Algorithm 813: SPG -- software for convex-constrained optimization: Fortran 77 software implementing the SPG method is introduced. SPG is a nonmonotone projected gradient algorithm for solving large-scale convex-constrained optimization problems. It combines the classical projected gradient method with the spectral gradient choice of steplength and a nonmonotone line-search strategy. The user provides objective function and gradient values, and projections onto the feasible set. Some recent numerical tests are reported on very large location problems, indicating that SPG is substantially more efficient than existing general-purpose software on problems for which projections can be computed efficiently.

This software is also peer reviewed by journal TOMS.

References in zbMATH (referenced in 66 articles , 1 standard article )

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  1. Birgin, E. G.; Sobral, F. N. C.: Minimizing the object dimensions in circle and sphere packing problems (2008)
  2. Ferreira-Mendonça, L.; Lopes, V. L. R.; Martínez, J. M.: Quasi-Newton acceleration for equality-constrained minimization (2008)
  3. Júdice, Joaquim J.; Raydan, Marcos; Rosa, Silvério S.; Santos, Sandra A.: On the solution of the symmetric eigenvalue complementarity problem by the spectral projected gradient algorithm (2008)
  4. Morales, José Luis; Nocedal, Jorge; Smelyanskiy, Mikhail: An algorithm for the fast solution of symmetric linear complementarity problems (2008)
  5. Xiao, Yunhai; Hu, Qingjie: Subspace Barzilai-Borwein gradient method for large-scale bound constrained optimization (2008)
  6. Auslender, Alfred; Silva, Paulo J. S.; Teboulle, Marc: Nonmonotone projected gradient methods based on barrier and Euclidean distances (2007)
  7. Crema, Alejandro; Loreto, Milagros; Raydan, Marcos: Spectral projected subgradient with a momentum term for the Lagrangean dual approach (2007)
  8. Schnörr, C.; Schüle, T.; Weber, S.: Variational reconstruction with DC-programming (2007)
  9. Birgin, E. G.; Martínez, J. M.; Mascarenhas, W. F.; Ronconi, D. P.: Method of sentinels for packing items within arbitrary convex regions (2006)
  10. Birgin, E. G.; Martínez, J. M.; Nishihara, F. H.; Ronconi, D. P.: Orthogonal packing of rectangular items within arbitrary convex regions by nonlinear optimization (2006)
  11. Hager, W. W.; Zhang, H.: Recent advances in bound constrained optimization (2006)
  12. La Cruz, William; Martínez, José Mario; Raydan, Marcos: Spectral residual method without gradient information for solving large-scale nonlinear systems of equations (2006)
  13. Weber, Stefan; Nagy, Antal; Schüle, Thomas; Schnörr, Christoph; Kuba, Attila: A benchmark evaluation of large-scale optimization approaches to binary tomography (2006)
  14. Zhang, Hongchao; Hager, William W.: PACBB: a projected adaptive cyclic Barzilai-Borwein method for box constrained optimization (2006)
  15. Zhou, Bin; Gao, Li; Dai, Yuhong: Monotone projected gradient methods for large-scale box-constrained quadratic programming (2006)
  16. Zhou, Bin; Gao, Li; Dai, Yu-Hong: Gradient methods with adaptive step-sizes (2006)
  17. Andreani, Roberto; Birgin, Ernesto G.; Martínez, José Mario; Yuan, Jinyun: Spectral projected gradient and variable metric methods for optimization with linear inequalities (2005)
  18. Birgin, E. G.; Castillo, R. A.; Martínez, J. M.: Numerical comparison of augmented Lagrangian algorithms for nonconvex problems (2005)
  19. Birgin, E. G.; Martínez, J. M.; Ronconi, D. P.: Optimizing the packing of cylinders into a rectangular container: A nonlinear approach (2005)
  20. Marina, Andretta; Birgin, Ernesto G.; Martínez, José Mario: Practical active-set Euclidian trust-region method with spectral projected gradients for bound-constrained minimization (2005)