106,543 research outputs found
Improved Online Algorithm for Weighted Flow Time
We discuss one of the most fundamental scheduling problem of processing jobs
on a single machine to minimize the weighted flow time (weighted response
time). Our main result is a -competitive algorithm, where is the
maximum-to-minimum processing time ratio, improving upon the
-competitive algorithm of Chekuri, Khanna and Zhu (STOC 2001). We
also design a -competitive algorithm, where is the
maximum-to-minimum density ratio of jobs. Finally, we show how to combine these
results with the result of Bansal and Dhamdhere (SODA 2003) to achieve a
-competitive algorithm (where is the
maximum-to-minimum weight ratio), without knowing in advance. As shown
by Bansal and Chan (SODA 2009), no constant-competitive algorithm is achievable
for this problem.Comment: 20 pages, 4 figure
Algorithms for Minimizing Weighted Flow Time
We study the problem of minimizing weighted flow time on a single machine in the preemptive setting. We present an O(log2 P)-competitive semi-online algorithm where P is the ratio of the maximum and minimum processing times of jobs in the system. In the offline setting we show that a (2 + ε)-approximation is achievable in quasi-polynomial time. These are the first non-trivial results for the weighted versions of minimizing flow time. For multiple machines we show that no competitive randomized online algorithm exists for weighted flow time. We also present an improved online algorithm for minimizing total stretch (a special case of weighted flow time) on multiple machines
The Geometry of Scheduling
We consider the following general scheduling problem: The input consists of n
jobs, each with an arbitrary release time, size, and a monotone function
specifying the cost incurred when the job is completed at a particular time.
The objective is to find a preemptive schedule of minimum aggregate cost. This
problem formulation is general enough to include many natural scheduling
objectives, such as weighted flow, weighted tardiness, and sum of flow squared.
Our main result is a randomized polynomial-time algorithm with an approximation
ratio O(log log nP), where P is the maximum job size. We also give an O(1)
approximation in the special case when all jobs have identical release times.
The main idea is to reduce this scheduling problem to a particular geometric
set-cover problem which is then solved using the local ratio technique and
Varadarajan's quasi-uniform sampling technique. This general algorithmic
approach improves the best known approximation ratios by at least an
exponential factor (and much more in some cases) for essentially all of the
nontrivial common special cases of this problem. Our geometric interpretation
of scheduling may be of independent interest.Comment: Conference version in FOCS 201
The map equation
Many real-world networks are so large that we must simplify their structure
before we can extract useful information about the systems they represent. As
the tools for doing these simplifications proliferate within the network
literature, researchers would benefit from some guidelines about which of the
so-called community detection algorithms are most appropriate for the
structures they are studying and the questions they are asking. Here we show
that different methods highlight different aspects of a network's structure and
that the the sort of information that we seek to extract about the system must
guide us in our decision. For example, many community detection algorithms,
including the popular modularity maximization approach, infer module
assignments from an underlying model of the network formation process. However,
we are not always as interested in how a system's network structure was formed,
as we are in how a network's extant structure influences the system's behavior.
To see how structure influences current behavior, we will recognize that links
in a network induce movement across the network and result in system-wide
interdependence. In doing so, we explicitly acknowledge that most networks
carry flow. To highlight and simplify the network structure with respect to
this flow, we use the map equation. We present an intuitive derivation of this
flow-based and information-theoretic method and provide an interactive on-line
application that anyone can use to explore the mechanics of the map equation.
We also describe an algorithm and provide source code to efficiently decompose
large weighted and directed networks based on the map equation.Comment: 9 pages and 3 figures, corrected typos. For associated Flash
application, see http://www.tp.umu.se/~rosvall/livemod/mapequation
General Bounds for Incremental Maximization
We propose a theoretical framework to capture incremental solutions to
cardinality constrained maximization problems. The defining characteristic of
our framework is that the cardinality/support of the solution is bounded by a
value that grows over time, and we allow the solution to be
extended one element at a time. We investigate the best-possible competitive
ratio of such an incremental solution, i.e., the worst ratio over all
between the incremental solution after steps and an optimum solution of
cardinality . We define a large class of problems that contains many
important cardinality constrained maximization problems like maximum matching,
knapsack, and packing/covering problems. We provide a general
-competitive incremental algorithm for this class of problems, and show
that no algorithm can have competitive ratio below in general.
In the second part of the paper, we focus on the inherently incremental
greedy algorithm that increases the objective value as much as possible in each
step. This algorithm is known to be -competitive for submodular objective
functions, but it has unbounded competitive ratio for the class of incremental
problems mentioned above. We define a relaxed submodularity condition for the
objective function, capturing problems like maximum (weighted) (-)matching
and a variant of the maximum flow problem. We show that the greedy algorithm
has competitive ratio (exactly) for the class of problems that satisfy
this relaxed submodularity condition.
Note that our upper bounds on the competitive ratios translate to
approximation ratios for the underlying cardinality constrained problems.Comment: fixed typo
- …