Background: I received my B.S. (1995) and M.S. (1999) from Yonsei University (Korea) and my Ph.D. from University of Washington (U.S.A.) in 2007. My research area is mainly in nonsmooth convex optimization. For more details, see curriculum vitae.
My thesis advisor: Professor Paul Tseng.
My Recent Research Papers
M. Kang, S. Yun, and H. Woo, Two-level convex relaxed variational model for multiplicative denoising, December 2011, submitted to SIAM J. Imaging Sci.
H. Woo and S. Yun, Proximal linearized alternating direction method for multiplicative denoising, June 2011, submitted to SIAM J. Sci. Comput.
S. Yun and H. Woo, A new multiplicative denoising variational model based on m-th root transformation, IEEE Trans. on Image Processing, 21 (2012), 2523 -- 2533.
H. Woo and S. Yun, Alternating minimization algorithm for speckle reduction with shifting technique, IEEE Trans. on Image Processing 21 (2012), 1701 -- 1714. (ama-speckle.pdf)
S. Yun, P. Tseng, and K.-C. Toh, A Block Coordinate Gradient Descent Method for Regularized Convex Separable Optimization and Covariance Selection, Math. Prog. 129 (2011), 331 -- 355. (BCGD.pdf)
Z. Shen, K.-C. Toh, and S. Yun, An Accelerated Proximal Gradient Algorithm for Frame Based Image Restorations via the Balanced Approach, SIAM J. Imaging Sci. 4 (2011), 573 -- 596. (apgimage.pdf)
S. Yun and H. Woo, Linearized Proximal Alternating Minimization Algorithm for Motion Deblurring by Nonlocal Regularization, Pattern Recognition 44 (2011), 1312 -- 1326. (lpama.pdf)
S. Yun and K.-C. Toh, A Coordinate Gradient Descent Method for L1-regularized Convex Minimization, Comput. Optim. Appl. 48 (2011), 273 -- 307. (cgd_l1.pdf, matlab code of CGD method for L1-regularized least linear squares problem, matlab code of CGD method for L1-regularized logistic regression problem)
P. Tseng and S. Yun, A Coordinate Gradient Descent Method for Linearly Constrained Smooth Optimization and Support Vector Machines Training, Comput. Optim. Appl. 47 (2010), 179 -- 206 (svm.pdf)
K.-C. Toh and S. Yun, An Accelerated Proximal Gradient Algorithm for Nuclear Norm Regularized Least Squares Problems, Pacific J. Optim. 6 (2010), 615 -- 640 (mc.pdf, matlab code)
P. Tseng and S. Yun, Block-Coordinate Gradient Descent Method for Linearly Constrained Nonsmooth Separable Optimization, J. Optim. Theory Appl. 140 (2009), 513 -- 535. (cgd_cnobi.pdf)
P. Tseng and S. Yun, A Coordinate Gradient Descent Method for Nonsmooth Separable Minimization, Math. Prog. 117 (2009) 387 -- 423 (cgd.pdf, matlab codeof coordinate gradient descent (CGD) method for unconstrained optimization with l1-regularization)
Recent Talks
talk at KAIST, May 2012
talk at Yonsei University, April 2012
talk at CSE Seminar in Yonsei University, December 2011
talk at Medical Imaging Seminar in Yonsei University, November 2011
talk at Forum "Math-for-Industry" 2011, October 2011
talk at the KSIAM 2010 Annual Meeting, December 2010
talk at the 2010 Global KMS International Conference, October 2010
talk at the Ewha Womans University, April 2010
talk1, talk2 at the 2009 NIMS Thematic Winter School, December 2009
talk at the 2009 Workshop on Nonlinear analysis and Optimization, November 2009
talk at the Chungbuk National University and the Kyungpook National University, June 2009
talk at the Kyungpook National University, June 2009
talk at the Singapore-MIT Alliance 10th Anniversary Symposium, January 2009
talk at the INFORMS Annual Meeting, November 2007
talk at the Second Mathematical Programming Society International Conference on Continuous Optimization, August 2007
talk at the 19th International Symposium on Mathematical Programming, July 2006
talk at the eighth SIAM Conference on Optimization, May 2005
Other sites
The R Project for Statistical Computing