A Fast Algorithm for Sparse Controller Design

We consider the task of designing sparse control laws for large-scale systems by directly minimizing an infinite horizon quadratic cost with an $\ell_1$ penalty on the feedback controller gains. Our focus is on an improved algorithm that allows us to scale to large systems (i.e. those where sparsity is most useful) with convergence times that are several orders of magnitude faster than existing algorithms. In particular, we develop an efficient proximal Newton method which minimizes per-iteration cost with a coordinate descent active set approach and fast numerical solutions to the Lyapunov equations. Experimentally we demonstrate the appeal of this approach on synthetic examples and real power networks significantly larger than those previously considered in the literature

Symmetry in Finite Combinatorial Objects: Scalable Methods and Applications.

Symmetries of combinatorial objects are known to complicate search algorithms, but such obstacles can often be removed by detecting symmetries early and discarding symmetric subproblems. Canonical labeling of combinatorial objects facilitates easy equivalence checking through quick matching. All existing canonical-labeling software also finds symmetries, but the fastest symmetry-finding software does not perform canonical labeling. In this thesis, we describe highly scalable symmetry-detection algorithms for two widely-used combinatorial objects: graphs and Boolean functions. Our algorithms are based on a decision tree that combines elements of group-theoretic computation with branching and backtracking search. Moreover, we contrast the search for graph symmetries and a canonical labeling to dissect typical algorithms and identify their similarities and differences. We develop a novel approach to graph canonical labeling where symmetries are found first and then used to speed up the canonical-labeling routines. Empirical results are given for graphs with millions of vertices and Boolean functions with hundreds of I/Os, where our algorithms can often find all symmetry group generators or a canonical labeling in seconds.PHDComputer Science & EngineeringUniversity of Michigan, Horace H. Rackham School of Graduate Studieshttp://deepblue.lib.umich.edu/bitstream/2027.42/100003/1/hadik_1.pd