831 research outputs found
Single machine scheduling with controllable processing times by submodular optimization
In scheduling with controllable processing times the actual processing time of each job is to be chosen from the interval between the smallest (compressed or fully crashed) value and the largest (decompressed or uncrashed) value. In the problems under consideration, the jobs are processed on a single machine and the quality of a schedule is measured by two functions: the maximum cost (that depends on job completion times) and the total compression cost. Our main model is bicriteria and is related to determining an optimal trade-off between these two objectives. Additionally, we consider a pair of associated single criterion problems, in which one of the objective functions is bounded while the other one is to be minimized. We reduce the bicriteria problem to a series of parametric linear programs defined over the intersection of a submodular polyhedron with a box. We demonstrate that the feasible region is represented by a so-called base polyhedron and the corresponding problem can be solved by the greedy algorithm that runs two orders of magnitude faster than known previously. For each of the associated single criterion problems, we develop algorithms that deliver the optimum faster than it can be deduced from a solution to the bicriteria problem
Total variation denoising in anisotropy
We aim at constructing solutions to the minimizing problem for the variant of
Rudin-Osher-Fatemi denoising model with rectilinear anisotropy and to the
gradient flow of its underlying anisotropic total variation functional. We
consider a naturally defined class of functions piecewise constant on
rectangles (PCR). This class forms a strictly dense subset of the space of
functions of bounded variation with an anisotropic norm. The main result shows
that if the given noisy image is a PCR function, then solutions to both
considered problems also have this property. For PCR data the problem of
finding the solution is reduced to a finite algorithm. We discuss some
implications of this result, for instance we use it to prove that continuity is
preserved by both considered problems.Comment: 34 pages, 9 figure
Capacitated max-Batching with Interval Graph Compatibilities
We consider the problem of partitioning interval graphs into cliques of bounded size. Each interval has a weight, and the cost of a clique is the maximum weight of any interval in the clique. This natural graph problem can be interpreted as a batch scheduling problem. Solving an open question from [7, 4, 5], we show NP-hardness, even if the bound on the clique sizes is constant. Moreover, we give a PTAS based on a novel dynamic programming technique for this case.
Approximation Algorithms for the Max-Buying Problem with Limited Supply
We consider the Max-Buying Problem with Limited Supply, in which there are
items, with copies of each item , and bidders such that every
bidder has valuation for item . The goal is to find a pricing
and an allocation of items to bidders that maximizes the profit, where
every item is allocated to at most bidders, every bidder receives at most
one item and if a bidder receives item then . Briest
and Krysta presented a 2-approximation for this problem and Aggarwal et al.
presented a 4-approximation for the Price Ladder variant where the pricing must
be non-increasing (that is, ). We present an
-approximation for the Max-Buying Problem with Limited Supply and, for
every , a -approximation for the Price Ladder
variant
A Technique for Obtaining True Approximations for -Center with Covering Constraints
There has been a recent surge of interest in incorporating fairness aspects
into classical clustering problems. Two recently introduced variants of the
-Center problem in this spirit are Colorful -Center, introduced by
Bandyapadhyay, Inamdar, Pai, and Varadarajan, and lottery models, such as the
Fair Robust -Center problem introduced by Harris, Pensyl, Srinivasan, and
Trinh. To address fairness aspects, these models, compared to traditional
-Center, include additional covering constraints. Prior approximation
results for these models require to relax some of the normally hard
constraints, like the number of centers to be opened or the involved covering
constraints, and therefore, only obtain constant-factor pseudo-approximations.
In this paper, we introduce a new approach to deal with such covering
constraints that leads to (true) approximations, including a -approximation
for Colorful -Center with constantly many colors---settling an open question
raised by Bandyapadhyay, Inamdar, Pai, and Varadarajan---and a
-approximation for Fair Robust -Center, for which the existence of a
(true) constant-factor approximation was also open. We complement our results
by showing that if one allows an unbounded number of colors, then Colorful
-Center admits no approximation algorithm with finite approximation
guarantee, assuming that . Moreover, under the
Exponential Time Hypothesis, the problem is inapproximable if the number of
colors grows faster than logarithmic in the size of the ground set
Packing While Traveling: Mixed Integer Programming for a Class of Nonlinear Knapsack Problems
Packing and vehicle routing problems play an important role in the area of
supply chain management. In this paper, we introduce a non-linear knapsack
problem that occurs when packing items along a fixed route and taking into
account travel time. We investigate constrained and unconstrained versions of
the problem and show that both are NP-hard. In order to solve the problems, we
provide a pre-processing scheme as well as exact and approximate mixed integer
programming (MIP) solutions. Our experimental results show the effectiveness of
the MIP solutions and in particular point out that the approximate MIP approach
often leads to near optimal results within far less computation time than the
exact approach
Ranking of multidimensional drug profiling data by fractional-adjusted bi-partitional scores
Motivation: The recent development of high-throughput drug profiling (high content screening or HCS) provides a large amount of quantitative multidimensional data. Despite its potentials, it poses several challenges for academia and industry analysts alike. This is especially true for ranking the effectiveness of several drugs from many thousands of images directly. This paper introduces, for the first time, a new framework for automatically ordering the performance of drugs, called fractional adjusted bi-partitional score (FABS). This general strategy takes advantage of graph-based formulations and solutions and avoids many shortfalls of traditionally used methods in practice. We experimented with FABS framework by implementing it with a specific algorithm, a variant of normalized cut—normalized cut prime (FABS-NC′), producing a ranking of drugs. This algorithm is known to run in polynomial time and therefore can scale well in high-throughput applications
Matroid and Knapsack Center Problems
In the classic -center problem, we are given a metric graph, and the
objective is to open nodes as centers such that the maximum distance from
any vertex to its closest center is minimized. In this paper, we consider two
important generalizations of -center, the matroid center problem and the
knapsack center problem. Both problems are motivated by recent content
distribution network applications. Our contributions can be summarized as
follows:
1. We consider the matroid center problem in which the centers are required
to form an independent set of a given matroid. We show this problem is NP-hard
even on a line. We present a 3-approximation algorithm for the problem on
general metrics. We also consider the outlier version of the problem where a
given number of vertices can be excluded as the outliers from the solution. We
present a 7-approximation for the outlier version.
2. We consider the (multi-)knapsack center problem in which the centers are
required to satisfy one (or more) knapsack constraint(s). It is known that the
knapsack center problem with a single knapsack constraint admits a
3-approximation. However, when there are at least two knapsack constraints, we
show this problem is not approximable at all. To complement the hardness
result, we present a polynomial time algorithm that gives a 3-approximate
solution such that one knapsack constraint is satisfied and the others may be
violated by at most a factor of . We also obtain a 3-approximation
for the outlier version that may violate the knapsack constraint by
.Comment: A preliminary version of this paper is accepted to IPCO 201
- …