A Globally Convergent Stabilized SQP Method
نویسندگان
چکیده
منابع مشابه
A Globally Convergent Stabilized SQP Method
Sequential quadratic programming (SQP) methods are a popular class of methods for nonlinearly constrained optimization. They are particularly effective for solving a sequence of related problems, such as those arising in mixed-integer nonlinear programming and the optimization of functions subject to differential equation constraints. Recently, there has been considerable interest in the formul...
متن کاملA Globally Convergent Stabilized Sqp Method: Superlinear Convergence
Regularized and stabilized sequential quadratic programming (SQP) methods are two classes of methods designed to resolve the numerical and theoretical difficulties associated with ill-posed or degenerate nonlinear optimization problems. Recently, a regularized SQP method has been proposed that allows convergence to points satisfying certain second-order KKT conditions (SIAM J. Optim., 23(4):198...
متن کاملA stabilized SQP method: superlinear convergence
Regularized and stabilized sequential quadratic programming (SQP) methods are two classes of methods designed to resolve the numerical and theoretical difficulties associated with ill-posed or degenerate nonlinear optimization problems. Recently, a stabilized SQP method has been proposed that allows convergence to points satisfying certain secondorder KKT conditions (Report CCoM 13-04, Center f...
متن کاملA 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...
متن کاملA globally convergent incremental Newton method
Motivated by machine learning problems over large data sets and distributed optimization over networks, we develop and analyze a new method called incremental Newton method for minimizing the sum of a large number of strongly convex functions. We show that our method is globally convergent for a variable stepsize rule. We further show that under a gradient growth condition, convergence rate is ...
متن کاملذخیره 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: SIAM Journal on Optimization
سال: 2013
ISSN: 1052-6234,1095-7189
DOI: 10.1137/120882913