| Budget Amount *help |
¥17,680,000 (Direct Cost: ¥13,600,000、Indirect Cost: ¥4,080,000)
Fiscal Year 2013: ¥3,510,000 (Direct Cost: ¥2,700,000、Indirect Cost: ¥810,000)
Fiscal Year 2012: ¥3,510,000 (Direct Cost: ¥2,700,000、Indirect Cost: ¥810,000)
Fiscal Year 2011: ¥3,510,000 (Direct Cost: ¥2,700,000、Indirect Cost: ¥810,000)
Fiscal Year 2010: ¥3,380,000 (Direct Cost: ¥2,600,000、Indirect Cost: ¥780,000)
Fiscal Year 2009: ¥3,770,000 (Direct Cost: ¥2,900,000、Indirect Cost: ¥870,000)
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| Research Abstract |
In this research project, we have tackled the hierarchical optimization problem and its related topics.In the 1st part,motivated by recent elegant characterizations of the solution sets of convex optimization problems as the fixed point sets of computable nonexpansive mappings, we present an algorithmic selection of an optimal point in the solution set of a convex optimization problem based on the hybrid steepest descent method and the Moreau regularization. In the process, we also developed proximal type algorithms for the sparsity aware adaptive learning problems, convex optimization algorithms for the tensor completion problems.In the 2nd part, we tackled mainly case studies of the hierarchical nonconvex optimization problems. These include, e.g., the optimal rank selection of the minimum-variance pseudo-unbiased reduced-rank estimator (MV-PURE) for ill-conditioned linear inverse problems. and the generalized eigenvalue problem for efficient adaptive subspace extraction.
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