22 research outputs found
Discrete Midpoint Convexity
For a function defined on a convex set in a Euclidean space, midpoint
convexity is the property requiring that the value of the function at the
midpoint of any line segment is not greater than the average of its values at
the endpoints of the line segment. Midpoint convexity is a well-known
characterization of ordinary convexity under very mild assumptions. For a
function defined on the integer lattice, we consider the analogous notion of
discrete midpoint convexity, a discrete version of midpoint convexity where the
value of the function at the (possibly noninteger) midpoint is replaced by the
average of the function values at the integer round-up and round-down of the
midpoint. It is known that discrete midpoint convexity on all line segments
with integer endpoints characterizes L-convexity, and that it
characterizes submodularity if we restrict the endpoints of the line segments
to be at -distance one. By considering discrete midpoint convexity
for all pairs at -distance equal to two or not smaller than two,
we identify new classes of discrete convex functions, called local and global
discrete midpoint convex functions, which are strictly between the classes of
L-convex and integrally convex functions, and are shown to be
stable under scaling and addition. Furthermore, a proximity theorem, with the
same small proximity bound as that for L-convex functions, is
established for discrete midpoint convex functions. Relevant examples of
classes of local and global discrete midpoint convex functions are provided.Comment: 39 pages, 6 figures, to appear in Mathematics of Operations Researc
Discrete Convex Functions on Graphs and Their Algorithmic Applications
The present article is an exposition of a theory of discrete convex functions
on certain graph structures, developed by the author in recent years. This
theory is a spin-off of discrete convex analysis by Murota, and is motivated by
combinatorial dualities in multiflow problems and the complexity classification
of facility location problems on graphs. We outline the theory and algorithmic
applications in combinatorial optimization problems
Scaling and Proximity Properties of Integrally Convex Functions
In discrete convex analysis, the scaling and proximity properties for the class of L^natural-convex functions were established more than a decade ago and have been used to design efficient minimization algorithms. For the larger class of integrally convex functions of n variables, we show here that the scaling property only holds when n leq 2, while a proximity theorem can be established for any n, but only with an exponential bound. This is, however, sufficient to extend the classical logarithmic complexity result for minimizing a discretely convex function in one dimension to the case of integrally convex functions in two dimensions. Furthermore, we identified a new class of discrete convex functions, called directed integrally convex functions, which is strictly between the classes of L^natural -convex and integrally convex functions but enjoys the same scaling and proximity properties that hold for L^natural -convex functions