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
References in zbMATH (referenced in 307 articles )
Showing results 1 to 20 of 307.
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- Altuntac, Erdem: Choice of the parameters in a primal-dual algorithm for Bregman iterated variational regularization (2021)
- Bello-Cruz, Yunier; Li, Guoyin; Nghia, Tran T. A.: On the linear convergence of forward-backward splitting method. I: Convergence analysis (2021)
- Bonettini, Silvia; Porta, Federica; Ruggiero, Valeria; Zanni, Luca: Variable metric techniques for forward-backward methods in imaging (2021)
- Chouzenoux, Emilie; Pesquet, Jean-Christophe; Riddell, Cyril; Savanier, Marion; Trousset, Yves: Convergence of proximal gradient algorithm in the presence of adjoint mismatch (2021)
- Dong, Qiao-Li; Li, Xiao-Huan; Cho, Yeol Je; Rassias, Themistocles M.: Multi-step inertial Krasnosel’skiǐ-Mann iteration with new inertial parameters arrays (2021)
- Gautam, Pankaj; Sahu, Daya Ram; Dixit, Avinash; Som, Tanmoy: Forward-backward-half forward dynamical systems for monotone inclusion problems with application to v-GNE (2021)
- Hodgkinson, Liam; Salomone, Robert; Roosta, Fred: Implicit Langevin algorithms for sampling from log-concave densities (2021)
- Jaramillo, Gabriela; Venkataramani, Shankar C.: A modified split Bregman algorithm for computing microstructures through Young measures (2021)
- Johnstone, Patrick R.; Eckstein, Jonathan: Single-forward-step projective splitting: exploiting cocoercivity (2021)
- Kadu, Ajinkya; van Leeuwen, Tristan; Batenburg, K. Joost: CoShaRP: a convex program for single-shot tomographic shape sensing (2021)
- Lindstrom, Scott B.; Sims, Brailey: Survey: sixty years of Douglas-Rachford (2021)
- Li, Yaguang; Xu, Wei; Gao, Xin: Graphical-model based high dimensional generalized linear models (2021)
- Messud, Jérémie; Poncet, Raphaël; Lambaré, Gilles: Optimal transport in full-waveform inversion: analysis and practice of the multidimensional Kantorovich-Rubinstein norm (2021)
- Pascal, Barbara; Pustelnik, Nelly; Abry, Patrice: Strongly convex optimization for joint fractal feature estimation and texture segmentation (2021)
- Pham, Minh; Lin, Xiaodong; Ruszczyński, Andrzej; Du, Yu: An outer-inner linearization method for non-convex and nondifferentiable composite regularization problems (2021)
- Rauhut, Holger; Stojanac, Željka: Tensor theta norms and low rank recovery (2021)
- Abe, Jiro; Yamagishi, Masao; Yamada, Isao: Linearly involved generalized Moreau enhanced models and their proximal splitting algorithm under overall convexity condition (2020)
- Banert, Sebastian; Ringh, Axel; Adler, Jonas; Karlsson, Johan; Öktem, Ozan: Data-driven nonsmooth optimization (2020)
- Bertocchi, Carla; Chouzenoux, Emilie; Corbineau, Marie-Caroline; Pesquet, Jean-Christophe; Prato, Marco: Deep unfolding of a proximal interior point method for image restoration (2020)
- Bolte, Jérôme; Chen, Zheng; Pauwels, Edouard: The multiproximal linearization method for convex composite problems (2020)