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 98 articles , 1 standard article )

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  1. Beck, Amir; Guttmann-Beck, Nili: FOM -- a MATLAB toolbox of first-order methods for solving convex optimization problems (2019)
  2. Renegar, James: Accelerated first-order methods for hyperbolic programming (2019)
  3. Tran-Dinh, Quoc: Proximal alternating penalty algorithms for nonsmooth constrained convex optimization (2019)
  4. Wong, Raymond K. W.; Zhang, Xiaoke: Nonparametric operator-regularized covariance function estimation for functional data (2019)
  5. Aravkin, Aleksandr Y.; Burke, James V.; Pillonetto, Gianluigi: Generalized system identification with stable spline kernels (2018)
  6. Bottou, Léon; Curtis, Frank E.; Nocedal, Jorge: Optimization methods for large-scale machine learning (2018)
  7. Tang, Sunli; Fernandez-Granda, Carlos; Lannuzel, Sylvain; Bernstein, Brett; Lattanzi, Riccardo; Cloos, Martijn; Knoll, Florian; Assländer, Jakob: Multicompartment magnetic resonance fingerprinting (2018)
  8. Wen, Bo; Chen, Xiaojun; Pong, Ting Kei: A proximal difference-of-convex algorithm with extrapolation (2018)
  9. Yu, Yongchao; Peng, Jigen: The matrix splitting based proximal fixed-point algorithms for quadratically constrained (\ell_1) minimization and Dantzig selector (2018)
  10. Ahookhosh, Masoud; Neumaier, Arnold: Optimal subgradient algorithms for large-scale convex optimization in simple domains (2017)
  11. Alli-Oke, Razak O.; Heath, William P.: A secant-based Nesterov method for convex functions (2017)
  12. Bauschke, Heinz H.; Bolte, Jérôme; Teboulle, Marc: A descent lemma beyond Lipschitz gradient continuity: first-order methods revisited and applications (2017)
  13. Dorsch, Dominik; Rauhut, Holger: Refined analysis of sparse MIMO radar (2017)
  14. Guo, Weihong; Song, Guohui; Zhang, Yue: PCM-TV-TFV: a novel two-stage framework for image reconstruction from Fourier data (2017)
  15. Huang, Wen; Gallivan, K. A.; Zhang, Xiangxiong: Solving phaselift by low-rank Riemannian optimization methods for complex semidefinite constraints (2017)
  16. Iwen, Mark; Viswanathan, Aditya; Wang, Yang: Robust sparse phase retrieval made easy (2017)
  17. Karimi, Sahar; Vavasis, Stephen: IMRO: A proximal quasi-Newton method for solving (\ell_1)-regularized least squares problems (2017)
  18. Li, Jinchao; Andersen, Martin S.; Vandenberghe, Lieven: Inexact proximal Newton methods for self-concordant functions (2017)
  19. Pang, Lili; Zhu, Detong: A line search filter-SQP method with Lagrangian function for nonlinear inequality constrained optimization (2017)
  20. Peña, Javier: Convergence of first-order methods via the convex conjugate (2017)

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