46 research outputs found
Tight Analysis of a Multiple-Swap Heuristic for Budgeted Red-Blue Median
Budgeted Red-Blue Median is a generalization of classic -Median in that
there are two sets of facilities, say and , that can
be used to serve clients located in some metric space. The goal is to open
facilities in and facilities in for
some given bounds and connect each client to their nearest open
facility in a way that minimizes the total connection cost.
We extend work by Hajiaghayi, Khandekar, and Kortsarz [2012] and show that a
multiple-swap local search heuristic can be used to obtain a
-approximation for Budgeted Red-Blue Median for any constant
. This is an improvement over their single swap analysis and
beats the previous best approximation guarantee of 8 by Swamy [2014].
We also present a matching lower bound showing that for every ,
there are instances of Budgeted Red-Blue Median with local optimum solutions
for the -swap heuristic whose cost is
times the optimum solution cost. Thus, our analysis is tight up to the lower
order terms. In particular, for any we show the single-swap
heuristic admits local optima whose cost can be as bad as times
the optimum solution cost
Asymmetric Traveling Salesman Path and Directed Latency Problems
We study integrality gaps and approximability of two closely related problems
on directed graphs. Given a set V of n nodes in an underlying asymmetric metric
and two specified nodes s and t, both problems ask to find an s-t path visiting
all other nodes. In the asymmetric traveling salesman path problem (ATSPP), the
objective is to minimize the total cost of this path. In the directed latency
problem, the objective is to minimize the sum of distances on this path from s
to each node. Both of these problems are NP-hard. The best known approximation
algorithms for ATSPP had ratio O(log n) until the very recent result that
improves it to O(log n/ log log n). However, only a bound of O(sqrt(n)) for the
integrality gap of its linear programming relaxation has been known. For
directed latency, the best previously known approximation algorithm has a
guarantee of O(n^(1/2+eps)), for any constant eps > 0. We present a new
algorithm for the ATSPP problem that has an approximation ratio of O(log n),
but whose analysis also bounds the integrality gap of the standard LP
relaxation of ATSPP by the same factor. This solves an open problem posed by
Chekuri and Pal [2007]. We then pursue a deeper study of this linear program
and its variations, which leads to an algorithm for the k-person ATSPP (where k
s-t paths of minimum total length are sought) and an O(log n)-approximation for
the directed latency problem
A Constant-Factor Approximation for Directed Latency in Quasi-Polynomial Time
We give the first constant-factor approximation for the Directed Latency
problem in quasi-polynomial time. Here, the goal is to visit all nodes in an
asymmetric metric with a single vehicle starting at a depot to minimize the
average time a node waits to be visited by the vehicle. The approximation
guarantee is an improvement over the polynomial-time -approximation
[Friggstad, Salavatipour, Svitkina, 2013] and no better quasi-polynomial time
approximation algorithm was known.
To obtain this, we must extend a recent result showing the integrality gap of
the Asymmetric TSP-Path LP relaxation is bounded by a constant [K\"{o}hne,
Traub, and Vygen, 2019], which itself builds on the breakthrough result that
the integrality gap for standard Asymmetric TSP is also a constant [Svensson,
Tarnawsi, and Vegh, 2018]. We show the standard Asymmetric TSP-Path integrality
gap is bounded by a constant even if the cut requirements of the LP relaxation
are relaxed from to
for some constant . We also give a better approximation
guarantee in the special case of Directed Latency in regret metrics where the
goal is to find a path minimize the average time a node waits in excess
of , i.e.
An -Approximation for Directed Steiner Tree in Planar Graphs
We present an -approximation for both the edge-weighted and
node-weighted versions of \DST in planar graphs where is the number of
terminals. We extend our approach to \MDST (in general graphs \MDST and \DST
are easily seen to be equivalent but in planar graphs this is not the case
necessarily) in which we get an -approximation for planar graphs
for where is the number of roots