This book is about the Augmented Lagrangian method, a popular technique for solving
constrained optimization problems. It is mainly dedicated to engineers, chemists, physicists …

Augmented Lagrangian methods with general lower-level constraints are considered in the
present research. These methods are useful when efficient algorithms exist for solving …

Mathematical programs with equilibrium constraints (MPECs) are difficult optimization
problems whose feasible sets do not satisfy most of the standard constraint qualifications …

Sequential optimality conditions provide adequate theoretical tools to justify stopping criteria
for nonlinear programming solvers. Approximate Karush–Kuhn–Tucker and approximate …

Abstract Two Augmented Lagrangian algorithms for solving KKT systems are introduced.
The algorithms differ in the way in which penalty parameters are updated. Possibly …

In this work we introduce a relaxed version of the constant positive linear dependence
constraint qualification (CPLD) that we call RCPLD. This development is inspired by a recent …

Every local minimizer of a smooth constrained optimization problem satisfies the sequential
approximate Karush--Kuhn--Tucker (AKKT) condition. This optimality condition is used to …

Optimization methods that employ the classical Powell-Hestenes-Rockafellar augmented
Lagrangian are useful tools for solving nonlinear programming problems. Their reputation …

We propose a new method that employs transfer learning techniques to effectively correct
sampling selection errors introduced by sparse annotations during supervised learning for …

We present two new constraint qualifications (CQs) that are weaker than the recently
introduced relaxed constant positive linear dependence (RCPLD) CQ. RCPLD is based on …