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).