A globally and superlinearly convergent trust-region SQP method without a penalty function for nonlinearly constrained optimization
نویسندگان
چکیده
In this paper, we propose a new trust-region SQP method, which uses no penalty function, for solving nonlinearly constrained optimization problem. Our method consists of alternate two algorithms. Specifically, we alternately proceed the feasibility restoration algorithm and the objective function minimization algorithm. The global and superlinear convergence property of the proposed method is shown.
منابع مشابه
Superlinearly convergent exact penalty projected structured Hessian updating schemes for constrained nonlinear least squares: asymptotic analysis
We present a structured algorithm for solving constrained nonlinear least squares problems, and establish its local two-step Q-superlinear convergence. The approach is based on an adaptive structured scheme due to Mahdavi-Amiri and Bartels of the exact penalty method of Coleman and Conn for nonlinearly constrained optimization problems. The structured adaptation also makes use of the ideas of N...
متن کاملA new double trust regions SQP method without a penalty function or a filter∗
A new trust-region SQP method for equality constrained optimization is considered. This method avoids using a penalty function or a filter, and yet can be globally convergent to first-order critical points under some reasonable assumptions. Each SQP step is composed of a normal step and a tangential step for which different trust regions are applied in the spirit of Gould and Toint [Math. Progr...
متن کاملA Superlinearly feasible SQP algorithm for Constrained Optimization
This paper is concerned with a Superlinearly feasible SQP algorithm algorithm for general constrained optimization. As compared with the existing SQP methods, it is necessary to solve equality constrained quadratic programming sub-problems at each iteration, which shows that the computational effort of the proposed algorithm is reduced further. Furthermore, under some mild assumptions, the algo...
متن کاملA Second-derivative Trust-region Sqp Method with a “trust-region-free” Predictor Step
In (NAR 08/18 and 08/21, Oxford University Computing Laboratory, 2008) we introduced a second-derivative SQP method (S2QP) for solving nonlinear nonconvex optimization problems. We proved that the method is globally convergent and locally superlinearly convergent under standard assumptions. A critical component of the algorithm is the so-called predictor step, which is computed from a strictly ...
متن کاملA Robust, Globally and Superlinearly Convergent QP-free Algorithm for Nonlinear Constrained Optimization
A new, robust QP-free algorithm for nonlinear constrained optimization problems which requires only the solution of two linear systems at each iteration is proposed. Based on a continuously differentiable merit function, the algorithm is actually a combination of a quasi-Newton method for the equality constrained optimization and an active-set strategy, and is an infeasible method. Under mild a...
متن کاملذخیره در منابع من
با ذخیره ی این منبع در منابع من، دسترسی به آن را برای استفاده های بعدی آسان تر کنید
عنوان ژورنال:
دوره شماره
صفحات -
تاریخ انتشار 2007