A new norm-relaxed SQP algorithm with global convergence

On the Global Convergence of a Filter--SQP Algorithm

A Stabilized Sqp Method: Global Convergence

Stabilized sequential quadratic programming (SQP) methods for nonlinear optimization are designed to provide a sequence of iterates with fast local convergence regardless of whether or not the active-constraint gradients are linearly dependent. This paper concerns the global convergence properties of a stabilized SQP method with a primal-dual augmented Lagrangian merit function. The proposed me...

A Second Derivative SQP Method: Global Convergence

Gould and Robinson (NAR 08/18, Oxford University Computing Laboratory, 2008) gave global convergence results for a second-derivative SQP method for minimizing the exact l1-merit function for a fixed value of the penalty parameter. To establish this result, we used the properties of the so-called Cauchy step, which was itself computed from the so-called predictor step. In addition, we allowed fo...

Global Convergence of Trust - Region SQP

Global convergence to rst-order critical points is proved for two trust-region SQP-lter algorithms of the type introduced by Fletcher and Leyyer (1997). The algorithms allow for an approximate solution of the quadratic subproblem and incorporate the safeguarding tests described in Fletcher, Leyyer and Toint (1998). The rst algorithm decomposes the step into its normal and tangential components ...

Global Convergence of a New Nonmonotone Algorithm

Abstract: In this study, we study the application of a kind of nonmonotone line search in BFGS algorithm for solving unconstrained optimization problems. This nonmonotone line search is belongs to Armijo-type line searches and when the step size is being computed at each iteration, the initial test step size can be adjusted according to the characteristics of objective functions. The global con...