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 251 articles )

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  1. Abe, Jiro; Yamagishi, Masao; Yamada, Isao: Linearly involved generalized Moreau enhanced models and their proximal splitting algorithm under overall convexity condition (2020)
  2. Banert, Sebastian; Ringh, Axel; Adler, Jonas; Karlsson, Johan; Öktem, Ozan: Data-driven nonsmooth optimization (2020)
  3. Bertocchi, Carla; Chouzenoux, Emilie; Corbineau, Marie-Caroline; Pesquet, Jean-Christophe; Prato, Marco: Deep unfolding of a proximal interior point method for image restoration (2020)
  4. Brogliato, Bernard; Tanwani, Aneel: Dynamical systems coupled with monotone set-valued operators: formalisms, applications, well-posedness, and stability (2020)
  5. Deng, Zhao; Liu, Sanyang: Inertial proximal strictly contractive peaceman-Rachford splitting method with an indefinite term for convex optimization (2020)
  6. Erichson, N. Benjamin; Zheng, Peng; Manohar, Krithika; Brunton, Steven L.; Kutz, J. Nathan; Aravkin, Aleksandr Y.: Sparse principal component analysis via variable projection (2020)
  7. Johnstone, Patrick R.; Eckstein, Jonathan: Projective splitting with forward steps only requires continuity (2020)
  8. O’Connor, Daniel; Vandenberghe, Lieven: On the equivalence of the primal-dual hybrid gradient method and Douglas-Rachford splitting (2020)
  9. Panahi, Ashkan; Chehreghani, Morteza Haghir; Dubhashi, Devdatt: Accelerated proximal incremental algorithm schemes for non-strongly convex functions (2020)
  10. Rasch, Julian; Chambolle, Antonin: Inexact first-order primal-dual algorithms (2020)
  11. Adcock, Ben; Gelb, Anne; Song, Guohui; Sui, Yi: Joint sparse recovery based on variances (2019)
  12. Adly, Samir; Bourdin, Loïc; Caubet, Fabien: On a decomposition formula for the proximal operator of the sum of two convex functions (2019)
  13. Ahookhosh, Masoud; Neumaier, Arnold: An optimal subgradient algorithm with subspace search for costly convex optimization problems (2019)
  14. Arridge, Simon; Maass, Peter; Öktem, Ozan; Schönlieb, Carola-Bibiane: Solving inverse problems using data-driven models (2019)
  15. Badea, Lori: On the convergence of a multigrid method for Moreau-regularized variational inequalities of the second kind (2019)
  16. Banjac, Goran; Goulart, Paul; Stellato, Bartolomeo; Boyd, Stephen: Infeasibility detection in the alternating direction method of multipliers for convex optimization (2019)
  17. Becker, Stephen; Fadili, Jalal; Ochs, Peter: On quasi-Newton forward-backward splitting: proximal calculus and convergence (2019)
  18. Bitterlich, Sandy; Boţ, Radu Ioan; Csetnek, Ernö Robert; Wanka, Gert: The proximal alternating minimization algorithm for two-block separable convex optimization problems with linear constraints (2019)
  19. Briceño-Arias, Luis M.; Chierchia, Giovanni; Chouzenoux, Emilie; Pesquet, Jean-Christophe: A random block-coordinate Douglas-Rachford splitting method with low computational complexity for binary logistic regression (2019)
  20. Chen, Wen-gu; Li, Peng: Truncated sparse approximation property and truncated (q)-norm minimization (2019)

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