| Budget Amount *help |
¥4,940,000 (Direct Cost: ¥3,800,000、Indirect Cost: ¥1,140,000)
Fiscal Year 2016: ¥1,170,000 (Direct Cost: ¥900,000、Indirect Cost: ¥270,000)
Fiscal Year 2015: ¥1,430,000 (Direct Cost: ¥1,100,000、Indirect Cost: ¥330,000)
Fiscal Year 2014: ¥1,040,000 (Direct Cost: ¥800,000、Indirect Cost: ¥240,000)
Fiscal Year 2013: ¥1,300,000 (Direct Cost: ¥1,000,000、Indirect Cost: ¥300,000)
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| Outline of Final Research Achievements |
We have proposed the proximal coordinate gradient method for large-scale convex optimization arisen in statics, signal processing, machine learning, and so on. The proposed method is a generalization of a variety of optimization methods, such as the proximal gradient method, the Newton method, and the coordinate descent method. We have investigated convergence properties of the proposed method. In particular we have given sufficient condition under which the proposed method converges globally and linearly. Moreover we have presented its worst iteration complexity. We have applied it to some applications such as the L1-L2 optimization and the portfolio selection problem, and found that the proposed method can find a reasonable solution efficiently.
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