SPGL1

SPGL1: A solver for large-scale sparse reconstruction: Probing the Pareto frontier for basis pursuit solutions. The basis pursuit problem seeks a minimum one-norm solution of an underdetermined least-squares problem. Basis Pursuit DeNoise (BPDN) fits the least-squares problem only approximately, and a single parameter determines a curve that traces the optimal trade-off between the least-squares fit and the one-norm of the solution. We prove that this curve is convex and continuously differentiable over all points of interest, and show that it gives an explicit relationship to two other optimization problems closely related to BPDN. We describe a root-finding algorithm for finding arbitrary points on this curve; the algorithm is suitable for problems that are large scale and for those that are in the complex domain. At each iteration, a spectral gradient-projection method approximately minimizes a least-squares problem with an explicit one-norm constraint. Only matrix-vector operations are required. The primal-dual solution of this problem gives function and derivative information needed for the root-finding method. Numerical experiments on a comprehensive set of test problems demonstrate that the method scales well to large problems.


References in zbMATH (referenced in 153 articles , 2 standard articles )

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  1. Abubakar, Auwal Bala; Kumam, Poom; Mohammad, Hassan: A note on the spectral gradient projection method for nonlinear monotone equations with applications (2020)
  2. Jiang, Shan; Fang, Shu-Cherng; Nie, Tiantian; Xing, Wenxun: A gradient descent based algorithm for (\ell_p) minimization (2020)
  3. Le Gia, Quoc Thong; Sloan, Ian H.; Womersley, Robert S.; Wang, Yu Guang: Isotropic sparse regularization for spherical harmonic representations of random fields on the sphere (2020)
  4. Perez, Guillaume; Barlaud, Michel; Fillatre, Lionel; Régin, Jean-Charles: A filtered bucket-clustering method for projection onto the simplex and the (\ell_1) ball (2020)
  5. Shehu, Yekini; Iyiola, Olaniyi S.; Ogbuisi, Ferdinard U.: Iterative method with inertial terms for nonexpansive mappings: applications to compressed sensing (2020)
  6. Sochala, Pierre; Chen, Chen; Dawson, Clint; Iskandarani, Mohamed: A polynomial chaos framework for probabilistic predictions of storm surge events (2020)
  7. van den Berg, Ewout: A hybrid quasi-Newton projected-gradient method with application to lasso and basis-pursuit denoising (2020)
  8. Wang, Guoqiang; Wei, Xinyuan; Yu, Bo; Xu, Lijun: An efficient proximal block coordinate homotopy method for large-scale sparse least squares problems (2020)
  9. Xie, Siyu; Guo, Lei: Analysis of compressed distributed adaptive filters (2020)
  10. Adcock, Ben; Gelb, Anne; Song, Guohui; Sui, Yi: Joint sparse recovery based on variances (2019)
  11. Adcock, Ben; Sui, Yi: Compressive Hermite interpolation: sparse, high-dimensional approximation from gradient-augmented measurements (2019)
  12. Aravkin, Aleksandr Y.; Burke, James V.; Drusvyatskiy, Dmitry; Friedlander, Michael P.; Roy, Scott: Level-set methods for convex optimization (2019)
  13. Brzyski, Damian; Gossmann, Alexej; Su, Weijie; Bogdan, Małgorzata: Group SLOPE -- adaptive selection of groups of predictors (2019)
  14. Gelb, Anne; Hou, X.; Li, Q.: Numerical analysis for conservation laws using (l_1) minimization (2019)
  15. Kim, Kyung-Su; Chung, Sae-Young: Greedy subspace pursuit for joint sparse recovery (2019)
  16. Kumar, Rajiv; Willemsen, Bram; Herrmann, Felix J.; Malcolm, Alison: Enabling numerically exact local solver for waveform inversion -- a low-rank approach (2019)
  17. Liu, Michelle; Kumar, Rajiv; Haber, Eldad; Aravkin, Aleksandr: Simultaneous-shot inversion for PDE-constrained optimization problems with missing data (2019)
  18. Manohar, Krithika; Kaiser, Eurika; Brunton, Steven L.; Kutz, J. Nathan: Optimized sampling for multiscale dynamics (2019)
  19. Movahedian, N.; Nobakhtian, S.; Sarabadan, M.: Nonsmooth sparsity constrained optimization problems: optimality conditions (2019)
  20. Petrosyan, Armenak; Tran, Hoang; Webster, Clayton: Reconstruction of jointly sparse vectors via manifold optimization (2019)

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