A numerical study of model-based methods for derivative-free optimization is presented. These methods typically include a geometry phase whose goal is to ensure the adequacy of the interpolation set. The paper studies the performance of an algorithm that dispenses with the geometry phase altogether (and therefore does not attempt to control the position of the interpolation set). Data are presented describing the evolution of the condition number of the interpolation matrix and the accuracy of the gradient estimate. The experiments are performed on smooth unconstrained optimization problems with dimensions ranging between 2 and 15
We present results from testing of parallel versions of algorithms for derivativefree optimization. ...
We introduce MNH, a new algorithm for unconstrained optimization when derivatives are unavailable, p...
Métodos de região de confiança formam uma classe de algoritmos iterativos amplamente utilizada em pr...
A numerical study of model-based methods for derivative-free optimization is presented. These method...
A numerical study of model-based methods for derivative-free optimization is presented. These method...
A numerical study of model-based methods for derivative-free optimization is presented. These method...
In this survey article we give the basic description of the interpolation based derivative free opti...
We present an introduction to a new class of derivative free methods for unconstrained optimization....
We present an introduction to a new class of derivative free methods for unconstrained optimization....
Abstract We consider derivative free methods based on sampling approaches for nonlinear optimizatio...
Derivative free optimization algorithms are implementations of trust region based derivative-free me...
We propose data profiles as a tool for analyzing the performance of derivative-free optimization sol...
Includes bibliographical references and index.Introduction -- Sampling and linear models -- Interpol...
A new derivative-free optimization method for unconstrained optimization of partially separable func...
Abstract—A new derivative-free optimization method for unconstrained optimization of partially separ...
We present results from testing of parallel versions of algorithms for derivativefree optimization. ...
We introduce MNH, a new algorithm for unconstrained optimization when derivatives are unavailable, p...
Métodos de região de confiança formam uma classe de algoritmos iterativos amplamente utilizada em pr...
A numerical study of model-based methods for derivative-free optimization is presented. These method...
A numerical study of model-based methods for derivative-free optimization is presented. These method...
A numerical study of model-based methods for derivative-free optimization is presented. These method...
In this survey article we give the basic description of the interpolation based derivative free opti...
We present an introduction to a new class of derivative free methods for unconstrained optimization....
We present an introduction to a new class of derivative free methods for unconstrained optimization....
Abstract We consider derivative free methods based on sampling approaches for nonlinear optimizatio...
Derivative free optimization algorithms are implementations of trust region based derivative-free me...
We propose data profiles as a tool for analyzing the performance of derivative-free optimization sol...
Includes bibliographical references and index.Introduction -- Sampling and linear models -- Interpol...
A new derivative-free optimization method for unconstrained optimization of partially separable func...
Abstract—A new derivative-free optimization method for unconstrained optimization of partially separ...
We present results from testing of parallel versions of algorithms for derivativefree optimization. ...
We introduce MNH, a new algorithm for unconstrained optimization when derivatives are unavailable, p...
Métodos de região de confiança formam uma classe de algoritmos iterativos amplamente utilizada em pr...