Minimizing two different upper bounds of the matrix which generates search directions of the nonlinear conjugate gradient method proposed by Dai and Liao, two modified conjugate gradient methods are proposed. Under proper conditions, it is briefly shown that the methods are globally convergent...

In this paper, we propose a strongly sub-feasible direction method for the solution of inequality constrained optimization problems whose objective functions are not necessarily differentiable. The algorithm combines the subgradient aggregation technique with the ideas of generalized cutting...

In this paper, a new trust region method is presented for general constrained optimization problem. In this algorithm, the trial step is obtained by solving two quadratic programming problems with bound constraints. The algorithm is implementable easily. Then we prove that the method is globally...

A globally convergent algorithm based on the stabilized sequential quadratic programming (sSQP) method is presented in order to solve optimization problems with equality constraints and bounds. This formulation has attractive features in the sense that constraint qualifications are not needed at...

In this paper, a new trust region method is presented for general constrained optimization problem. In this algorithm, the trial step is obtained by solving two quadratic programming problems with bound constraints. The algorithm is implementable easily. Then we prove that the method is globally...