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 70 articles , 1 standard article )

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  1. Birgin, E. G.; Haeser, G.; Ramos, Alberto: Augmented Lagrangians with constrained subproblems and convergence to second-order stationary points (2018)
  2. Birgin, E. G.; Martínez, J. M.: On regularization and active-set methods with complexity for constrained optimization (2018)
  3. Zarepisheh, Masoud; Xing, Lei; Ye, Yinyu: A computation study on an integrated alternating direction method of multipliers for large scale optimization (2018)
  4. Birgin, E. G.; Lobato, R. D.; Martínez, J. M.: A nonlinear programming model with implicit variables for packing ellipsoids (2017)
  5. Brás, Carmo P.; Fischer, Andreas; Júdice, Joaquim J.; Schönefeld, Klaus; Seifert, Sarah: A block active set algorithm with spectral choice line search for the symmetric eigenvalue complementarity problem (2017)
  6. Cores, Debora; Figueroa, Johanna: A convex optimization approach for solving large scale linear systems (2017)
  7. Mu, Bin; Ren, Juhui; Yuan, Shijin: An efficient approach based on the gradient definition for solving conditional nonlinear optimal perturbation (2017)
  8. Antonelli, Laura; De Simone, Valentina; di Serafino, Daniela: On the application of the spectral projected gradient method in image segmentation (2016)
  9. Birgin, E. G.; Lobato, R. D.; Martínez, J. M.: Packing ellipsoids by nonlinear optimization (2016)
  10. Birgin, Ernesto G.; Lobato, Rafael D.; Martínez, José Mario: Constrained optimization with integer and continuous variables using inexact restoration and projected gradients (2016)
  11. Cherian, Anoop; Sra, Suvrit: Positive definite matrices: data representation and applications to computer vision (2016)
  12. Loreto, Milagros; Clapp, Samantha; Cratty, Charles; Page, Breeanna: Modified spectral projected subgradient method: convergence analysis and momentum parameter heuristics (2016)
  13. Bueno, L. F.; Haeser, G.; Martínez, J. M.: A flexible inexact-restoration method for constrained optimization (2015)
  14. Cui, Ming: Adjoint-free calculation method for conditional nonlinear optimal perturbations (2015)
  15. Gould, Nicholas I. M.; Orban, Dominique; Toint, Philippe L.: CUTEst: a constrained and unconstrained testing environment with safe threads for mathematical optimization (2015)
  16. Loreto, Milagros; Crema, Alejandro: Convergence analysis for the modified spectral projected subgradient method (2015)
  17. Zhou, Yang: Discrete least squares hybrid approximation with regularization on the two-sphere (2015)
  18. Cominetti, Roberto; Mascarenhas, Walter F.; Silva, Paulo J. S.: A Newton’s method for the continuous quadratic knapsack problem (2014)
  19. Ernesto Birgin; Jose Martínez; Marcos Raydan: Spectral Projected Gradient Methods: Review and Perspectives (2014) not zbMATH
  20. Maciel, María C.; Mendonça, María G.; Verdiell, Adriana B.: Monotone and nonmonotone trust-region-based algorithms for large scale unconstrained optimization problems (2013)

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