A new conic method for unconstrained minimization

A modified secant method for unconstrained minimization

A Dimension–reducing Conic Method for Unconstrained Optimization

In this paper we present a new algorithm for finding the unconstrained minimum of a twice–continuously differentiable function f(x) in n variables. This algorithm is based on a conic model function, which does not involve the conjugacy matrix or the Hessian of the model function. The basic idea in this paper is to accelerate the convergence of the conic method choosing more appropriate points x...

A Multidimensional Bisection Method for Unconstrained Minimization Problem

An extension of a new multidimensional bisection method for minimizing function over simplex is proposed for solving nonlinear unconstrained minimization problem. The method does not require a differentiability of function, and is guaranteed to converge to the minimizer for the class of strictly unimodal functions. The computational results demonstrating an effectiveness of algorithm for minimi...

A Nonmonotone Second - Order Steplength Method for Unconstrained Minimization

In this paper, a nonmonotone method based on McCormick’s second-order Armijo’s step-size rule [7] for unconstrained optimization problems is proposed. Every limit point of the sequence generated by using this procedure is proved to be a stationary point with the second-order optimality conditions. Numerical tests on a set of standard test problems are presented and show that the new algorithm i...

A regularized limited-memory BFGS method for unconstrained minimization problems

The limited-memory BFGS (L-BFGS) algorithm is a popular method of solving large-scale unconstrained minimization problems. Since LBFGS conducts a line search with the Wolfe condition, it may require many function evaluations for ill-posed problems. To overcome this difficulty, we propose a method that combines L-BFGS with the regularized Newton method. The computational cost for a single iterat...