1,350 research outputs found
Lagrangian Relaxation and Partial Cover
Lagrangian relaxation has been used extensively in the design of
approximation algorithms. This paper studies its strengths and limitations when
applied to Partial Cover.Comment: 20 pages, extended abstract appeared in STACS 200
Greedy Algorithms for Steiner Forest
In the Steiner Forest problem, we are given terminal pairs ,
and need to find the cheapest subgraph which connects each of the terminal
pairs together. In 1991, Agrawal, Klein, and Ravi, and Goemans and Williamson
gave primal-dual constant-factor approximation algorithms for this problem;
until now, the only constant-factor approximations we know are via linear
programming relaxations.
We consider the following greedy algorithm: Given terminal pairs in a metric
space, call a terminal "active" if its distance to its partner is non-zero.
Pick the two closest active terminals (say ), set the distance
between them to zero, and buy a path connecting them. Recompute the metric, and
repeat. Our main result is that this algorithm is a constant-factor
approximation.
We also use this algorithm to give new, simpler constructions of cost-sharing
schemes for Steiner forest. In particular, the first "group-strict" cost-shares
for this problem implies a very simple combinatorial sampling-based algorithm
for stochastic Steiner forest
Linear Optimal Power Flow Using Cycle Flows
Linear optimal power flow (LOPF) algorithms use a linearization of the
alternating current (AC) load flow equations to optimize generator dispatch in
a network subject to the loading constraints of the network branches. Common
algorithms use the voltage angles at the buses as optimization variables, but
alternatives can be computationally advantageous. In this article we provide a
review of existing methods and describe a new formulation that expresses the
loading constraints directly in terms of the flows themselves, using a
decomposition of the network graph into a spanning tree and closed cycles. We
provide a comprehensive study of the computational performance of the various
formulations, in settings that include computationally challenging applications
such as multi-period LOPF with storage dispatch and generation capacity
expansion. We show that the new formulation of the LOPF solves up to 7 times
faster than the angle formulation using a commercial linear programming solver,
while another existing cycle-based formulation solves up to 20 times faster,
with an average speed-up of factor 3 for the standard networks considered here.
If generation capacities are also optimized, the average speed-up rises to a
factor of 12, reaching up to factor 213 in a particular instance. The speed-up
is largest for networks with many buses and decentral generators throughout the
network, which is highly relevant given the rise of distributed renewable
generation and the computational challenge of operation and planning in such
networks.Comment: 11 pages, 5 figures; version 2 includes results for generation
capacity optimization; version 3 is the final accepted journal versio
Lagrangian Relaxation and Partial Cover (Extended Abstract)
Lagrangian relaxation has been used extensively in the design of
approximation algorithms. This paper studies its strengths and
limitations when applied to Partial Cover.
We show that for Partial Cover in general no algorithm that uses
Lagrangian relaxation and a Lagrangian Multiplier Preserving (LMP)
-approximation as a black box can yield an approximation
factor better than~. This matches the upper bound
given by K"onemann et al. (ESA 2006, pages
468--479).
Faced with this limitation we study a specific, yet broad class of
covering problems: Partial Totally Balanced Cover. By carefully
analyzing the inner workings of the LMP algorithm we are able to
give an almost tight characterization of the integrality gap of the
standard linear relaxation of the problem. As a consequence we
obtain improved approximations for the Partial version of Multicut
and Path Hitting on Trees, Rectangle Stabbing, and Set Cover with
-Blocks
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