Abstract. The paper gives a common theoretical treatment for gradient and Newton type methods for general classes of problems. First, for Euler-Lagrange equations Newton’s method is characterized as an (asymptoti-cally) optimal variable steepest descent method. Second, Sobolev gradient type minimization is developed for general problems using a continuous Newton method which takes into account a ‘boundary condition ’ operator. 1
A Sobolev gradient of a real-valued functional is a gradient of that functional taken relative to th...
The renewed interest in Steepest Descent (SD) methods following the work of Barzilai and Borwein [2]...
The nonlinear minimization problem is to find a (local) minimizer for an objective function f(·), wh...
This paper gives a common theoretical treatment for gradient and Newton type methods for general cl...
This paper presents a general and comprehensive description of Optimization Methods, and Algorithms ...
This paper presents a general and comprehensive description of Optimization Methods, and Algorithms ...
This paper presents a general and comprehensive description of Optimization Methods, and Algorithms ...
This paper presents a general and comprehensive description of Optimization Methods, and Algorithms ...
A Sobolev gradient of a real-valued functional on a Hilbert space is a gradient of that functional t...
first-order, second-order necessary sufficiency under convexity Algorithms for Univariate Optimizati...
Não disponívelThis paper is intended to present gradient and related methods to solve operator equat...
It is well known that the minimization of a smooth function f (x) is equivalent to minimizing its gr...
The renewed interest in Steepest Descent (SD) methods following the work of Barzilai and Borwein [2]...
The renewed interest in Steepest Descent (SD) methods following the work of Barzilai and Borwein [2]...
The renewed interest in Steepest Descent (SD) methods following the work of Barzilai and Borwein [2]...
A Sobolev gradient of a real-valued functional is a gradient of that functional taken relative to th...
The renewed interest in Steepest Descent (SD) methods following the work of Barzilai and Borwein [2]...
The nonlinear minimization problem is to find a (local) minimizer for an objective function f(·), wh...
This paper gives a common theoretical treatment for gradient and Newton type methods for general cl...
This paper presents a general and comprehensive description of Optimization Methods, and Algorithms ...
This paper presents a general and comprehensive description of Optimization Methods, and Algorithms ...
This paper presents a general and comprehensive description of Optimization Methods, and Algorithms ...
This paper presents a general and comprehensive description of Optimization Methods, and Algorithms ...
A Sobolev gradient of a real-valued functional on a Hilbert space is a gradient of that functional t...
first-order, second-order necessary sufficiency under convexity Algorithms for Univariate Optimizati...
Não disponívelThis paper is intended to present gradient and related methods to solve operator equat...
It is well known that the minimization of a smooth function f (x) is equivalent to minimizing its gr...
The renewed interest in Steepest Descent (SD) methods following the work of Barzilai and Borwein [2]...
The renewed interest in Steepest Descent (SD) methods following the work of Barzilai and Borwein [2]...
The renewed interest in Steepest Descent (SD) methods following the work of Barzilai and Borwein [2]...
A Sobolev gradient of a real-valued functional is a gradient of that functional taken relative to th...
The renewed interest in Steepest Descent (SD) methods following the work of Barzilai and Borwein [2]...
The nonlinear minimization problem is to find a (local) minimizer for an objective function f(·), wh...