4 research outputs found
Tree Projections and Constraint Optimization Problems: Fixed-Parameter Tractability and Parallel Algorithms
Tree projections provide a unifying framework to deal with most structural
decomposition methods of constraint satisfaction problems (CSPs). Within this
framework, a CSP instance is decomposed into a number of sub-problems, called
views, whose solutions are either already available or can be computed
efficiently. The goal is to arrange portions of these views in a tree-like
structure, called tree projection, which determines an efficiently solvable CSP
instance equivalent to the original one. Deciding whether a tree projection
exists is NP-hard. Solution methods have therefore been proposed in the
literature that do not require a tree projection to be given, and that either
correctly decide whether the given CSP instance is satisfiable, or return that
a tree projection actually does not exist. These approaches had not been
generalized so far on CSP extensions for optimization problems, where the goal
is to compute a solution of maximum value/minimum cost. The paper fills the
gap, by exhibiting a fixed-parameter polynomial-time algorithm that either
disproves the existence of tree projections or computes an optimal solution,
with the parameter being the size of the expression of the objective function
to be optimized over all possible solutions (and not the size of the whole
constraint formula, used in related works). Tractability results are also
established for the problem of returning the best K solutions. Finally,
parallel algorithms for such optimization problems are proposed and analyzed.
Given that the classes of acyclic hypergraphs, hypergraphs of bounded
treewidth, and hypergraphs of bounded generalized hypertree width are all
covered as special cases of the tree projection framework, the results in this
paper directly apply to these classes. These classes are extensively considered
in the CSP setting, as well as in conjunctive database query evaluation and
optimization
Optimal Algorithms for Ranked Enumeration of Answers to Full Conjunctive Queries
We study ranked enumeration of join-query results according to very general
orders defined by selective dioids. Our main contribution is a framework for
ranked enumeration over a class of dynamic programming problems that
generalizes seemingly different problems that had been studied in isolation. To
this end, we extend classic algorithms that find the k-shortest paths in a
weighted graph. For full conjunctive queries, including cyclic ones, our
approach is optimal in terms of the time to return the top result and the delay
between results. These optimality properties are derived for the widely used
notion of data complexity, which treats query size as a constant. By performing
a careful cost analysis, we are able to uncover a previously unknown tradeoff
between two incomparable enumeration approaches: one has lower complexity when
the number of returned results is small, the other when the number is very
large. We theoretically and empirically demonstrate the superiority of our
techniques over batch algorithms, which produce the full result and then sort
it. Our technique is not only faster for returning the first few results, but
on some inputs beats the batch algorithm even when all results are produced.Comment: 50 pages, 19 figure