A Globally Convergent Lcl Method for Nonlinear Optimization

@inproceedings{FriedlanderAGC,
  title={A Globally Convergent Lcl Method for Nonlinear Optimization},
  author={Michael P. Friedlander and Michael A. Saunders}
}
For optimization problems with nonlinear constraints, linearly constrained Lagrangian (LCL) methods sequentially minimize a Lagrangian function subject to linearized constraints. These methods converge rapidly near a solution but may not be reliable from arbitrary starting points. The well known example MINOS has proven effective on many large problems. Its success motivates us to propose a globally convergent variant. Our stabilized LCL method possesses two important properties: the… CONTINUE READING