The method of Conjugate Gradient (CG) is a key component optimization methods that aren't bound by local convergence characteristics. In this study, we created KHI3, novel search direction in the Algorithm. approach satisfies regression criterion. overall proposed technique has also been proved utilizing Wolff line words. A new algorithm for solving large-scale unconstrained issue particularly ...

The gradient method is a very efficient iterative technique for solving unconstrained optimization problems. Motivated by recent modifications of some variants the SM method, this study proposed two methods that are globally convergent as well computationally efficient. Each under influence backtracking line search. Results obtained from numerical implementation these and performance profiling ...

A new nonmonotone algorithm is proposed and analyzed for unconstrained nonlinear optimization. The nonmonotone techniques applied in this algorithm are based on the estimate sequence proposed by Nesterov (Introductory Lectures on Convex Optimization: A Basic Course, 2004) for convex optimization. Under proper assumptions, global convergence of this algorithm is established for minimizing genera...

Constrained optimization via the Genetic Algorithm (GA) is often a challenging endeavor, as the GA is most directly suited to unconstrained optimization. Traditionally, external penalty functions have been used to convert a constrained optimization problem into an unconstrained problem for GA-based optimization. This approach requires the somewhat arbitrary selection of penalty draw-down coeffi...

The major interest of this paper is to show that, at least in theory, a pair of primal and dual "e-optimal solutions" to a general linear program in Karmarkar's standard form can be obtained by solving an unconstrained convex program. Hence unconstrained convex optimization methods are suggested to be carefully reviewed for this purpose.

A new approach to the problem of minimizing L2sensitivity subject to L2-norm scaling constraints for state-space digital filters is proposed. Using linearalgebraic techniques, the problem at hand is converted into an unconstrained optimization problem, and the unconstrained problem obtained is then solved by applying an efficient quasi-Newton algorithm. Computer simulation results are presented...