References#

[ProxAdam]

Melchior, Joseph, Moolekamp. “Proximal Adam: Robust Adaptive Update Scheme for Constrained Optimization” (2020).

[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.

[CVS]

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.

[PD3O]

Yan, Ming. “A new primal-dual algorithm for minimizing the sum of three functions with a linear operator.” arXiv preprint arXiv:1611.09805 (2018).

[PSA]

Condat L., Kitahara D., Contreras A., and Hirabayashi A. “Proximal Splitting Algorithms for Convex Optimization: A Tour of Recent Advances, with New Twists.” arXiv preprint arXiv:1912.00137 (2021).

[dPSA]

Condat L., Malinovsky G., and Richtarik, P.. “Distributed Proximal Splitting Algorithms with rates and acceleration.” Frontiers in Signal Processing (2022) 1:776825. doi: 10.3389/frsip.2021.776825

[CPA]

Chambolle, A., & Pock, T. (2011). A first-order primal-dual algorithm for convex problems with applications to imaging. Journal of mathematical imaging and vision, 40(1), 120-145.

[FB]

Lions, Pierre-Louis, and Bertrand Mercier. “Splitting algorithms for the sum of two nonlinear operators.” SIAM Journal on Numerical Analysis 16.6 (1979): 964-979.

[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).

[NLCP]

Valkonen, Tuomo, “A primal-dual hybrid gradient method for nonlinear operators with applications to MRI.” Inverse Problems 30 (2014), 055012, doi:10.1088/0266-5611/30/5/055012.

[PP]

Rockafellar, R. Tyrrell. “Monotone operators and the proximal point algorithm.” SIAM journal on control and optimization 14.5 (1976): 877-898.

[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).

[FINUFFT]

Barnett, Alexander H., Jeremy Magland, and Ludvig af Klinteberg. “A parallel nonuniform fast Fourier transform library based on an “Exponential of semicircle” kernel.” SIAM Journal on Scientific Computing 41.5 (2019): C479-C504.

[cuFINUFFT]

Shih, Yu-hsuan, et al. “cuFINUFFT: a load-balanced GPU library for general-purpose nonuniform FFTs.” 2021 IEEE International Parallel and Distributed Processing Symposium Workshops (IPDPSW). IEEE, 2021.

[NumOpt_NocWri]

Wright S, Nocedal J. “Numerical optimization”. Springer Science. 1999 Apr 3;35(67-68):7.

[PoCS_Dykstra]

Boyle JP, Dykstra RL. “A method for finding projections onto the intersection of convex sets in Hilbert spaces.” Advances in order restricted statistical inference 1986 (pp. 28-47). Springer, New York, NY.

[WelfordAlg]

Welford, B. P. “Note on a method for calculating corrected sums of squares and products.” Technometrics 4.3 (1962): 419-420.

[ULA]

Alain Durmus, and Éric Moulines. “Nonasymptotic convergence analysis for the unadjusted Langevin algorithm.” The Annals of Applied Probability 27(3) 1551-1587, 2017.

[MYULA]

Alain Durmus, Éric Moulines, and Marcelo Pereyra, “Efficient Bayesian Computation by Proximal Markov Chain Monte Carlo: When Langevin Meets Moreau”, SIAM Journal of Imaging Science, 2018

[UQ_MCMC]

Cai, Xiaohao, Marcelo Pereyra, and Jason D. McEwen. “Uncertainty quantification for radio interferometric imaging–I. Proximal MCMC methods.” Monthly Notices of the Royal Astronomical Society 480.3 (2018): 4154-4169.