References¶
- ProxAlg
Parikh, Neal, and Stephen Boyd. “Proximal algorithms.” Foundations and Trends in optimization 1.3 (2014): 127-239.
- FirstOrd
Beck, Amir. First-order methods in optimization. Society for Industrial and Applied Mathematics, 2017.
- OnKerLearn
Martins, André FT, et al. “Online multiple kernel learning for structured prediction.” arXiv preprint arXiv:1010.2770 (2010).
- ProxSplit
Combettes, Patrick L., and Jean-Christophe Pesquet. “Proximal splitting methods in signal processing.” Fixed-point algorithms for inverse problems in science and engineering. Springer, New York, NY, 2011. 185-212.
- FuncSphere
Simeoni, Matthieu Martin Jean-Andre. Functional Inverse Problems on Spheres: Theory, Algorithms and Applications. No. THESIS. EPFL, 2020.
- PDS
Condat, Laurent. “A primal–dual splitting method for convex optimization involving Lipschitzian, proximable and linear composite terms.” Journal of Optimization Theory and Applications 158.2 (2013): 460-479.
- APGD
Liang, Jingwei, Tao Luo, and Carola-Bibiane Schönlieb. “Improving” Fast Iterative Shrinkage-Thresholding Algorithm”: Faster, Smarter and Greedier.” arXiv preprint arXiv:1811.01430 (2018).
- P2
Jain, Raj, and Imrich Chlamtac. “The P2 algorithm for dynamic calculation of quantiles and histograms without storing observations.” Communications of the ACM 28.10 (1985): 1076-1085.
- GaussProcesses
Rasmussen, Carl Edward, and C. K. Williams. “Gaussian processes for machine learning, vol. 1.” (2006).
- SubGauss
Aziznejad, Shayan, and Michael Unser. “An L1 representer theorem for multiple-kernel regression.” arXiv preprint arXiv:1811.00836 (2018).