UNLocBoX

The UNLocboX is a matlab convex optimization toolbox part of the UnlocX project. It composed of the most used algorithms such as forward backward, Douglas-Rachford and ppxa. Moreover a all collection of proximal operators are available in order to implement problems very efficiently.

Keywords for this software

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References in zbMATH (referenced in 294 articles )

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  1. Altuntac, Erdem: Choice of the parameters in a primal-dual algorithm for Bregman iterated variational regularization (2021)
  2. Bonettini, Silvia; Porta, Federica; Ruggiero, Valeria; Zanni, Luca: Variable metric techniques for forward-backward methods in imaging (2021)
  3. Johnstone, Patrick R.; Eckstein, Jonathan: Single-forward-step projective splitting: exploiting cocoercivity (2021)
  4. Abe, Jiro; Yamagishi, Masao; Yamada, Isao: Linearly involved generalized Moreau enhanced models and their proximal splitting algorithm under overall convexity condition (2020)
  5. Banert, Sebastian; Ringh, Axel; Adler, Jonas; Karlsson, Johan; Öktem, Ozan: Data-driven nonsmooth optimization (2020)
  6. Bertocchi, Carla; Chouzenoux, Emilie; Corbineau, Marie-Caroline; Pesquet, Jean-Christophe; Prato, Marco: Deep unfolding of a proximal interior point method for image restoration (2020)
  7. Bolte, Jérôme; Chen, Zheng; Pauwels, Edouard: The multiproximal linearization method for convex composite problems (2020)
  8. Bonettini, S.; Prato, M.; Rebegoldi, S.: Convergence of inexact forward-backward algorithms using the forward-backward envelope (2020)
  9. Bredies, Kristian; Holler, Martin: Higher-order total variation approaches and generalisations (2020)
  10. Brogliato, Bernard; Tanwani, Aneel: Dynamical systems coupled with monotone set-valued operators: formalisms, applications, well-posedness, and stability (2020)
  11. Chi, Eric C.; Gaines, Brian J.; Sun, Will Wei; Zhou, Hua; Yang, Jian: Provable convex co-clustering of tensors (2020)
  12. Chouzenoux, Emilie; Corbineau, Marie-Caroline; Pesquet, Jean-Christophe: A proximal interior point algorithm with applications to image processing (2020)
  13. Deng, Zhao; Liu, Sanyang: Inertial proximal strictly contractive peaceman-Rachford splitting method with an indefinite term for convex optimization (2020)
  14. Erichson, N. Benjamin; Zheng, Peng; Manohar, Krithika; Brunton, Steven L.; Kutz, J. Nathan; Aravkin, Aleksandr Y.: Sparse principal component analysis via variable projection (2020)
  15. Fernandez Vidal, Ana; De Bortoli, Valentin; Pereyra, Marcelo; Durmus, Alain: Maximum likelihood estimation of regularization parameters in high-dimensional inverse problems: an empirical Bayesian approach. I: Methodology and experiments (2020)
  16. Hare, Warren; Planiden, Chayne; Sagastizábal, Claudia: A derivative-free (\mathcalV\mathcalU)-algorithm for convex finite-max problems (2020)
  17. Ho, Lam Si Tung; Schaeffer, Hayden; Tran, Giang; Ward, Rachel: Recovery guarantees for polynomial coefficients from weakly dependent data with outliers (2020)
  18. Hong, Mingyi; Chang, Tsung-Hui; Wang, Xiangfeng; Razaviyayn, Meisam; Ma, Shiqian; Luo, Zhi-Quan: A block successive upper-bound minimization method of multipliers for linearly constrained convex optimization (2020)
  19. Hu, Leyu; Zhang, Wenxing; Cai, Xingju; Han, Deren: A parallel operator splitting algorithm for solving constrained total-variation retinex (2020)
  20. Hütter, Jan-Christian; Mao, Cheng; Rigollet, Philippe; Robeva, Elina: Estimation of Monge matrices (2020)

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