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 133 articles , 1 standard article )

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  1. Curtis, Frank E.; Dai, Yutong; Robinson, Daniel P.: A subspace acceleration method for minimization involving a group sparsity-inducing regularizer (2022)
  2. Liu, Deyi; Cevher, Volkan; Tran-Dinh, Quoc: A Newton Frank-Wolfe method for constrained self-concordant minimization (2022)
  3. Liu, Tianxiang; Takeda, Akiko: An inexact successive quadratic approximation method for a class of difference-of-convex optimization problems (2022)
  4. Tran-Dinh, Quoc; Liang, Ling; Toh, Kim-Chuan: A new homotopy proximal variable-metric framework for composite convex minimization (2022)
  5. Abubakar, Auwal Bala; Kumam, Poom; Mohammad, Hassan; Ibrahim, Abdulkarim Hassan: PRP-like algorithm for monotone operator equations (2021)
  6. Beinert, Robert; Bredies, Kristian: Tensor-free proximal methods for lifted bilinear/quadratic inverse problems with applications to phase retrieval (2021)
  7. Fang, Sheng; Liu, Yong-Jin; Xiong, Xianzhu: Efficient sparse Hessian-based semismooth Newton algorithms for Dantzig selector (2021)
  8. 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)
  9. 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
  10. Liu, Yanli; Xu, Yunbei; Yin, Wotao: Acceleration of primal-dual methods by preconditioning and simple subproblem procedures (2021)
  11. Mao, Xiaoyu; He, Hongjin; Xu, Hong-Kun: A partially proximal linearized alternating minimization method for finding Dantzig selectors (2021)
  12. Nakayama, Shummin; Narushima, Yasushi; Yabe, Hiroshi: Inexact proximal memoryless quasi-Newton methods based on the Broyden family for minimizing composite functions (2021)
  13. Pi, J.; Wang, Honggang; Pardalos, Panos M.: A dual reformulation and solution framework for regularized convex clustering problems (2021)
  14. 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)
  15. Folberth, James; Becker, Stephen: Safe feature elimination for non-negativity constrained convex optimization (2020)
  16. Kikuchi, Paula A.; Oliveira, Aurelio R. L.: New preconditioners applied to linear programming and the compressive sensing problems (2020)
  17. Staib, Matthew; Jegelka, Stefanie: Robust budget allocation via continuous submodular functions (2020)
  18. Ahookhosh, Masoud: Accelerated first-order methods for large-scale convex optimization: nearly optimal complexity under strong convexity (2019)
  19. Bao, Weizhu; Ruan, Xinran: Computing ground states of Bose-Einstein condensates with higher order interaction via a regularized density function formulation (2019)
  20. Beck, Amir; Guttmann-Beck, Nili: FOM -- a MATLAB toolbox of first-order methods for solving convex optimization problems (2019)

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