Abstract
We present an iterative method for finding zeroes of maximal monotone operators in a real Hilbert space. The underlying idea relies upon the discretization of a first order dissipative dynamical system which allows us to preserve the local feature, as well as to obtain convergence results. The main theorems do not only recover known convergence results of standard and inertial proximal methods, but also provide a theoretical basis for the application of new iterative methods.
References
- F. Alvarez: On the minimizing property of a second order dissipative dynamical system in Hilbert spaces, SIAM J. Control Optimization 38(4) (2000) 1102–1119.https://doi.org/10.1137/S0363012998335802Google Scholar
- F. Alvarez, H. Attouch: An inertial proximal method for monotone operators via discretization of a nonlinear oscillator with damping, Set-Valued Anal. 9 (2001) 3–11.https://doi.org/10.1023/A:1011253113155Google Scholar
- F. Alvarez, H. Attouch, J. Bolte, P. Redont: A second-order gradient-like dissipative dynamical system with Hessian driven damping. Application to optimization and mechanics, J. Math. Pures Appl., IX. Sér. 81(8) (2002) 747–779.https://doi.org/10.1016/S0021-7824(01)01253-3Google Scholar
- F. Alvarez, J. M. Pérez: A dynamical system associated with Newton’s method for parametric approximations of convex minimization problems, Appl. Math. Optimization 38 (1998) 193–217.https://doi.org/10.1007/s002459900088Google Scholar
- Ph. N. Anh, L. D. Muu, V. H. Nguyen, J.-J. Strodiot: Contraction principle to implement the proximal point method for multivalued monotone variational inequalities, J. Optimization Theory Appl. 124(2) (2005) 285–306.https://doi.org/10.1007/s10957-004-0926-0Google Scholar
- H. Attouch, X. Goudon, P. Redont: The heavy ball system with friction method. I: The continuous dynamical system, Commun. Contemp. Math. 2(1) (2000) 1–34.https://doi.org/10.1142/S0219199700000025Google Scholar
- H. Attouch, P. Redont: The second-order in time continuous Newton method, in: Approximation Optimization and Mathematical Economics, M. Lassonde (ed.), Physica, Heidelberg (2001) 25–36.https://doi.org/10.1007/978-3-642-57592-1_2Google Scholar
- A. Auslender: Optimisation. Méthodes Numériques, Applicable Mathematics Series, Pitman, London (1976).Google Scholar
- H. Brézis: Opérateurs Maximaux Monotones, Math. Stud. 5, North-Holland, Amsterdam (1973).Google Scholar
- R. S. Burachik, A. N. Iusem, B. F. Svaiter: Enlargement of maximal monotone operators with application to variational inequalities, Set-Valued Anal. 5 (1997) 159–180.https://doi.org/10.1023/A:1008615624787Google Scholar
- F. Jules, P. E. Maingé: Numerical appraches to stationnary solution of a second order dissipative dynamical system, Optimization 51(2) (2002) 235–255.https://doi.org/10.1080/02331930290019404Google Scholar
- B. Lemaire: The proximal algorithm, in: New Methods in Optimization and their Industrial Uses, J.-P. Penot (ed.), Internat. Ser. Numer. Math. 87, Birkhäuser, Basel (1989) 73–87.Google Scholar
- A. Moudafi: A perturbed inertial proximal method for maximal monotone operators, Commun. Appl. Nonlinear Anal. 11(3) (2004) 101–108.Google Scholar
- A. Moudafi, E. Elisabeth: An approximate inertial proximal method using enlargement of a maximal monotone operator, Int. J. Pure Appl. Math. 5(3) (2003) 283–299.Google Scholar
- Z. Opial: Weak convergence of the sequence of successive approximations for nonexpansive mappings, Bull. Amer. Math. Soc. 73 (1967) 591–597.https://doi.org/10.1090/S0002-9904-1967-11761-0Google Scholar
- R. T. Rockafellar: Monotone operators and the proximal point algorithm, SIAM J. Control Optimization 14(5) (1976) 877–898.https://doi.org/10.1137/0314056Google Scholar
- M. V. Solodov, B. F. Svaiter: A hybrid projection-proximal point algorithm, J. Convex Analysis 6(1) (1999) 59–70.https://doi.org/10.68381/jca06005Google Scholar
Suggested citation
Published by Heldermann Verlag, 2007. Rights now held by Banach Press LLC.
