A novel augmented Lagrangian method of multipliers for optimization with general inequality constraints

نویسندگان

چکیده

We introduce a twice differentiable augmented Lagrangian for nonlinear optimization with general inequality constraints and show that strict local minimizer of the original problem is an approximate solution Lagrangian. A novel method multipliers (ALM) then presented. Our originated from generalization Hestenes-Powell Lagrangian, combination interior-point technique. It shares similar algorithmic framework existing ALMs constraints, but it can use second derivatives does not depend on projections set constraints. In each iteration, our solves continuously unconstrained subproblem primal variables. The dual iterates, penalty smoothing parameters are updated adaptively. global convergence analyzed. Without assuming any constraint qualification, proved proposed has strong convergence. may converge to either Karush-Kuhn-Tucker (KKT) point or singular stationary when converging minimizer. also infeasible program infeasible. Furthermore, capable rapidly detecting possible infeasibility solved problem. Under suitable conditions, locally linearly convergent KKT point, which consistent equality preliminary numerical experiments some small benchmark test problems demonstrate theoretical results.

برای دانلود باید عضویت طلایی داشته باشید

برای دانلود متن کامل این مقاله و بیش از 32 میلیون مقاله دیگر ابتدا ثبت نام کنید

اگر عضو سایت هستید لطفا وارد حساب کاربری خود شوید

منابع مشابه

Partially Augmented Lagrangian Method for Matrix Inequality Constraints

We discuss a partially augmented Lagrangian method for optimization programs with matrix inequality constraints. A global convergence result is obtained. Applications to hard problems in feedback control are presented to validate the method numerically.

متن کامل

A Globally Convergent Augmented Lagrangian Pattern Search Algorithm for Optimization with General Constraints and Simple Bounds

We give a pattern search method for nonlinearly constrained optimization that is an adaption of a bound constrained augmented Lagrangian method first proposed by Conn, Gould, and Toint [SIAM J. Numer. Anal., 28 (1991), pp. 545–572]. In the pattern search adaptation, we solve the bound constrained subproblem approximately using a pattern search method. The stopping criterion proposed by Conn, Go...

متن کامل

A globally convergent Lagrangian barrier algorithm for optimization with general inequality constraints and simple bounds

We consider the global and local convergence properties of a class of Lagrangian barrier methods for solving nonlinear programming problems. In such methods, simple bound constraints may be treated separately from more general constraints. The objective and general constraint functions are combined in a Lagrangian barrier function. A sequence of such functions are approximately minimized within...

متن کامل

A Globally Convergent Lagrangian Barrier Algorithm for Optimization with General Inequality Constraints

We consider the global and local convergence properties of a class of Lagrangian barrier methods for solving nonlinear programming problems. In such methods, simple bound constraints may be treated separately from more general constraints. The objective and general constraint functions are combined in a Lagrangian barrier function. A sequence of Lagrangian barrier functions are approximately mi...

متن کامل

ذخیره در منابع من


  با ذخیره ی این منبع در منابع من، دسترسی به آن را برای استفاده های بعدی آسان تر کنید

ژورنال

عنوان ژورنال: Mathematics of Computation

سال: 2022

ISSN: ['1088-6842', '0025-5718']

DOI: https://doi.org/10.1090/mcom/3799