(A) Journal Papers

  1. Y. Chen, W. W. Hager, F. Huang, D. T. Phan, X. Ye, and W. Yin. Fast algorithms for image reconstruction with application to partially parallel MR imaging. To appear in SIAM Journal on Imaging Sciences, 2011. [pdf].
  2. J. Meng, W. Yin, Y. Li, N. Nguyen, and Z. Han. Compressive sensing based high resolution channel estimation for OFDM system. To appear in IEEE Journal of Selected Topics in Signal Processing, Special Issue on Robust Measures and Tests Using Sparse Data for Detection and Estimation, 2011. [pdf].
  3. J. Laska, Z. Wen, W. Yin, and R. Baraniuk. Trust, but verify: Fast and accurate signal recovery from 1-bit compressive measurements. To appear in IEEE Transactions on Signal Processing, 2011. [pdf].
  4. L. Qu and W. Yin. Copula density estimation by total variation penalized likelihood with linear equality. To appear in Computational Statistics and Data Analysis, 2011. [pdf].
  5. Z. Wen, W. Yin, H. Zhang, and D. Goldfarb. On the convergence of an active set method for l1-minimization. To appear in Optimization Methods and Software, 2011. [pdf] [website/code].
  6. J. Meng, W. Yin, H. Li, E. Houssain, and Z. Han. Collaborative spectrum sensing from sparse observations for cognitive radio networks. IEEE Journal on Selected Areas in Communications, Special Issue on Advances in Cognitive Radio Networking and Communications, 29(2):327–337, 2011. [pdf].
  7. Z. Wen, D. Goldfarb, and W. Yin. Alternating direction augmented Lagrangian methods for semidefinite programming. Mathematical Programming Computation, 2(3-4):203–230, 2010. [pdf] [code] [data].
  8. Y. Wang and W. Yin. Sparse signal reconstruction via iterative support detection. SIAM Journal on Imaging Sciences, 3(3):462–491, 2010. [pdf] [website/code].
  9. W. Yin. Analysis and generalizations of the linearized Bregman method. SIAM Journal on Imaging Sciences, 3(4):856–877, 2010. [pdf].
  10. F. Huang, Y. Chen, W. Yin, W. Lin, X. Ye, W. Guo, and A. Reykowski. A rapid and robust numerical algorithm for sensitivity encoding with sparsity constraints: Self-feeding sparse SENSE. Magnetic Resonance in Medicine, 64(4):1078–1088, 2010. [pdf].
  11. J. Yang, Y. Zhang, and W. Yin. A fast alternating direction method for TVL1-L2 signal reconstruction from partial fourier data. IEEE Journal of Selected Topics in Signal Processing, Special Issue on Compressed Sensing, 4(2):288–297, 2010. [pdf] [website/code].
  12. S. Osher, Y. Mao, B. Dong, and W. Yin. Fast linearized Bregman iteration for compressive sensing and sparse denoising. Communications in Mathematical Sciences, 8(1):93–111, 2010. [pdf].
  13. Q. Li, W. Yin, and Z. Deng. Image-based face illumination transferring using the logarithmic total variation model. The Visual Computer, 26(1):41–49, 2010. [pdf].
  14. E. T. Hale, W. Yin, and Y. Zhang. Fixed-point continuation applied to compressed sensing: Implementation and numerical experiments. Journal of Computational Mathematics, 28(2):170–194, 2010. [pdf] [website/code].
  15. D. Goldfarb and W. Yin. Parametric maximum flow algorithms for fast total variation minimization. SIAM Journal on Scientific Computing, 31(5):3712–3743, 2009. [pdf] [website/code].
  16. J. Yang, Y. Zhang, and W. Yin. An efficient TVL1 algorithm for deblurring multichannel images corrupted by impulsive noise. SIAM Journal on Scientific Computing, 31(4):2842–2865, 2009. [pdf] [website/code].
  17. J. Yang, W. Yin, Y. Zhang, and Y. Wang. A fast algorithm for edge-preserving variational multichannel image restoration. SIAM Journal on Imaging Sciences, 2(2):569–592, 2009. [pdf] [website/code].
  18. Z. Wen, W. Yin, D. Goldfarb, and Y. Zhang. A fast algorithm for sparse reconstruction based on shrinkage, subspace optimization and continuation. SIAM Journal on Scientific Computing, 32(4): 1832–1857, 2009. [pdf] [website/code].
  19. J. Shi, W. Yin, S. Osher, and P. Sajda. A fast hybrid algorithm for large scale L1-regularized logistic regression. Journal of Machine Learning Research, 11:713–741, 2008. [pdf].
  20. Y. Wang, J. Yang, W. Yin, and Y. Zhang. A new alternating minimization algorithm for total variation image reconstruction. SIAM Journal on Imaging Sciences, 1(3):248–272, 2008. [pdf] [website/code].
  21. D. Goldfarb, Z. Wen, and W. Yin. A curvilinear search method for p-harmonic flows on spheres. SIAM Journal on Imaging Sciences, 2(1):84–109, 2008. [pdf] [code].
  22. W. Yin, S. Osher, D. Goldfarb, and J. Darbon. Bregman iterative algorithms for l1-minimization with applications to compressed sensing. SIAM Journal on Imaging Sciences, 1(1):143–168, 2008. [pdf] [website/code].
  23. E. T. Hale, W. Yin, and Y. Zhang. Fixed-point continuation for l1-minimization: Methodology and convergence. SIAM Journal on Optimization, 19(3):1107–1130, 2008. [pdf] [website/code].
  24. W. Yin, D. Goldfarb, and S. Osher. A comparison of three total variation based texture extraction models. Journal of Visual Communication and Image Representation, 18(3):240–252, 2007. [pdf].
  25. W. Yin, D. Goldfarb, and S. Osher. The total variation regularized L1 model for multiscale decomposition. SIAM Journal on Multiscale Modeling and Simulation, 6(1):190–211, 2006. [pdf].
  26. T. Chen, W. Yin, X. S. Zhou, D. Comaniciu, and T. Huang. Total variation models for variable lighting face recognition. IEEE Transactions on Pattern Analysis and Machine Intelligence (PAMI), 28(9):1519–1524, 2006. [pdf].
  27. D. Goldfarb and W. Yin. Second-order cone programming methods for total variation based image restoration. SIAM Journal on Scientific Computing, 27(2):622–645, 2005. [pdf].
  28. O. Scherzer, W. Yin, and S. Osher. Slope and G-set characterization of set-valued functions and applications to non-differentiable optimization problems. Communications in Mathematical Sciences, 3(4):479–492, 2005. [pdf].
  29. W. Yin, D. Goldfarb, and S. Osher. Image cartoon-texture decomposition and feature selection using the total variation regularized L1 functional. Variational, Geometric, and Level Set Methods in Computer Vision, 3752:73–84, 2005. [pdf].
  30. S. Osher, M. Burger, D. Goldfarb, J. Xu, and W. Yin. An iterative regularization method for total variation-based image restoration. SIAM Journal on Multiscale Modeling and Simulation, 4(2):460–489, 2005. [pdf].
  31. W. Yin, T. Chen, X. S. Zhou, and A. Chakraborty. Background correction for cDNA microarray image using the TV+L1 model. Bioinformatics, 21(10):2410–2416, 2005. [pdf].
  32. T. Chen, T. Huang, W. Yin, and X. S. Zhou. A new coarse-to-fine framework for 3D brain MR image registration. Computer Vision for Biomedical Image Applications, 3765:114–124, 2005. [pdf].

(C) Published in Refereed Conference Proceedings

  1. W. Yin, Z. Wen, S. Li, J. Meng, and Z. Han. Dynamic compressive spectrum sensing for cognitive radio networks. Information Sciences and Systems (CISS), 2011 45th Annual Conference on, pages 1–6, 2011. [pdf].
  2. J. Meng, Y. Li, N. Nguyen, W. Yin, and Z. Han. High resolution OFDM channel estimation with low speed ADC using compressive sensing. IEEE ICC 2011 Signal Processing for Communications Symposium, 2011. [pdf].
  3. Y. Li, W.-C. Shih, Z. Han, and W. Yin. Oil spill sensor using multispectral infrared imaging via l1 minimization. International Conference on Acoustics, Speech, and Signal Processing (ICASSP), 2011. [pdf].
  4. W. Yin, S. P. Morgan, J. Yang, and Y. Zhang. Practical compressive sensing with Toeplitz and circulant matrices. In proceedings of Visual Communications and Image Processing (VCIP), 2010. [pdf] [website/code].
  5. W. Guo and W. Yin. EdgeCS: an edge guided compressive sensing reconstruction. In proceedings of Visual Communications and Image Processing (VCIP), 2010. [pdf].
  6. J. Meng, W. Yin, H. Li, E. Houssain, and Z. Han. Collaborative spectrum sensing from sparse observations using matrix completion for cognitive radio networks. International Conference on Acoustics, Speech, and Signal Processing (ICASSP’10), pages 3114–3117, 2010. [pdf].
  7. S. Ma, W. Yin, Y. Zhang, and A. Chakraborty. An efficient algorithm for compressed MR imaging using total variation and wavelets. IEEE International Conference on Computer Vision and Pattern Recognition (CVPR’08), pages 1–8, 2008. [pdf] [code].
  8. R. Chartrand and W. Yin. Iteratively reweighted algorithms for compressive sensing. International Conference on Acoustics, Speech, and Signal Processing (ICASSP’08), pages 3869–3872, 2008. [pdf] [code].
  9. T. Chen, W. Yin, X. S. Zhou, D. Domaniciu, and T. Huang. Illumination normalization for face recognition and uneven background correction using total variation based image models. IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR’05), 2:532–539, 2005. [pdf].

(D) Submitted / Under Review

  1. M.-J. Lai, Y. Xu, and W. Yin. Low-rank matrix recovery using unconstrained smoothed-lq minimization. Submitted to SIAM Journal on Numerical Analysis, 2011. [pdf].
  2. Q. Ling, Z. Wen, and W. Yin. Decentralized jointly sparse signal recovery by reweighted ℓq minimization. Submitted to IEEE Transactions on Signal Processing, 2011. [pdf].
  3. W. Guo and W. Yin. Edge guided reconstruction for compressive imaging. Rice University CAAM Technical Report TR11-07. Submitted to SIAM Journal on Imaging Sciences, 2011. [pdf].
  4. Y. Xu, W. Yin, Z. Wen, and Y. Zhang. An alternating direction algorithm for matrix completion with nonnegative factors. Rice University CAAM Technical Report TR11-03. Submitted to Special Issue on Computational Mathematics, Journal of Frontiers of Mathematics in China (Springer), 2011. [pdf].
  5. Z. Wen and W. Yin. A feasible method for optimization with orthogonality constraints. Rice University CAAM Technical Report TR10-26. Submitted to Mathematical Programming, 2010. [pdf] [website/code].

(E) Technical Reports and Other Publications

  1. W. Deng, W. Yin, and Y. Zhang. Group sparse optimization by alternating direction method. Rice University CAAM Technical Report TR11-06, 2011. [pdf] [website/code].
  2. Z. Wen, W. Yin, and Y. Zhang. Solving a low-rank factorization model for matrix completion by a non-linear successive over-relaxation algorithm. Rice University CAAM Technical Report TR10-07, 2010. [pdf] [website/code].
  3. S. Valiollahzadeh and W. Yin. Hyperspectral data reconstruction combining spatial and spectral sparsity. Rice University CAAM Technical Report TR10-29, 2010. [pdf].
  4. W. Yin. Gurobi Mex: a MATLAB interface for Gurobi, User Guide and Examples. Online at http://www.caam.rice.edu/~wy1/gurobi_mex/, 2009-2011.
  5. S. P. Morgan, W. Yin, and K. R. Vixie. A MATLAB implementation of a flat norm motivated polygonal edge matching method using a decomposition of boundary into four 1-dimensional currents. arXiv:0812.0340v1. Rice University CAAM Technical Report TR09-35, 2009. [pdf].
  6. W. Yin and Y. Zhang. Extracting salient features from less data via l1-minimization. SIAG/Optimization Views and News, 19(1), 2008. [pdf].

In Preparation

  1. R. Lai, Z. Wen, W. Yin, X. Gu, and L. M. Lui. Fast and robust algorithms for harmonic energy minimization on genus-0 surfaces. In preparation, 2011. [pdf].
  2. Q. Ling, Y. Xu, W. Yin, and Z. Wen. Decentralized low-rank matrix completion. In preparation, 2011. [pdf].
  3. M.-J. Lai and W. Yin. Exact and stable recovery conditions for smoothed l1 and nuclear norm minimization. In preparation, 2011.