Geodesic
Proximal Point Algorithm, Douglas-Rachford Algorithm and Alternating Projections: a Case Study
Journal of convex analysis, Tome 23 (2016) no. 1, pp. 237-261
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Many iterative methods for solving optimization or feasibility problems have been invented, and often convergence of the iterates to some solution is proven. Under favourable conditions, one might have additional bounds on the distance of the iterate to the solution leading thus to worst case estimates, i.e., how fast the algorithm must converge.
Classification : 65K05, 65K10, 90C25
Mots-clés : Alternating projections, convex feasibility problem, convex set, Douglas-Rachford algorithm, projection, proximal mapping, proximal point algorithm, proximity operator 
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     author = {H. H. Bauschke and M. N. Dao and D. Noll and H. M. Phan},
     title = {Proximal {Point} {Algorithm,} {Douglas-Rachford} {Algorithm} and {Alternating} {Projections:} a {Case} {Study}},
     journal = {Journal of convex analysis},
     pages = {237--261},
     year = {2016},
     volume = {23},
     number = {1},
     url = {http://geodesic.mathdoc.fr/item/JCA_2016_23_1_JCA_2016_23_1_a8/}
}
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H. H. Bauschke; M. N. Dao; D. Noll; H. M. Phan. Proximal Point Algorithm, Douglas-Rachford Algorithm and Alternating Projections: a Case Study. Journal of convex analysis, Tome 23 (2016) no. 1, pp. 237-261. http://geodesic.mathdoc.fr/item/JCA_2016_23_1_JCA_2016_23_1_a8/