L. Armijo, Minimization of functions having Lipschitz continuous first partial derivatives, Pacific Journal of Mathematics, vol.16, issue.1, pp.1-3, 1966.
DOI : 10.2140/pjm.1966.16.1

I. Bongartz, A. R. Conn, N. Gould, and P. L. Toint, CUTE: constrained and unconstrained testing environment, ACM Transactions on Mathematical Software, vol.21, issue.1, pp.123-260, 1995.
DOI : 10.1145/200979.201043

URL : http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.38.2938

R. E. Bellman, R. E. Kalaba, J. [. Lockett, R. B. Dennis, . [. Schnabel et al., Convergence of some algorithms for convex minimization Numerical Methods for Unconstrained Optimization and Nonlinear Equations Some numercial experiments with variable-storage quasi-Newton algorithms An effective algorithm for minimization Inexact proximal point algorithms and descent methods in optimization Self-adaptive inexact proximal point methods A method for the solution of certain nonlinear problems in least squares, Numerical Inversion of the Laplace Transform Mathematical Programming Mathematical ProgrammingLS97] C. Lemaréchal and C. Sagastizábal. Variable metric bundle methods: from conceptual to implementable forms. Mathematical Programming, pp.143-144261, 1944.

D. W. Marquardt, An Algorithm for Least-Squares Estimation of Nonlinear Parameters, Journal of the Society for Industrial and Applied Mathematics, vol.11, issue.2, pp.431-441, 1963.
DOI : 10.1137/0111030

B. Martinet, Régularisation d'inéquations variationelles par approximation successives, pp.154-158, 1970.

J. J. Moré, Recent developments in algorithms and software for trust region methods A proximal approach to the inversion of illconditioned matrices, Mathematical Programming, the State of the ArtNoc80] J. Nocedal. Updating quasi-Newton matrices with limited storage. Mathematics of Computation, pp.258-28723, 1980.

J. [. Qi, . T. Sunroc76-]-r, . V. Rockafellar-[-ss99-]-m, B. F. Solodov, . Svaiter et al., A nonsmooth version of Newton's method Mathematical Programming Augmented Lagrangians and applications of the proximal point algorithm in convex programming A hybrid projection-proximal point algorithm A unified framework for some inexact proximal point algorithms. Numerical functional analysis and optimization On Convergence Properties of Algorithms for Unconstrained Minimization, Convergence conditions for ascent methods, pp.97-116059, 1969.