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Numerical Solution of Implicitly Constrained Optimization Problems

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Matthias Heinkenschloss

Department of Computational and Applied Mathematics

Rice University

CAAM Technical Report TR08-05 (rev. June 2012, Jan 2013)

### Abstract

Many applications require the minimization of a smooth function
*f: R*^{n} → R whose evaluation requires
the solution of a system of nonlinear equations. This system represents
a numerical simulation that must be run to evaluate *f*.
We refer to this system of nonlinear equations as an implicit constraint.

In theory *f* can be minimized using the steepest descent
method or Newton-type methods for unconstrained minimization.
However, for the practical application of derivative based methods for the
minimization of *f* one has to deal with many interesting
issues that arise out of the presence of the system of nonlinear equations
that must be solved to evaluate *f*.

This article studies some of these issues, ranging from sensitivity and
adjoint techniques for derivative computation to implementation issues in
Newton-type methods. A discretized optimal control problem governed by the
unsteady Burgers equation is used to illustrate the ideas.

The material in this article is accessible to anyone with knowledge of Newton-type
methods for finite dimensional unconstrained optimization.
However, many of the concepts discussed in this article extend to and are
used in areas such as optimal control and PDE constrained optimization.
** Keywords.** Unconstrained minimization, implicit constraints, adjoints, sensitivities,
Newton method, nonlinear programming, optimal control, Burgers equation.

PDF

Matlab codes