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 59 articles , 1 standard article )

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  1. Antonelli, Laura; De Simone, Valentina; di Serafino, Daniela: On the application of the spectral projected gradient method in image segmentation (2016)
  2. Birgin, E.G.; Lobato, R.D.; Martínez, J.M.: Packing ellipsoids by nonlinear optimization (2016)
  3. Cherian, Anoop; Sra, Suvrit: Positive definite matrices: data representation and applications to computer vision (2016)
  4. Bueno, L.F.; Haeser, G.; Martínez, J.M.: A flexible inexact-restoration method for constrained optimization (2015)
  5. Cui, Ming: Adjoint-free calculation method for conditional nonlinear optimal perturbations (2015)
  6. Gould, Nicholas I.M.; Orban, Dominique; Toint, Philippe L.: CUTEst: a constrained and unconstrained testing environment with safe threads for mathematical optimization (2015)
  7. Loreto, Milagros; Crema, Alejandro: Convergence analysis for the modified spectral projected subgradient method (2015)
  8. Zhou, Yang: Discrete least squares hybrid approximation with regularization on the two-sphere (2015)
  9. Cominetti, Roberto; Mascarenhas, Walter F.; Silva, Paulo J.S.: A Newton’s method for the continuous quadratic knapsack problem (2014)
  10. Maciel, María C.; Mendonça, María G.; Verdiell, Adriana B.: Monotone and nonmonotone trust-region-based algorithms for large scale unconstrained optimization problems (2013)
  11. Birgin, Ernesto G.; Gentil, Jan M.: Evaluating bound-constrained minimization software (2012)
  12. Bouhamidi, A.; Jbilou, K.: A Kronecker approximation with a convex constrained optimization method for blind image restoration (2012)
  13. Cheng, Wanyou; Li, Donghui: An active set modified Polak-Ribiére-Polyak method for large-scale nonlinear bound constrained optimization (2012)
  14. Andreani, R.; Júdice, J.J.; Martínez, J.M.; Patrício, J.: On the natural merit function for solving complementarity problems (2011)
  15. Bouhamidi, A.; Jbilou, K.; Raydan, M.: Convex constrained optimization for large-scale generalized Sylvester equations (2011)
  16. Chen, Xiaohong; Chen, Songcan; Xue, Hui: Large correlation analysis (2011)
  17. Francisco, Juliano B.; Martínez, J.M.; Martínez, Leandro; Pisnitchenko, Feodor: Inexact restoration method for minimization problems arising in electronic structure calculations (2011)
  18. Li, Qingna; Qi, Houduo; Xiu, Naihua: Block relaxation and majorization methods for the nearest correlation matrix with factor structure (2011)
  19. Xiao, Yun-Hai; Hu, Qing-Jie; Wei, Zengxin: Modified active set projected spectral gradient method for bound constrained optimization (2011)
  20. Yuan, Gonglin; Lu, Xiwen: An active set limited memory BFGS algorithm for bound constrained optimization (2011)

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