GPDT

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.


References in zbMATH (referenced in 33 articles , 2 standard articles )

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  1. Amaral, Sergio; Allaire, Douglas; Willcox, Karen: Optimal $L_2$-norm empirical importance weights for the change of probability measure (2017)
  2. 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)
  3. Camelo, S.A.; González-Lima, M.D.; Quiroz, A.J.: Nearest neighbors methods for support vector machines (2015)
  4. Niu, Lingfeng; Zhou, Ruizhi; Zhao, Xi; Shi, Yong: Two new decomposition algorithms for training bound-constrained support vector machines (2015)
  5. Beck, Amir: The 2-coordinate descent method for solving double-sided simplex constrained minimization problems (2014)
  6. Cassioli, A.; Di Lorenzo, D.; Sciandrone, M.: On the convergence of inexact block coordinate descent methods for constrained optimization (2013)
  7. Dai, Yu-Hong: A new analysis on the Barzilai-Borwein gradient method (2013)
  8. He, Hongjin; Han, Deren; Li, Zhibao: Some projection methods with the BB step sizes for variational inequalities (2012)
  9. Gonzalez-Lima, Maria D.; Hager, William W.; Zhang, Hongchao: An affine-scaling interior-point method for continuous knapsack constraints with application to support vector machines (2011)
  10. Liuzzi, Giampaolo; Palagi, Laura; Piacentini, Mauro: On the convergence of a Jacobi-type algorithm for singly linearly-constrained problems subject to simple bounds (2011)
  11. Niu, Lingfeng; Yuan, Ya-Xiang: A parallel decomposition algorithm for training multiclass kernel-based vector machines (2011)
  12. Andretta, Marina; Birgin, Ernesto G.; Martínez, J.M.: Partial spectral projected gradient method with active-set strategy for linearly constrained optimization (2010)
  13. Fletcher, Roger; Zanghirati, Gaetano: Binary separation and training support vector machines (2010)
  14. Ruggiero, Valeria; Serafini, Thomas; Zanella, Riccardo; Zanni, Luca: Iterative regularization algorithms for constrained image deblurring on graphics processors (2010)
  15. Zhu, Mingqiang; Wright, Stephen J.; Chan, Tony F.: Duality-based algorithms for total-variation-regularized image restoration (2010)
  16. Bonettini, Silvia; Serafini, Thomas: Non-negatively constrained image deblurring with an inexact interior point method (2009)
  17. Bonettini, S.; Zanella, R.; Zanni, L.: A scaled gradient projection method for constrained image deblurring (2009)
  18. La Cruz, William; Noguera, Gilberto: Hybrid spectral gradient method for the unconstrained minimization problem (2009)
  19. Lin, C.J.; Lucidi, S.; Palagi, L.; Risi, A.; Sciandrone, M.: Decomposition algorithm model for singly linearly-constrained problems subject to lower and Upper bounds (2009)
  20. Loris, I.; Bertero, M.; De Mol, C.; Zanella, R.; Zanni, L.: Accelerating gradient projection methods for $\ell _1$-constrained signal recovery by steplength selection rules (2009)

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