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A coordinate gradient descent method for linearly constrained smooth optimization and support vector machines training

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  • Paul Tseng
  • Sangwoon Yun

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  • Paul Tseng & Sangwoon Yun, 2010. "A coordinate gradient descent method for linearly constrained smooth optimization and support vector machines training," Computational Optimization and Applications, Springer, vol. 47(2), pages 179-206, October.
    • Handle: RePEc:spr:coopap:v:47:y:2010:i:2:p:179-206
    DOI: 10.1007/s10589-008-9215-4
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    References listed on IDEAS

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    1. C. J. Lin & S. Lucidi & L. Palagi & A. Risi & M. Sciandrone, 2009. "Decomposition Algorithm Model for Singly Linearly-Constrained Problems Subject to Lower and Upper Bounds," Journal of Optimization Theory and Applications, Springer, vol. 141(1), pages 107-126, April.
    2. K. C. Kiwiel, 2007. "On Linear-Time Algorithms for the Continuous Quadratic Knapsack Problem," Journal of Optimization Theory and Applications, Springer, vol. 134(3), pages 549-554, September.
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    Citations

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    Cited by:

    1. Huang, Xiaolin & Shi, Lei & Pelckmans, Kristiaan & Suykens, Johan A.K., 2014. "Asymmetric ν-tube support vector regression," Computational Statistics & Data Analysis, Elsevier, vol. 77(C), pages 371-382.
    2. Ion Necoara & Andrei Patrascu, 2014. "A random coordinate descent algorithm for optimization problems with composite objective function and linear coupled constraints," Computational Optimization and Applications, Springer, vol. 57(2), pages 307-337, March.
    3. Tianxiang Liu & Ting Kei Pong, 2017. "Further properties of the forward–backward envelope with applications to difference-of-convex programming," Computational Optimization and Applications, Springer, vol. 67(3), pages 489-520, July.
    4. Amir Beck, 2014. "The 2-Coordinate Descent Method for Solving Double-Sided Simplex Constrained Minimization Problems," Journal of Optimization Theory and Applications, Springer, vol. 162(3), pages 892-919, September.
    5. Andrea Cristofari, 2019. "An almost cyclic 2-coordinate descent method for singly linearly constrained problems," Computational Optimization and Applications, Springer, vol. 73(2), pages 411-452, June.
    6. Weizhe Gu & Wei-Po Chen & Chun-Hsu Ko & Yuh-Jye Lee & Jein-Shan Chen, 2018. "Two smooth support vector machines for $$\varepsilon $$ ε -insensitive regression," Computational Optimization and Applications, Springer, vol. 70(1), pages 171-199, May.
    7. Leonardo Galli & Alessandro Galligari & Marco Sciandrone, 2020. "A unified convergence framework for nonmonotone inexact decomposition methods," Computational Optimization and Applications, Springer, vol. 75(1), pages 113-144, January.
    8. Andrea Manno & Laura Palagi & Simone Sagratella, 2014. "A Class of Convergent Parallel Algorithms for SVMs Training," DIAG Technical Reports 2014-17, Department of Computer, Control and Management Engineering, Universita' degli Studi di Roma "La Sapienza".
    9. Wei Peng & Hui Zhang & Xiaoya Zhang, 2019. "Nonconvex Proximal Incremental Aggregated Gradient Method with Linear Convergence," Journal of Optimization Theory and Applications, Springer, vol. 183(1), pages 230-245, October.
    10. Andrea Manno & Laura Palagi & Simone Sagratella, 2018. "Parallel decomposition methods for linearly constrained problems subject to simple bound with application to the SVMs training," Computational Optimization and Applications, Springer, vol. 71(1), pages 115-145, September.
    11. Fan Wu & Wei Bian, 2020. "Accelerated iterative hard thresholding algorithm for $$l_0$$l0 regularized regression problem," Journal of Global Optimization, Springer, vol. 76(4), pages 819-840, April.
    12. Kimon Fountoulakis & Rachael Tappenden, 2018. "A flexible coordinate descent method," Computational Optimization and Applications, Springer, vol. 70(2), pages 351-394, June.

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