13,378 research outputs found
Stable gonality is computable
Stable gonality is a multigraph parameter that measures the complexity of a
graph. It is defined using maps to trees. Those maps, in some sense, divide the
edges equally over the edges of the tree; stable gonality asks for the map with
the minimum number of edges mapped to each edge of the tree. This parameter is
related to treewidth, but unlike treewidth, it distinguishes multigraphs from
their underlying simple graphs. Stable gonality is relevant for problems in
number theory. In this paper, we show that deciding whether the stable gonality
of a given graph is at most a given integer belongs to the class NP, and we
give an algorithm that computes the stable gonality of a graph in
time.Comment: 15 pages; v2 minor changes; v3 minor change
Spatial Interpolants
We propose Splinter, a new technique for proving properties of
heap-manipulating programs that marries (1) a new separation logic-based
analysis for heap reasoning with (2) an interpolation-based technique for
refining heap-shape invariants with data invariants. Splinter is property
directed, precise, and produces counterexample traces when a property does not
hold. Using the novel notion of spatial interpolants modulo theories, Splinter
can infer complex invariants over general recursive predicates, e.g., of the
form all elements in a linked list are even or a binary tree is sorted.
Furthermore, we treat interpolation as a black box, which gives us the freedom
to encode data manipulation in any suitable theory for a given program (e.g.,
bit vectors, arrays, or linear arithmetic), so that our technique immediately
benefits from any future advances in SMT solving and interpolation.Comment: Short version published in ESOP 201
Network Kriging
Network service providers and customers are often concerned with aggregate
performance measures that span multiple network paths. Unfortunately, forming
such network-wide measures can be difficult, due to the issues of scale
involved. In particular, the number of paths grows too rapidly with the number
of endpoints to make exhaustive measurement practical. As a result, it is of
interest to explore the feasibility of methods that dramatically reduce the
number of paths measured in such situations while maintaining acceptable
accuracy.
We cast the problem as one of statistical prediction--in the spirit of the
so-called `kriging' problem in spatial statistics--and show that end-to-end
network properties may be accurately predicted in many cases using a
surprisingly small set of carefully chosen paths. More precisely, we formulate
a general framework for the prediction problem, propose a class of linear
predictors for standard quantities of interest (e.g., averages, totals,
differences) and show that linear algebraic methods of subset selection may be
used to effectively choose which paths to measure. We characterize the
performance of the resulting methods, both analytically and numerically. The
success of our methods derives from the low effective rank of routing matrices
as encountered in practice, which appears to be a new observation in its own
right with potentially broad implications on network measurement generally.Comment: 16 pages, 9 figures, single-space
Recognizing hyperelliptic graphs in polynomial time
Recently, a new set of multigraph parameters was defined, called
"gonalities". Gonality bears some similarity to treewidth, and is a relevant
graph parameter for problems in number theory and multigraph algorithms.
Multigraphs of gonality 1 are trees. We consider so-called "hyperelliptic
graphs" (multigraphs of gonality 2) and provide a safe and complete sets of
reduction rules for such multigraphs, showing that for three of the flavors of
gonality, we can recognize hyperelliptic graphs in O(n log n+m) time, where n
is the number of vertices and m the number of edges of the multigraph.Comment: 33 pages, 8 figure
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