## Structured Semidefinite Programs and Semialgebraic Geometry Methods in Robustness and Optimization

### Pablo A. Parrilo

#### PhD Dissertation, California Institute of Technology, May 2000

In the first part of this thesis, we introduce a specific class of
Linear Matrix Inequalities (LMI) whose optimal solution can be
characterized exactly. This family corresponds to the case where the
associated linear operator maps the cone of positive semidefinite
matrices onto itself. In this case, the optimal value equals the
spectral radius of the operator. It is shown that some rank
minimization problems, as well as generalizations of the structured
singular value ($\mu$) LMIs, have exactly this
property.

In the same spirit of exploiting structure to achieve computational
efficiency, an algorithm for the numerical solution of a special class
of frequency-dependent LMIs is presented. These optimization problems
arise from robustness analysis questions, via the
Kalman-Yakubovich-Popov lemma. The procedure is an outer
approximation method based on the algorithms used in the computation
of \hinf\ norms for linear, time invariant systems. The result is
especially useful for systems with large state dimension.

The other main contribution in this thesis is the formulation of a
convex optimization framework for semialgebraic problems, i.e., those
that can be expressed by polynomial equalities and inequalities. The
key element is the interaction of concepts in real algebraic geometry
(Positivstellensatz) and semidefinite programming.

To this end, an LMI formulation for the sums of squares decomposition
for multivariable polynomials is presented. Based on this, it is shown
how to construct sufficient Positivstellensatz-based convex tests to
prove that certain sets are empty. Among other applications, this
leads to a nonlinear extension of many LMI based results in uncertain
linear system analysis.

Within the same framework, we develop stronger criteria for matrix
copositivity, and generalizations of the well-known standard
semidefinite relaxations for quadratic programming.

Some applications to new and previously studied problems are
presented. A few examples are Lyapunov function computation, robust
bifurcation analysis, structured singular values, etc. It is shown
that the proposed methods allow for improved solutions for very
diverse questions in continuous and combinatorial optimization.