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 125 articles , 1 standard article )

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  1. Gong, Yuxuan; Li, Peijun; Wang, Xu; Xu, Xiang: Numerical solution of an inverse random source problem for the time fractional diffusion equation via phaselift (2021)
  2. Jakob S. Jørgensen, Evelina Ametova, Genoveva Burca, Gemma Fardell, Evangelos Papoutsellis, Edoardo Pasca, Kris Thielemans, Martin Turner, Ryan Warr, William R. B. Lionheart, Philip J. Withers: Core Imaging Library - Part I: a versatile Python framework for tomographic imaging (2021) arXiv
  3. Liu, Yanli; Xu, Yunbei; Yin, Wotao: Acceleration of primal-dual methods by preconditioning and simple subproblem procedures (2021)
  4. Nakayama, Shummin; Narushima, Yasushi; Yabe, Hiroshi: Inexact proximal memoryless quasi-Newton methods based on the Broyden family for minimizing composite functions (2021)
  5. Pi, J.; Wang, Honggang; Pardalos, Panos M.: A dual reformulation and solution framework for regularized convex clustering problems (2021)
  6. Tong, Can; Teng, Yueyang; Yao, Yudong; Qi, Shouliang; Li, Chen; Zhang, Tie: Eigenvalue-free iterative shrinkage-thresholding algorithm for solving the linear inverse problems (2021)
  7. Folberth, James; Becker, Stephen: Safe feature elimination for non-negativity constrained convex optimization (2020)
  8. Kikuchi, Paula A.; Oliveira, Aurelio R. L.: New preconditioners applied to linear programming and the compressive sensing problems (2020)
  9. Staib, Matthew; Jegelka, Stefanie: Robust budget allocation via continuous submodular functions (2020)
  10. Ahookhosh, Masoud: Accelerated first-order methods for large-scale convex optimization: nearly optimal complexity under strong convexity (2019)
  11. Bao, Weizhu; Ruan, Xinran: Computing ground states of Bose-Einstein condensates with higher order interaction via a regularized density function formulation (2019)
  12. Beck, Amir; Guttmann-Beck, Nili: FOM -- a MATLAB toolbox of first-order methods for solving convex optimization problems (2019)
  13. Friedlander, Michael P.; Macêdo, Ives; Pong, Ting Kei: Polar convolution (2019)
  14. Liu, Tianxiang; Pong, Ting Kei; Takeda, Akiko: A successive difference-of-convex approximation method for a class of nonconvex nonsmooth optimization problems (2019)
  15. Lorenz, Dirk A.; Tran-Dinh, Quoc: Non-stationary Douglas-Rachford and alternating direction method of multipliers: adaptive step-sizes and convergence (2019)
  16. Renegar, James: Accelerated first-order methods for hyperbolic programming (2019)
  17. Sun, Tianxiao; Quoc, Tran-Dinh: Generalized self-concordant functions: a recipe for Newton-type methods (2019)
  18. Tran-Dinh, Quoc: Proximal alternating penalty algorithms for nonsmooth constrained convex optimization (2019)
  19. Wen, Bo; Xue, Xiaoping: On the convergence of the iterates of proximal gradient algorithm with extrapolation for convex nonsmooth minimization problems (2019)
  20. Wong, Raymond K. W.; Zhang, Xiaoke: Nonparametric operator-regularized covariance function estimation for functional data (2019)

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