| Budget Amount *help |
¥2,340,000 (Direct Cost: ¥1,800,000、Indirect Cost: ¥540,000)
Fiscal Year 2012: ¥1,300,000 (Direct Cost: ¥1,000,000、Indirect Cost: ¥300,000)
Fiscal Year 2011: ¥1,040,000 (Direct Cost: ¥800,000、Indirect Cost: ¥240,000)
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| Research Abstract |
Matrix singular value decomposition plays an important role in many application areas. In this study, we have improved the differential quotient difference with shifts (dqds) algorithm, which is one of the most efficient iterative algorithms of bidiagonal SVD. We have incorporated a new aggressive deflation into the dqds. Our algorithm is faster than the original algorithm. Also we showed convergence rates of the dqds. Furthermore, we proposed a new multishift QR algorithm for symmetric eigenvalue problems. We also proved its global convergence.
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