Connect Four and Graph Decomposition

We introduce the standard decomposition, a way of decomposing a labeled graph into a sum of certain labeled subgraphs. We motivate this graph-theoretic concept by relating it to Connect Four decompositions of standard sets. We prove that all standard decompositions can be generated in polynomial time, which implies that all Connect Four decompositions can be generated in polynomial time

On the Enumeration of all Minimal Triangulations

We present an algorithm that enumerates all the minimal triangulations of a graph in incremental polynomial time. Consequently, we get an algorithm for enumerating all the proper tree decompositions, in incremental polynomial time, where "proper" means that the tree decomposition cannot be improved by removing or splitting a bag

Hypergraph Acyclicity and Propositional Model Counting

We show that the propositional model counting problem #SAT for CNF- formulas with hypergraphs that allow a disjoint branches decomposition can be solved in polynomial time. We show that this class of hypergraphs is incomparable to hypergraphs of bounded incidence cliquewidth which were the biggest class of hypergraphs for which #SAT was known to be solvable in polynomial time so far. Furthermore, we present a polynomial time algorithm that computes a disjoint branches decomposition of a given hypergraph if it exists and rejects otherwise. Finally, we show that some slight extensions of the class of hypergraphs with disjoint branches decompositions lead to intractable #SAT, leaving open how to generalize the counting result of this paper

On Difference-of-SOS and Difference-of-Convex-SOS Decompositions for Polynomials

In this paper, we are interested in developing polynomial decomposition techniques to reformulate real valued multivariate polynomials into difference-of-sums-of-squares (namely, D-SOS) and difference-of-convex-sums-of-squares (namely, DC-SOS). Firstly, we prove that the set of D-SOS and DC-SOS polynomials are vector spaces and equivalent to the set of real valued polynomials. Moreover, the problem of finding D-SOS and DC-SOS decompositions are equivalent to semidefinite programs (SDP) which can be solved to any desired precision in polynomial time. Some important algebraic properties and the relationships among the set of sums-of-squares (SOS) polynomials, positive semidefinite (PSD) polynomials, convex-sums-of-squares (CSOS) polynomials, SOS-convex polynomials, D-SOS and DC-SOS polynomials are discussed. Secondly, we focus on establishing several practical algorithms for constructing D-SOS and DC-SOS decompositions for any polynomial without solving SDP. Using DC-SOS decomposition, we can reformulate polynomial optimization problems in the realm of difference-of-convex (DC) programming, which can be handled by efficient DC programming approaches. Some examples illustrate how to use our methods for constructing D-SOS and DC-SOS decompositions. Numerical performance of D-SOS and DC-SOS decomposition algorithms and their parallelized methods are tested on a synthetic dataset with 1750 randomly generated large and small sized sparse and dense polynomials. Some real-world applications in higher order moment portfolio optimization problems, eigenvalue complementarity problems, Euclidean distance matrix completion problems, and Boolean polynomial programs are also presented.Comment: 47 pages, 19 figure

Polynomial subspace decomposition for broadband angle of arrival estimation

In this paper we study the impact of polynomial or broadband subspace decompositions on any subsequent processing, which here uses the example of a broadband angle of arrival estimation technique using a recently proposed polynomial MUSIC (P-MUSIC) algorithm. The subspace decompositions are performed by iterative polynomial EVDs, which differ in their approximations to diagonalise and spectrally majorise s apce-time covariance matrix.We here show that a better diagonalisation has a significant impact on the accuracy of defining broadband signal and noise subspaces, demonstrated by a much higher accuracy of the P-MUSIC spectrum