Add to ORKGAbstract. In this paper we present a method for minimization of anondifferentiable function. The method uses trust region strategy combined with a conjugate subgradient method. It is proved that thesequence of points generated by the algorithm has an accumulation pointwhich satisfies the first order necessary and sufficient conditions
We present a convex nondifferentiable minimization algorithm of proximal bundle type that does not r...
This work deals with optimisation problems in which the numerical cost associated with the evaluatio...
© 2019, Springer Science+Business Media, LLC, part of Springer Nature. We suggest a conjugate subgra...
The minimization of a particular nondifferentiable function is considered. The first and second orde...
Three fundamental convergence properties of trust region (TR) methods for solving nonsmooth unconstr...
We introduce a trust region algorithm for minimization of nonsmooth functions with linear constraint...
In this paper we study the minimization of a nonsmooth black-box type function, without assuming any...
Nonsmooth optimization consists of minimizing a continuous function by systematically choosing itera...
In this paper a new algorithm for minimizing locally Lipschitz functions is developed. Descent direc...
2013-2014 > Academic research: refereed > Publication in refereed journalVersion of RecordPublishe
A general family of trust region algorithms for nonsmooth optimization is considered. Conditions for...
We present a trust-region method for minimizing a general differentiable function restricted to an a...
Abstract. We consider methods for large-scale unconstrained minimization based on finding an approxi...
Based on the notion of the ε -subgradient, we present a unified technique to establish convergence p...
Convexity is an essential characteristic in optimization. In reality, many optimization problems are...
We present a convex nondifferentiable minimization algorithm of proximal bundle type that does not r...
This work deals with optimisation problems in which the numerical cost associated with the evaluatio...
© 2019, Springer Science+Business Media, LLC, part of Springer Nature. We suggest a conjugate subgra...
The minimization of a particular nondifferentiable function is considered. The first and second orde...
Three fundamental convergence properties of trust region (TR) methods for solving nonsmooth unconstr...
We introduce a trust region algorithm for minimization of nonsmooth functions with linear constraint...
In this paper we study the minimization of a nonsmooth black-box type function, without assuming any...
Nonsmooth optimization consists of minimizing a continuous function by systematically choosing itera...
In this paper a new algorithm for minimizing locally Lipschitz functions is developed. Descent direc...
2013-2014 > Academic research: refereed > Publication in refereed journalVersion of RecordPublishe
A general family of trust region algorithms for nonsmooth optimization is considered. Conditions for...
We present a trust-region method for minimizing a general differentiable function restricted to an a...
Abstract. We consider methods for large-scale unconstrained minimization based on finding an approxi...
Based on the notion of the ε -subgradient, we present a unified technique to establish convergence p...
Convexity is an essential characteristic in optimization. In reality, many optimization problems are...
We present a convex nondifferentiable minimization algorithm of proximal bundle type that does not r...
This work deals with optimisation problems in which the numerical cost associated with the evaluatio...
© 2019, Springer Science+Business Media, LLC, part of Springer Nature. We suggest a conjugate subgra...