Magnus WahlstrÃ¶m
Royal Holloway
Half-integrality, LP-branching and FPT Algorithms
By a classical result in combinatorial optimization, Vertex Cover has a
half-integral LP-relaxation (i.e., the natural LP-relaxation of the Vertex
Cover problem always has an optimal solution where all variables take
values 0, 1 or 1/2, implying a 2-approximation); a similar statement holds
for Node Multiway Cut. Recently, it has been realised that beyond
approximation, these results also have powerful applications for FPT
algorithms, e.g., we can check in 2^O(k) poly(n) time whether there is a
solution that is at most k units more costly than the LP-optimum. This has
been used to improve the FPT running time for several problems (e.g.,
Almost 2-SAT and Multiway Cut). Still, the set of problems with known
half-integral LP-relaxations was somewhat restricted.
In a recent paper (SODA 2014), we showed how to interpret the existence of
such a relaxation in the language of CSPs. Using this connection, we
showed how many of the known half-integral relaxations can be explained as
optimisation over a function class known as k-submodular functions, and we
used this function class to produce new half-integral LP-relaxations and
strongly improved FPT running times for a range of problems.
In the present talk we give a brief overview of this connection, and a
sketch of its applications in FPT. (Interestingly, the resulting
polytopes, despite being half-integral, do not imply any approximation
results.)