Implementing a smooth exact penalty function for general constrained nonlinear optimization

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Abstract

We build upon Estrin et al. (2019) to develop a general constrained nonlinear optimization algorithm based on a smooth penalty function proposed by Fletcher (1970, 1973b). Although Fletcher’s approach has historically been considered impractical, we show that the computational kernels required are no more expensive than those in other widely accepted methods for nonlinear optimization. The main kernel for evaluating the penalty function and its derivatives solves structured linear systems. When the matrices are available explicitly, we store a single factorization each iteration. Otherwise, we obtain a factorization-free optimization algorithm by solving each linear system iteratively. The penalty function shows promise in cases where the linear systems can be solved efficiently, e.g., PDE-constrained optimization problems when efficient preconditioners exist. We demonstrate the merits of the approach, and give numerical results on several PDE-constrained and standard test problems.

Companion paper

This is the second paper of a two-part series that develops a smooth penalty function for constrained optimization. The first paper develops the method for nonlinear equality constraints.

BiBTeX

@article{EstrinFriedlanderOrbanSaunders:2020a,
  author  =  {R. Estrin and M. P. Friedlander and D. Orban and M. A. Saunders},
  title   =  {Implementing a smooth exact penalty function for general constrained nonlinear optimization},
  journal =  {SIAM J. Sci. Comput.},
  volume  =  {42},
  number  =  {3},
  pages   =  {A1836–A1859},
  year    =  {2020},
  DOI     =  {10.1137/19M1255069}
}