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 82 articles , 1 standard article )

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  1. Yu, Yongchao; Peng, Jigen: The matrix splitting based proximal fixed-point algorithms for quadratically constrained $\ell_1$ minimization and Dantzig selector (2018)
  2. Ahookhosh, Masoud; Neumaier, Arnold: Optimal subgradient algorithms for large-scale convex optimization in simple domains (2017)
  3. Alli-Oke, Razak O.; Heath, William P.: A secant-based Nesterov method for convex functions (2017)
  4. Bauschke, Heinz H.; Bolte, Jér^ome; Teboulle, Marc: A descent lemma beyond Lipschitz gradient continuity: first-order methods revisited and applications (2017)
  5. Dorsch, Dominik; Rauhut, Holger: Refined analysis of sparse MIMO radar (2017)
  6. Huang, Wen; Gallivan, K.A.; Zhang, Xiangxiong: Solving phaselift by low-rank Riemannian optimization methods for complex semidefinite constraints (2017)
  7. Iwen, Mark; Viswanathan, Aditya; Wang, Yang: Robust sparse phase retrieval made easy (2017)
  8. Karimi, Sahar; Vavasis, Stephen: IMRO: A proximal quasi-Newton method for solving $\ell_1$-regularized least squares problems (2017)
  9. Li, Jinchao; Andersen, Martin S.; Vandenberghe, Lieven: Inexact proximal Newton methods for self-concordant functions (2017)
  10. Pang, Lili; Zhu, Detong: A line search filter-SQP method with Lagrangian function for nonlinear inequality constrained optimization (2017)
  11. Soltani, Sara; Andersen, Martin S.; Hansen, Per Christian: Tomographic image reconstruction using training images (2017)
  12. Tran-Dinh, Quoc: Adaptive smoothing algorithms for nonsmooth composite convex minimization (2017)
  13. Tropp, Joel A.: Book review of: S. Foucart and H. Rauhut, A mathematical introduction to compressive sensing (2017)
  14. Wen, Bo; Chen, Xiaojun; Pong, Ting Kei: Linear convergence of proximal gradient algorithm with extrapolation for a class of nonconvex nonsmooth minimization problems (2017)
  15. Yu, Yongchao; Peng, Jigen: The Moreau envelope based efficient first-order methods for sparse recovery (2017)
  16. Byrd, Richard H.; Nocedal, Jorge; Oztoprak, Figen: An inexact successive quadratic approximation method for L-1 regularized optimization (2016)
  17. Diamond, Steven; Boyd, Stephen: Matrix-free convex optimization modeling (2016)
  18. Fogel, Fajwel; Waldspurger, Irène; d’Aspremont, Alexandre: Phase retrieval for imaging problems (2016)
  19. Fountoulakis, Kimon; Gondzio, Jacek: Performance of first- and second-order methods for $\ell_1$-regularized least squares problems (2016)
  20. Fountoulakis, Kimon; Gondzio, Jacek: A second-order method for strongly convex $\ell _1$-regularization problems (2016)

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