Parallel software for training large scale support vector machines on multiprocessor systems Parallel software for solving the quadratic program arising in training support vector machines for classification problems is introduced. The software implements an iterative decomposition technique and exploits both the storage and the computing resources available on multiprocessor systems, by distributing the heaviest computational tasks of each decomposition iteration. Based on a wide range of recent theoretical advances, relevant decomposition issues, such as the quadratic subproblem solution, the gradient updating, the working set selection, are systematically described and their careful combination to get an effective parallel tool is discussed. A comparison with state-of-the-art packages on benchmark problems demonstrates the good accuracy and the remarkable time saving achieved by the proposed software. Furthermore, challenging experiments on real-world data sets with millions training samples highlight how the software makes large scale standard nonlinear support vector machines effectively tractable on common multiprocessor systems. This feature is not shown by any of the available codes.

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  1. Ding, Xiaojian; Jin, Sheng; Lei, Ming; Yang, Fan: A predictor-corrector affine scaling method to train optimized extreme learning machine (2022)
  2. Crisci, Serena; Porta, Federica; Ruggiero, Valeria; Zanni, Luca: Spectral properties of Barzilai-Borwein rules in solving singly linearly constrained optimization problems subject to lower and upper bounds (2020)
  3. Etesami, S. Rasoul: Complexity and approximability of optimal resource allocation and Nash equilibrium over networks (2020)
  4. Galli, Leonardo; Galligari, Alessandro; Sciandrone, Marco: A unified convergence framework for nonmonotone inexact decomposition methods (2020)
  5. Crisci, Serena; Ruggiero, Valeria; Zanni, Luca: Steplength selection in gradient projection methods for box-constrained quadratic programs (2019)
  6. di Serafino, Daniela; Ruggiero, Valeria; Toraldo, Gerardo; Zanni, Luca: On the steplength selection in gradient methods for unconstrained optimization (2018)
  7. di Serafino, Daniela; Toraldo, Gerardo; Viola, Marco; Barlow, Jesse: A two-phase gradient method for quadratic programming problems with a single linear constraint and bounds on the variables (2018)
  8. Manno, Andrea; Palagi, Laura; Sagratella, Simone: Parallel decomposition methods for linearly constrained problems subject to simple bound with application to the SVMs training (2018)
  9. Piccialli, Veronica; Sciandrone, Marco: Nonlinear optimization and support vector machines (2018)
  10. Yan, Xihong; Wang, Kai; He, Hongjin: On the convergence rate of scaled gradient projection method (2018)
  11. Amaral, Sergio; Allaire, Douglas; Willcox, Karen: Optimal (L_2)-norm empirical importance weights for the change of probability measure (2017)
  12. Chen, Tianyi; Curtis, Frank E.; Robinson, Daniel P.: A reduced-space algorithm for minimizing (\ell_1)-regularized convex functions (2017)
  13. Nosratipour, Hadi; Fard, Omid Solaymani; Borzabadi, Akbar Hashemi; Sarani, Farhad: Stable equilibrium configuration of two bar truss by an efficient nonmonotone global Barzilai-Borwein gradient method in a fuzzy environment (2017)
  14. Stanković, Ljubiša; Daković, Miloš; Vujović, Stefan: Reconstruction of sparse signals in impulsive disturbance environments (2017)
  15. Stanković, Ljubiša; Daković, Miloš: On a gradient-based algorithm for sparse signal reconstruction in the signal/measurements domain (2016)
  16. Camelo, S. A.; González-Lima, M. D.; Quiroz, A. J.: Nearest neighbors methods for support vector machines (2015)
  17. Niu, Lingfeng; Zhou, Ruizhi; Zhao, Xi; Shi, Yong: Two new decomposition algorithms for training bound-constrained support vector machines (2015)
  18. Beck, Amir: The 2-coordinate descent method for solving double-sided simplex constrained minimization problems (2014)
  19. Cassioli, A.; Di Lorenzo, D.; Sciandrone, M.: On the convergence of inexact block coordinate descent methods for constrained optimization (2013)
  20. Dai, Yu-Hong: A new analysis on the Barzilai-Borwein gradient method (2013)

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