Add to ORKGInternational audienceWe propose a relaxed-inertial proximal point type algorithm for solving optimization problems consisting in minimizing strongly quasiconvex functions whose variables lie in finitely dimensional linear subspaces. A relaxed version of the method where the constraint set is only closed and convex is also discussed, and so is the case of a quasiconvex objective function. Numerical experiments illustrate the theoretical results
Numerical methods of mathematical programming are considered in the paper aiming at the algorithm de...
Several optimization schemes have been known for convex optimization problems. However, numerical al...
AbstractA finite algorithm is presented for solving the quasi-concave minimization problem subject t...
International audienceWe propose a relaxed-inertial proximal point type algorithm for solving optimi...
International audienceIn a Hilbert space setting, the authors recently introduced a general class of...
This paper extends the full convergence of the classic proximal point method to solve continuous qua...
A class of minimax problems is considered. We approach it with the techniques of quasiconvex optimiz...
This paper proposes an implementable proximal quasi-Newton method for minimizing a nondifferentiable...
ABSTRACT In this paper we propose an inexact proximal point method to solve multiobjective minimiza...
The proximal point algorithm is classical and popular in the community of optimization. In practice,...
In this article we develop a global optimization algorithm for quasiconvex programming where the obj...
We introduce a proximal algorithm using quasidistances for multiobjective minimization problems with...
International audienceWe present an inexact proximal point algorithm using quasi distances to solve ...
In this paper we propose an extension of the proximal point method to solve minimization problems wi...
This paper demonstrates a customized application of the classical proximal point algorithm (PPA) to ...
Numerical methods of mathematical programming are considered in the paper aiming at the algorithm de...
Several optimization schemes have been known for convex optimization problems. However, numerical al...
AbstractA finite algorithm is presented for solving the quasi-concave minimization problem subject t...
International audienceWe propose a relaxed-inertial proximal point type algorithm for solving optimi...
International audienceIn a Hilbert space setting, the authors recently introduced a general class of...
This paper extends the full convergence of the classic proximal point method to solve continuous qua...
A class of minimax problems is considered. We approach it with the techniques of quasiconvex optimiz...
This paper proposes an implementable proximal quasi-Newton method for minimizing a nondifferentiable...
ABSTRACT In this paper we propose an inexact proximal point method to solve multiobjective minimiza...
The proximal point algorithm is classical and popular in the community of optimization. In practice,...
In this article we develop a global optimization algorithm for quasiconvex programming where the obj...
We introduce a proximal algorithm using quasidistances for multiobjective minimization problems with...
International audienceWe present an inexact proximal point algorithm using quasi distances to solve ...
In this paper we propose an extension of the proximal point method to solve minimization problems wi...
This paper demonstrates a customized application of the classical proximal point algorithm (PPA) to ...
Numerical methods of mathematical programming are considered in the paper aiming at the algorithm de...
Several optimization schemes have been known for convex optimization problems. However, numerical al...
AbstractA finite algorithm is presented for solving the quasi-concave minimization problem subject t...