294 research outputs found
The Moser-Tardos Framework with Partial Resampling
The resampling algorithm of Moser \& Tardos is a powerful approach to develop
constructive versions of the Lov\'{a}sz Local Lemma (LLL). We generalize this
to partial resampling: when a bad event holds, we resample an
appropriately-random subset of the variables that define this event, rather
than the entire set as in Moser & Tardos. This is particularly useful when the
bad events are determined by sums of random variables. This leads to several
improved algorithmic applications in scheduling, graph transversals, packet
routing etc. For instance, we settle a conjecture of Szab\'{o} & Tardos (2006)
on graph transversals asymptotically, and obtain improved approximation ratios
for a packet routing problem of Leighton, Maggs, & Rao (1994)
Improved bounds and algorithms for graph cuts and network reliability
Karger (SIAM Journal on Computing, 1999) developed the first fully-polynomial
approximation scheme to estimate the probability that a graph becomes
disconnected, given that its edges are removed independently with probability
. This algorithm runs in time to obtain an
estimate within relative error .
We improve this run-time through algorithmic and graph-theoretic advances.
First, there is a certain key sub-problem encountered by Karger, for which a
generic estimation procedure is employed, we show that this has a special
structure for which a much more efficient algorithm can be used. Second, we
show better bounds on the number of edge cuts which are likely to fail. Here,
Karger's analysis uses a variety of bounds for various graph parameters, we
show that these bounds cannot be simultaneously tight. We describe a new graph
parameter, which simultaneously influences all the bounds used by Karger, and
obtain much tighter estimates of the cut structure of . These techniques
allow us to improve the runtime to , our results also
rigorously prove certain experimental observations of Karger & Tai (Proc.
ACM-SIAM Symposium on Discrete Algorithms, 1997). Our rigorous proofs are
motivated by certain non-rigorous differential-equation approximations which,
however, provably track the worst-case trajectories of the relevant parameters.
A key driver of Karger's approach (and other cut-related results) is a bound
on the number of small cuts: we improve these estimates when the min-cut size
is "small" and odd, augmenting, in part, a result of Bixby (Bulletin of the
AMS, 1974)
Algorithmic and enumerative aspects of the Moser-Tardos distribution
Moser & Tardos have developed a powerful algorithmic approach (henceforth
"MT") to the Lovasz Local Lemma (LLL); the basic operation done in MT and its
variants is a search for "bad" events in a current configuration. In the
initial stage of MT, the variables are set independently. We examine the
distributions on these variables which arise during intermediate stages of MT.
We show that these configurations have a more or less "random" form, building
further on the "MT-distribution" concept of Haeupler et al. in understanding
the (intermediate and) output distribution of MT. This has a variety of
algorithmic applications; the most important is that bad events can be found
relatively quickly, improving upon MT across the complexity spectrum: it makes
some polynomial-time algorithms sub-linear (e.g., for Latin transversals, which
are of basic combinatorial interest), gives lower-degree polynomial run-times
in some settings, transforms certain super-polynomial-time algorithms into
polynomial-time ones, and leads to Las Vegas algorithms for some coloring
problems for which only Monte Carlo algorithms were known.
We show that in certain conditions when the LLL condition is violated, a
variant of the MT algorithm can still produce a distribution which avoids most
of the bad events. We show in some cases this MT variant can run faster than
the original MT algorithm itself, and develop the first-known criterion for the
case of the asymmetric LLL. This can be used to find partial Latin transversals
-- improving upon earlier bounds of Stein (1975) -- among other applications.
We furthermore give applications in enumeration, showing that most applications
(where we aim for all or most of the bad events to be avoided) have many more
solutions than known before by proving that the MT-distribution has "large"
min-entropy and hence that its support-size is large
Partial resampling to approximate covering integer programs
We consider column-sparse covering integer programs, a generalization of set
cover, which have a long line of research of (randomized) approximation
algorithms. We develop a new rounding scheme based on the Partial Resampling
variant of the Lov\'{a}sz Local Lemma developed by Harris & Srinivasan (2019).
This achieves an approximation ratio of , where is the minimum covering
constraint and is the maximum -norm of any column of the
covering matrix (whose entries are scaled to lie in ). When there are
additional constraints on the variable sizes, we show an approximation ratio of
(where is the maximum number
of non-zero entries in any column of the covering matrix). These results
improve asymptotically, in several different ways, over results of Srinivasan
(2006) and Kolliopoulos & Young (2005).
We show nearly-matching inapproximability and integrality-gap lower bounds.
We also show that the rounding process leads to negative correlation among the
variables, which allows us to handle multi-criteria programs
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