[1] H. Attouch, J. Bolte, On the convergence of the proximal algorithm for nonsmooth functions in-volving analytic features, Mathematical Programming 116(1-2) Series B, 5-16, 2009
[2] H. Attouch, J. Bolte, P. Redont, A. Soubeyran, Proximal alternating minimization and projec-tion methods for nonconvex problems: an approach based on the Kurdyka- Lojasiewicz inequality,Mathematics of Operations Research 35(2), 438-457, 2010
[3] H. Attouch, J. Bolte, B.F. Svaiter, Convergence of descent methods for semi-algebraic and tameproblems: proximal algorithms, forward-backward splitting, and regularized Gauss-Seidel methods, Mathematical Programming 137(1-2) Series A, 91-129, 2013
[4] H. Attouch, Z. Chbani, J. Peypouquet, P. Redont, Fast convergence of inertial dynamics andalgorithms with asymptotic vanishing viscosity, Math. Program. 168(1-2) Ser. B, 123-175, 2018
[5] P. B´egout, J. Bolte, M.A. Jendoubi, On damped second-order gradient systems, Journal of Differ-ential Equations (259), 3115-3143, 2015
[6] J. Bolte, S. Sabach, M. Teboulle, Proximal alternating linearized minimization for nonconvex andnonsmooth problems, Mathematical Programming Series A (146)(1-2), 459-494, 2014
[7] J. Bolte, A. Daniilidis, A. Lewis, The Lojasiewicz inequality for nonsmooth subanalytic functionswith applications to subgradient dynamical systems, SIAM Journal on Optimization 17(4), 1205-1223, 2006
[8] J. Bolte, A. Daniilidis, A. Lewis, M. Shiota, Clarke subgradients of stratifiable functions, SIAMJournal on Optimization 18(2), 556-572, 2007
[9] J. Bolte, A. Daniilidis, O. Ley, L. Mazet, Characterizations of Lojasiewicz inequalities: subgradientflows, talweg, convexity, Transactions of the American Mathematical Society 362(6), 3319-3363,2010
[10] R.I. Bot¸, E.R. Csetnek, S.C. Laszlo, Approaching nonsmooth nonconvex minimization throughsecond-order proximal-gradient dynamical systems, Journal of Evolution Equations 18(3), 1291-1318, 2018
[11] R.I. Bot¸, E.R. Csetnek, S.C. Laszlo, An inertial forward-backward algorithm for minimizing thesum of two non-convex functions, Euro Journal on Computational Optimization 4(1), 3-25, 2016
[12] R.I. Bot¸, E.R. Csetnek, S.C. Laszlo, A second order dynamical approach withvariable damping to nonconvex smooth minimization, Applicable Analysis (2018),https://doi.org/10.1080/00036811.2018.1495330
[13] P. Frankel, G. Garrigos, J. Peypouquet, Splitting Methods with Variable Metric forKurdyka Lojasiewicz Functions and General Convergence Rates, Journal of Optimization Theoryand Applications, 165(3), 874900, 2015
[14] K. Kurdyka, On gradients of functions definable in o-minimal structures, Annales de l’institutFourier (Grenoble) 48(3), 769-783, 1998
[15] S.C. Laszlo, Convergence rates for an inertial algorithm of gradient type associated to a smoothnonconvex minimization, https://arxiv.org/abs/1807.0038721
[16] G. Li, T. K. Pong, Calculus of the Exponent of Kurdyka- Lojasiewicz Inequality and Its Applicationsto Linear Convergence of First-Order Methods, Foundations of Computational Mathematics, 1-34, 2018
[17] S. Lojasiewicz, Une propriete topologique des sous-ensembles analytiques reels, Les Equations auxDerivees Partielles, Editions du Centre National de la Recherche Scientifique Paris, 87-89, 1963
[18] Y.E. Nesterov, A method for solving the convex programming problem with convergence rateO(1/k2), (Russian) Dokl. Akad. Nauk SSSR 269(3), 543-547, 1983
[19] Y. Nesterov, Introductory lectures on convex optimization: a basic course. Kluwer Academic Pub-lishers, Dordrecht, 2004
[20] B. T. Polyak, Some methods of speeding up the convergence of iteration methods, U.S.S.R. Comput.Math. Math. Phys., 4(5):1-17, 1964
[21] R.T. Rockafellar, R.J.-B. Wets, Variational Analysis, Fundamental Principles of Mathematical Sciences 317, Springer-Verlag, Berlin, 1998
[22] W. Su, S. Boyd, E.J. Candes, A differential equation for modeling Nesterov’s accelerated gradientmethod: theory and insights, Journal of Machine Learning Research, 17, 1-43, 2016