TFOCS

TFOCS: Templates for First-Order Conic Solvers. TFOCS (pronounced tee-fox) provides a set of Matlab templates, or building blocks, that can be used to construct efficient, customized solvers for a variety of convex models, including in particular those employed in sparse recovery applications. It was conceived and written by Stephen Becker, Emmanuel J. Candès and Michael Grant.


References in zbMATH (referenced in 67 articles , 1 standard article )

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  1. Bauschke, Heinz H.; Bolte, Jér^ome; Teboulle, Marc: A descent lemma beyond Lipschitz gradient continuity: first-order methods revisited and applications (2017)
  2. Iwen, Mark; Viswanathan, Aditya; Wang, Yang: Robust sparse phase retrieval made easy (2017)
  3. Karimi, Sahar; Vavasis, Stephen: IMRO: A proximal quasi-Newton method for solving $\ell_1$-regularized least squares problems (2017)
  4. Soltani, Sara; Andersen, Martin S.; Hansen, Per Christian: Tomographic image reconstruction using training images (2017)
  5. Wen, Bo; Chen, Xiaojun; Pong, Ting Kei: Linear convergence of proximal gradient algorithm with extrapolation for a class of nonconvex nonsmooth minimization problems (2017)
  6. Byrd, Richard H.; Nocedal, Jorge; Oztoprak, Figen: An inexact successive quadratic approximation method for L-1 regularized optimization (2016)
  7. Fogel, Fajwel; Waldspurger, Irène; d’Aspremont, Alexandre: Phase retrieval for imaging problems (2016)
  8. Fountoulakis, Kimon; Gondzio, Jacek: A second-order method for strongly convex $\ell _1$-regularization problems (2016)
  9. Fountoulakis, Kimon; Gondzio, Jacek: Performance of first- and second-order methods for $\ell_1$-regularized least squares problems (2016)
  10. Friedlander, Michael P.; Mac^edo, Ives: Low-rank spectral optimization via gauge duality (2016)
  11. Iwen, Mark A.; Viswanathan, Aditya; Wang, Yang: Fast phase retrieval from local correlation measurements (2016)
  12. Lessard, Laurent; Recht, Benjamin; Packard, Andrew: Analysis and design of optimization algorithms via integral quadratic constraints (2016)
  13. Morgenshtern, Veniamin I.; Candès, Emmanuel J.: Super-resolution of positive sources: the discrete setup (2016)
  14. Neumaier, Arnold: OSGA: a fast subgradient algorithm with optimal complexity (2016)
  15. O’Donoghue, Brendan; Chu, Eric; Parikh, Neal; Boyd, Stephen: Conic optimization via operator splitting and homogeneous self-dual embedding (2016)
  16. Soltani, Sara; Kilmer, Misha E.; Hansen, Per Christian: A tensor-based dictionary learning approach to tomographic image reconstruction (2016)
  17. Boche, Holger; Calderbank, Robert; Kutyniok, Gitta; Vybíral, Jan: A survey of compressed sensing (2015)
  18. Candès, Emmanuel J.; Eldar, Yonina C.; Strohmer, Thomas; Voroninski, Vladislav: Phase retrieval via matrix completion (2015)
  19. Candès, Emmanuel J.; Li, Xiaodong; Soltanolkotabi, Mahdi: Phase retrieval from coded diffraction patterns (2015)
  20. Chaudhury, K.N.; Khoo, Y.; Singer, A.: Global registration of multiple point clouds using semidefinite programming (2015)

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