«unconstrained optimization problem»
۲۴۱۲ نتیجهA hybrid CG algorithm for nonlinear unconstrained optimization with application in image restoration
This paper presents a new hybrid conjugate gradient method for solving nonlinear unconstrained optimization problems; it is based on a combination of $RMIL$ (Rivaie-Mustafa-Ismail-Leong) and $hSM$ (hybrid Sulaiman- Mohammed) methods. The proposed algorithm enjoys the sufficient d
An Efficient FR-CD-Like Algorithm for Unconstrained Optimization
A hybrid conjugate gradient (CG) method is proposed for solving unconstrained optimization problems. The direction of the method is a combination of a three-term conjugate descent (CD) and Fletcher-Reeves (FR) CG directions. Also, it is close to the direction of the memoryless Br
Exploiting Negative Curvature Directions in Linesearch Methods for Unconstrained Optimization
In this paper we consider the deenition of new eecient linesearch algorithms for solving large scale unconstrained optimization problems which exploit the local nonconvexity of the objective function. Existing algorithms of this class compute, at each iteration, two search direct
An efficient improvement of the Newton method for solving nonconvex optimization problems
Newton method is one of the most famous numerical methods among the line search methods to minimize functions. It is well known that the search direction and step length play important roles in this class of methods to solve optimization problems. In this investigation, a new mod
an adaptive nonmonotone trust region method for unconstrained optimization problems based on a simple subproblem
using a simple quadratic model in the trust region subproblem, a new adaptive nonmonotone trust region method is proposed for solving unconstrained optimization problems. in our method, based on a slight modification of the proposed approach in (j. optim. theory appl. 158(2):626-
An adaptive nonmonotone trust region method for unconstrained optimization problems based on a simple subproblem
Using a simple quadratic model in the trust region subproblem, a new adaptive nonmonotone trust region method is proposed for solving unconstrained optimization problems. In our method, based on a slight modification of the proposed approach in (J. Optim. Theory Appl. 158(2):626-
A New Sufficient Descent Conjugate Gradient Method for Unconstrained Optimization
In this paper, a new conjugate conjugate method with sufficient descent property is proposed for the unconstrained optimization problem. An attractive property of the new method is that the descent direction generated by the method always possess the sufficient descent property,
Solving the Unconstrained Optimization Problems Using the Combination of Nonmonotone Trust Region Algorithm and Filter Technique
In this paper, we propose a new nonmonotone adaptive trust region method for solving unconstrained optimization problems that is equipped with the filter technique. In the proposed method, the various nonmonotone technique is used. Using this technique, the algorithm can advantag
Adaptation of Genetic Algorithms for Optimization Problem Solving
Since genetic algorithms (GAs) are inspired from the idea of evolution, it is natural to expect that besides original problem conversion to suit GA, also adaptation will be used for tuning internal parameters of genetic algorithms to speed up the iterative solution of the given p
On Comparison of Crisp, Fuzzy, Intuitionistic Fuzzy Unconstrained Optimization Problems Using Newton’s Method
This paper is focused on arithmetic operations fuzzy and intuitionistic numbers to solve the unconstrained optimization problems with triangular trapezoidal, number coefficients. The optimal solution obtained by Newton’s method, MATLAB outputs are also provided illustrative examp