Computational Optimization and Applications

, Volume 59, Issue 3, pp 541–563 | Cite as

An efficient gradient method using the Yuan steplength

  • Roberta De Asmundis
  • Daniela di Serafino
  • William W. Hager
  • Gerardo Toraldo
  • Hongchao Zhang


We propose a new gradient method for quadratic programming, named SDC, which alternates some steepest descent (SD) iterates with some gradient iterates that use a constant steplength computed through the Yuan formula. The SDC method exploits the asymptotic spectral behaviour of the Yuan steplength to foster a selective elimination of the components of the gradient along the eigenvectors of the Hessian matrix, i.e., to push the search in subspaces of smaller and smaller dimensions. The new method has global and \(R\)-linear convergence. Furthermore, numerical experiments show that it tends to outperform the Dai–Yuan method, which is one of the fastest methods among the gradient ones. In particular, SDC appears superior as the Hessian condition number and the accuracy requirement increase. Finally, if the number of consecutive SD iterates is not too small, the SDC method shows a monotonic behaviour.


Gradient methods Yuan steplength Quadratic programming 



We wish to thank the anonymous referees for their constructive and detailed comments, which helped to improve the quality of this paper. This work was partially supported by INdAM-GNCS (2013 Project Numerical methods and software for large-scale optimization with applications to image processing and 2014 Project First-order optimization methods for image restoration and analysis), by the National Science Foundation (Grants 1016204 and 1115568), and by the Office of Naval Research (Grant N00014-11-1-0068).


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Copyright information

© Springer Science+Business Media New York 2014

Authors and Affiliations

  • Roberta De Asmundis
    • 1
  • Daniela di Serafino
    • 2
    • 3
  • William W. Hager
    • 4
  • Gerardo Toraldo
    • 5
  • Hongchao Zhang
    • 6
  1. 1.Department of Computer, Control and Management Engineering “Antonio Ruberti”Sapienza University of RomeRomaItaly
  2. 2.Department of Mathematics and PhysicsSecond University of NaplesCasertaItaly
  3. 3.Institute for High-Performance Computing and NetworkingCNRNaplesItaly
  4. 4.Department of MathematicsUniversity of FloridaGainesvilleUSA
  5. 5.Department of Mathematics and ApplicationsUniversity of Naples Federico IINaplesItaly
  6. 6.Department of MathematicsLouisiana State UniversityBaton RougeUSA

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