論文 :

 I. Yamada, “The hybrid steepest descent method for the variational inequality problem over the intersection of fixed point sets of nonexpansive mappings,” in : Inherently Parallel Algorithm for Feasibility and Optimization and Their Applications (D.Butnariu, Y.Censor and S.Reich, Eds.),  pp.473-505, Elsevier, 2001.
 I.Yamada, N.Ogura and N.Shirakawa, “A numerically robust hybrid steepest descent method for the convexly constrained generalized inverse problems,” in : Inverse Problems, Image Analysis and Medical Imaging (Z.Nashed and O. Scherzer Eds.), Contemporary Mathematics 313, pp.269–305, American Mathematical Society, 2002.
 I.Yamada and N. Ogura, “Adaptive projected subgradient method for asymptotic minimization of sequence of nonnegative convex functions,” Numerical Functional Analysis and Optimization, vol.25, pp.593-617, 2004.
 M.Yukawa, K.Slavakis and  I.Yamada, “Adaptive parallel quadratic-metric projection algorithms,” IEEE Trans. Audio, Speech and Language Processing, vol.15, pp.1665-1680, 2007.
 K. Slavakis and I. Yamada, “Robust  wideband beamforming by the hybrid steepest descent method,” IEEE Trans. Signal Processing, vol.55, pp.4511-4522, 2007.  
 T.Piotrowski and I.Yamada, “MV-PURE Estimator : minimum-variance pseudo unbiased reduced-rank estimator for linearly constrained ill-conditioned inverse problems,” IEEE Trans. Signal Processing, vol.56,  pp.3408-3423, 2008.
 R. Cavalcante, I. Yamada and  B. Mulgrew, “An adaptive projected subgradient approach to learning in diffusion networks,” IEEE Trans. Signal Processing, vol.57, pp.2762-2774, 2009.
 N.Takahashi and I.Yamada, “Steady-state mean-square performance analysis of a relaxed set-membership NLMS algorithm by the energy conservation argument,” IEEE Trans. Signal Processing, vol.57, pp.3361-8211;3372, 2009.
 K.Slavakis, S.Theodoridis and I.Yamada, “Adaptive constrained filtering in reproducing kernel Hilbert spaces: the Beamforming Case,” IEEE Trans. Signal Processing, vol.57, pp.4744-4764, 2009.
 S.Gandy and I.Yamada, “Convex Optimization Techniques for the Efficient Recovery of a Sparsely Corrupted Low-rank Matrix,” Journal of Math-for-Industry, volume JMI2010B, 2010.
 I.Yamada and K.Oguchi, “High-Resolution Estimation of the Directions-of-Arrival Distribution by Algebraic Phase Unwrapping Algorithms,” Multidimensional Systems and Signal Processing,(Invited /Accepted), Springer, 2011(2010: online).
 S.Gandy, B. Recht and I.Yamada, “Tensor completion and low-n-rank tensor recovery via convex optimization,” Inverse Problems, vol.27, 2011.

著書:

 山田功, 工学のための関数解析, 数理工学社 (2009)

受賞 :

 電子情報通信学会論文賞 (1991, 1995, 2006, 2009)
 国際コミュニケーション基金優秀研究賞(2004)
 ドコモ・モバイル・サイエンス賞[基礎科学部門](2005)
 藤野研究賞(2008)
 電子情報通信学会業績賞(2009)


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Last-modified: 2016-09-05 (月) 08:55:47 (1714d)