23 research outputs found
Sparse Sums of Positive Semidefinite Matrices
Recently there has been much interest in "sparsifying" sums of rank one
matrices: modifying the coefficients such that only a few are nonzero, while
approximately preserving the matrix that results from the sum. Results of this
sort have found applications in many different areas, including sparsifying
graphs. In this paper we consider the more general problem of sparsifying sums
of positive semidefinite matrices that have arbitrary rank.
We give several algorithms for solving this problem. The first algorithm is
based on the method of Batson, Spielman and Srivastava (2009). The second
algorithm is based on the matrix multiplicative weights update method of Arora
and Kale (2007). We also highlight an interesting connection between these two
algorithms.
Our algorithms have numerous applications. We show how they can be used to
construct graph sparsifiers with auxiliary constraints, sparsifiers of
hypergraphs, and sparse solutions to semidefinite programs
Sketching Cuts in Graphs and Hypergraphs
Sketching and streaming algorithms are in the forefront of current research
directions for cut problems in graphs. In the streaming model, we show that
-approximation for Max-Cut must use space;
moreover, beating -approximation requires polynomial space. For the
sketching model, we show that -uniform hypergraphs admit a
-cut-sparsifier (i.e., a weighted subhypergraph that
approximately preserves all the cuts) with
edges. We also make first steps towards sketching general CSPs (Constraint
Satisfaction Problems)