19,941 research outputs found
From error bounds to the complexity of first-order descent methods for convex functions
This paper shows that error bounds can be used as effective tools for
deriving complexity results for first-order descent methods in convex
minimization. In a first stage, this objective led us to revisit the interplay
between error bounds and the Kurdyka-\L ojasiewicz (KL) inequality. One can
show the equivalence between the two concepts for convex functions having a
moderately flat profile near the set of minimizers (as those of functions with
H\"olderian growth). A counterexample shows that the equivalence is no longer
true for extremely flat functions. This fact reveals the relevance of an
approach based on KL inequality. In a second stage, we show how KL inequalities
can in turn be employed to compute new complexity bounds for a wealth of
descent methods for convex problems. Our approach is completely original and
makes use of a one-dimensional worst-case proximal sequence in the spirit of
the famous majorant method of Kantorovich. Our result applies to a very simple
abstract scheme that covers a wide class of descent methods. As a byproduct of
our study, we also provide new results for the globalization of KL inequalities
in the convex framework.
Our main results inaugurate a simple methodology: derive an error bound,
compute the desingularizing function whenever possible, identify essential
constants in the descent method and finally compute the complexity using the
one-dimensional worst case proximal sequence. Our method is illustrated through
projection methods for feasibility problems, and through the famous iterative
shrinkage thresholding algorithm (ISTA), for which we show that the complexity
bound is of the form where the constituents of the bound only depend
on error bound constants obtained for an arbitrary least squares objective with
regularization
Query Containment for Highly Expressive Datalog Fragments
The containment problem of Datalog queries is well known to be undecidable.
There are, however, several Datalog fragments for which containment is known to
be decidable, most notably monadic Datalog and several "regular" query
languages on graphs. Monadically Defined Queries (MQs) have been introduced
recently as a joint generalization of these query languages. In this paper, we
study a wide range of Datalog fragments with decidable query containment and
determine exact complexity results for this problem. We generalize MQs to
(Frontier-)Guarded Queries (GQs), and show that the containment problem is
3ExpTime-complete in either case, even if we allow arbitrary Datalog in the
sub-query. If we focus on graph query languages, i.e., fragments of linear
Datalog, then this complexity is reduced to 2ExpSpace. We also consider nested
queries, which gain further expressivity by using predicates that are defined
by inner queries. We show that nesting leads to an exponentially increasing
hierarchy for the complexity of query containment, both in the linear and in
the general case. Our results settle open problems for (nested) MQs, and they
paint a comprehensive picture of the state of the art in Datalog query
containment.Comment: 20 page
Best of Two Local Models: Local Centralized and Local Distributed Algorithms
We consider two models of computation: centralized local algorithms and local
distributed algorithms. Algorithms in one model are adapted to the other model
to obtain improved algorithms.
Distributed vertex coloring is employed to design improved centralized local
algorithms for: maximal independent set, maximal matching, and an approximation
scheme for maximum (weighted) matching over bounded degree graphs. The
improvement is threefold: the algorithms are deterministic, stateless, and the
number of probes grows polynomially in , where is the number of
vertices of the input graph.
The recursive centralized local improvement technique by Nguyen and
Onak~\cite{onak2008} is employed to obtain an improved distributed
approximation scheme for maximum (weighted) matching. The improvement is
twofold: we reduce the number of rounds from to for a
wide range of instances and, our algorithms are deterministic rather than
randomized
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