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Regularized Lotka-Volterra Dynamical System as Continuous Proximal-Like Method in Optimization

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  • H. Attouch

    (Université)

  • M. Teboulle

    (Tel-Aviv University)

Abstract

We introduce and study a new type of dynamical system which combines the continuous gradient method with a nonlinear Lotka-Volterra (LV) type of differential system within a logarithmic-quadratic proximal scheme. We prove a global existence and viability result for the resulting trajectory which holds for a general smooth function. The asymptotic behavior of the produced trajectory is analyzed and global convergence of the trajectory to a minimizer of the convex minimization problem over the nonnegative orthant is established. The implicit discretization which is at the origin of the proposed continuous dynamical system is an interior proximal scheme for minimizing a closed proper convex function, and convergence results and properties of the resulting discrete scheme are also established. We show finally that the trajectories of the family of regularized Lotka-Volterra systems, parametrized by the positive parameter associated with the quadratic proximal term, are uniformly convergent to the solution of the classical LV-dynamical system, as the parameter associated with the proximal term approaches zero.

Suggested Citation

  • H. Attouch & M. Teboulle, 2004. "Regularized Lotka-Volterra Dynamical System as Continuous Proximal-Like Method in Optimization," Journal of Optimization Theory and Applications, Springer, vol. 121(3), pages 541-570, June.
    • Handle: RePEc:spr:joptap:v:121:y:2004:i:3:d:10.1023_b:jota.0000037603.51578.45
    DOI: 10.1023/B:JOTA.0000037603.51578.45
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    References listed on IDEAS

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    1. Alfredo N. Iusem & B. F. Svaiter & Marc Teboulle, 1994. "Entropy-Like Proximal Methods in Convex Programming," Mathematics of Operations Research, INFORMS, vol. 19(4), pages 790-814, November.
    2. Marc Teboulle, 1992. "Entropic Proximal Mappings with Applications to Nonlinear Programming," Mathematics of Operations Research, INFORMS, vol. 17(3), pages 670-690, August.
    3. Alfred Auslender & Marc Teboulle & Sami Ben-Tiba, 1999. "Interior Proximal and Multiplier Methods Based on Second Order Homogeneous Kernels," Mathematics of Operations Research, INFORMS, vol. 24(3), pages 645-668, August.
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    Cited by:

    1. Sylvain Sorin, 2023. "Continuous Time Learning Algorithms in Optimization and Game Theory," Dynamic Games and Applications, Springer, vol. 13(1), pages 3-24, March.
    2. Paul-Emile Maingé, 2009. "Asymptotic convergence of an inertial proximal method for unconstrained quasiconvex minimization," Computational Optimization and Applications, Springer, vol. 45(4), pages 631-644, December.
    3. Papa Quiroz, E.A. & Mallma Ramirez, L. & Oliveira, P.R., 2015. "An inexact proximal method for quasiconvex minimization," European Journal of Operational Research, Elsevier, vol. 246(3), pages 721-729.
    4. Papa Quiroz, E.A. & Roberto Oliveira, P., 2012. "An extension of proximal methods for quasiconvex minimization on the nonnegative orthant," European Journal of Operational Research, Elsevier, vol. 216(1), pages 26-32.
    5. Souza, Sissy da S. & Oliveira, P.R. & da Cruz Neto, J.X. & Soubeyran, A., 2010. "A proximal method with separable Bregman distances for quasiconvex minimization over the nonnegative orthant," European Journal of Operational Research, Elsevier, vol. 201(2), pages 365-376, March.

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