Ph.D Student | Pan Dror |
---|---|

Subject | Optimization Methods for Solving Structured Nonconvex Minimization Problems |

Department | Department of Industrial Engineering and Management |

Supervisors | Assistant Professor Shoham Sabach |

Professor Amir Beck | |

Full Thesis text |

We consider two approaches towards solving hard optimization problems (nonconvex, and in some cases nonsmooth) that have special structures.

In the first approach, we seek to compute a global
optimal solution for such a problem in reasonable run time. We develop a *branch
and bound* algorithm that globally solves special continuous optimization
problems where a potentially nonconvex objective function is to be minimized
under nonconvex inequality constraints that satisfy some specific solvability assumptions.
The assumptions hold for some special *quadratically constrained quadratic programming*
that contain only ball, out-of-ball and linear constraints, and a nonconvex quadratic
objective to minimize. Such problems can be seen as extensions of the standard *trust
region subproblem*, and the proposed algorithm utilizes the ability to efficiently
compute all the local and global minimizers of that problem. We give numerical
experiments showing that for moderate dimensions, the algorithm can stop and
verify optimality in a reasonable time. Application of the algorithm to *sparse
source localization* problems is presented.

In the second approach, we apply an iterative scheme
called *majorization-minimization* on some special nonconvex and nonsmooth
optimization problems. The method is based on minimizing at each iterate a
properly constructed *consistent majorizer* of the objective function. We
describe a variety of classes of functions for which such a construction is
possible. We introduce an inexact variant of the method, in which only
approximate minimization of the consistent majorizer is performed at each
iteration. Both the exact and the inexact algorithms are shown to be descent
methods whose accumulation points have a property that is stronger than
standard stationarity. We give examples of cases in which the exact method can
be applied. Finally, we show that the inexact method can be applied to the so-called
sparse source localization problem. Such a problem is a special setting of a
more general model of composition of a support function over a smooth map.
Under some additional assumptions on the majorizers used, the inexact algorithm
is applicable by utilizing a fast optimization method on a smooth convex dual
of its subproblems.