AbstractThe secant equation, which underlies all standard ‘quasi-Newton’ minimisation methods, arises from the use of a linear function to model the gradient along a chosen direction. We present new minimisation algorithms, derived by replacing this linear model with a more general one involving a free parameter, which is determined by using information contained in the current approximate Hessian. The use of such a model can give more flexibility in the criteria to be satisfied during the line-search. The new methods can operate as soon as a reasonable approximation to the Hessian has been accumulated and may, in one sense, be viewed as acceleration techniques for quasi-Newton methods
AbstractWe consider multistep quasi-Newton methods for unconstrained optimization. These methods wer...
Quasi-Newton methods are among the most practical and efficient iterative methods for solving uncons...
AbstractA bound on the possible deterioration in the condition number of the inverse Hessian approxi...
AbstractA bound on the possible deterioration in the condition number of the inverse Hessian approxi...
AbstractMost “quasi-Newton” methods in common use for function minimisation use a quadratic model to...
AbstractQuasi-Newton methods update, at each iteration, the existing Hessian approximation (or its i...
Quasi-Newton methods are often used in the frame of non-linear optimization. In those methods, the q...
Quasi-Newton methods are often used in the frame of non-linear optimization. In those methods, the q...
Many methods for solving minimization problems are variants of Newton method, which requires the spe...
This paper develops a modified quasi-Newton method for structured unconstrained optimization with pa...
AbstractThis paper presents a modified quasi-Newton method for structured unconstrained optimization...
Quasi-Newton methods are among the most practical and efficient iterative methods for solving uncons...
AbstractBy the use of a nonlinear model for the gradient of the objective function along a chosen di...
AbstractWe consider multi-step quasi-Newton methods for unconstrained optimization. These methods we...
AbstractQuasi-Newton methods for unconstrained minimization generate a sequence of matrices that can...
AbstractWe consider multistep quasi-Newton methods for unconstrained optimization. These methods wer...
Quasi-Newton methods are among the most practical and efficient iterative methods for solving uncons...
AbstractA bound on the possible deterioration in the condition number of the inverse Hessian approxi...
AbstractA bound on the possible deterioration in the condition number of the inverse Hessian approxi...
AbstractMost “quasi-Newton” methods in common use for function minimisation use a quadratic model to...
AbstractQuasi-Newton methods update, at each iteration, the existing Hessian approximation (or its i...
Quasi-Newton methods are often used in the frame of non-linear optimization. In those methods, the q...
Quasi-Newton methods are often used in the frame of non-linear optimization. In those methods, the q...
Many methods for solving minimization problems are variants of Newton method, which requires the spe...
This paper develops a modified quasi-Newton method for structured unconstrained optimization with pa...
AbstractThis paper presents a modified quasi-Newton method for structured unconstrained optimization...
Quasi-Newton methods are among the most practical and efficient iterative methods for solving uncons...
AbstractBy the use of a nonlinear model for the gradient of the objective function along a chosen di...
AbstractWe consider multi-step quasi-Newton methods for unconstrained optimization. These methods we...
AbstractQuasi-Newton methods for unconstrained minimization generate a sequence of matrices that can...
AbstractWe consider multistep quasi-Newton methods for unconstrained optimization. These methods wer...
Quasi-Newton methods are among the most practical and efficient iterative methods for solving uncons...
AbstractA bound on the possible deterioration in the condition number of the inverse Hessian approxi...