An unconstrained minimization method which is based on Powell's derivative free method is presented. The proposed method retains the termination prop-erties of Powell's method and it can be successfully applied to problems with imprecise function values. The ability of this method to cope with imprecise or noisy problems is due to the fact that it proceeds solely by comparing the relative size of the function values. The method has been implemented and tested, and performance information is given
We consider the problem of unconstrained minimization of a smooth objective function in ℝn in a sett...
A new accelerated gradient method for finding the minimum of a function f(x) whose variables are unc...
We consider the unconstrained optimization of a function when each function evaluation is subject to...
A simple derivative free optimization method is presented. Some examples are provided showing the sp...
Derivative-free optimization has shown advantage in solving sophisticated problems such as policy se...
This outstanding text for graduate students and researchers proposes improvements to existing algori...
We propose a new class of rigorous methods for derivative-free optimization with the aim of deliveri...
AbstractA new algorithm for unconstrained optimization is presented which is based on a modified one...
Existing algorithms are examined, with particular attention given to their merits and defects, in or...
A new derivative-free optimization method for unconstrained optimization of partially separable func...
The problem of minimizing a multivariate function is recurrent in many disciplines as Physics, Mat...
Abstract—A new derivative-free optimization method for unconstrained optimization of partially separ...
In this work, we propose a new globally convergent derivative-free algorithm for the minimization of...
In this paper we propose a new derivative-free algorithm for linearly constrained finite minimax pro...
In this paper, we consider the unconstrained optimization problem under the following situation: (S1...
We consider the problem of unconstrained minimization of a smooth objective function in ℝn in a sett...
A new accelerated gradient method for finding the minimum of a function f(x) whose variables are unc...
We consider the unconstrained optimization of a function when each function evaluation is subject to...
A simple derivative free optimization method is presented. Some examples are provided showing the sp...
Derivative-free optimization has shown advantage in solving sophisticated problems such as policy se...
This outstanding text for graduate students and researchers proposes improvements to existing algori...
We propose a new class of rigorous methods for derivative-free optimization with the aim of deliveri...
AbstractA new algorithm for unconstrained optimization is presented which is based on a modified one...
Existing algorithms are examined, with particular attention given to their merits and defects, in or...
A new derivative-free optimization method for unconstrained optimization of partially separable func...
The problem of minimizing a multivariate function is recurrent in many disciplines as Physics, Mat...
Abstract—A new derivative-free optimization method for unconstrained optimization of partially separ...
In this work, we propose a new globally convergent derivative-free algorithm for the minimization of...
In this paper we propose a new derivative-free algorithm for linearly constrained finite minimax pro...
In this paper, we consider the unconstrained optimization problem under the following situation: (S1...
We consider the problem of unconstrained minimization of a smooth objective function in ℝn in a sett...
A new accelerated gradient method for finding the minimum of a function f(x) whose variables are unc...
We consider the unconstrained optimization of a function when each function evaluation is subject to...