algorithms with an accurate summation algorithm generates the most accurate results, but it also suffers from a notable increase in the computing time ratio. On the other hand, the second proposed algorithm, which is simply another form of the first one with the change of order in summation, combined with the pure floating-point addition produces results that are nearly as accurate as those generated by the former combination. Since these results are achieved with significantly smaller computing time ratio, then the study concludes that the last combination is the best method for complex matrix multiplication in terms of accuracy and computing time efficiency.

This paper discusses accurate numerical algorithms for matrix multiplication. Matrix multiplication is a basic and important problem in numerical linear algebra. Numerical computations using floating-point arithmetic can be quickly performed on existing computers. However, the accumulation of rounding errors due to finite precision arithmetic is a critical problem. An error-free transformation for matrix multiplication is reviewed in this paper. Such a transformation is extremely useful for developing accurate numerical algorithms for matrix multiplication. One advantage of the transformation is that it exploits Basic Linear Algebra Subprograms (BLAS). We provide a rounding error analysis of reproducible algorithms for matrix multiplication based on the error-free transformation. In addition, we propose an error-free transformation for matrix multiplication that can be utilized with the divide and conquer methods.

This paper concerns thin QR decomposition in an oblique inner product. Cholesky QR is known as a fast algorithm for thin QR decomposition. On the other hand, this algorithm is not applicable for ill-conditioned matrices. The preconditioned Cholesky QR algorithms named LU-Cholesky QR for thin QR decomposition are recently proposed. We apply these preconditioning techniques to thin QR decomposition in an oblique inner product.

Although numerical computation is very fast, however, the results may not be accurate due to the accumulation of rounding errors. Consequently, much research has focussed on ways to verifying the accuracy of approximate solutions. Floating-point filters are one such technique. These can, for example, be used to guarantee the signs of computed results, such as those of the matrix determinants that are so important in the computational geometry field. In this paper, we extend floating-point filters to guarantee absolute and relative errors.

The numerical computations with guaranteed accuracy called verified numerical computations are studied, herein. We focus on an accurate numerical solution for dense linear systems of the form Ax = b and error bound of ‖ x ^ − x ‖ ∞ in supercomputing, where x ^ is an approximate solution. First, we implemented accurate algorithms for a matrix-vector product for parallel and distributed computing, and then, applied them to iterative refinements. Next, we implemented verified numerical computations using the Parallel Basic Linear Algebra Subprograms (PBLAS) and the Scalable Linear Algebra Package library (ScaLAPACK). Numerical examples of the verified numerical computations for linear systems are presented using the Fujitsu FX100 supercomputer. They clearly illustrate the efficiency of the verified numerical computations.