Dror Rawitz

Hitting sets when the VC-dimension is small

We present an approximation algorithm for the hitting set problem when the VC-dimension of the set system is small. Our algorithm uses a linear programming relaxation to compute a probability measure for which @?-nets are always hitting sets (see Corollary 15.6 in Pach and Agarwal [Combinatorial Geometry, J. Wiley, New York, 1995]). The comparable algorithm of Bronnimann and Goodrich [Almost optimal set covers in finite VC-dimension, Discrete Comput. Geom. 14 (1995) 463] computes such a probability measure by an iterative reweighting technique. The running time of our algorithm is comparable with theirs, and the approximation ratio is smaller by a constant factor. We also show how our algorithm can be parallelized and extended to the minimum cost hitting set problem.

On the Equivalence between the Primal-Dual Schema and the Local Ratio Technique

We discuss two approximation paradigms that were used to construct many approximation algorithms during the last two decades, the primal-dual schema and the local ratio technique. Recently, primal-dual algorithms were devised by first constructing a local ratio algorithm and then transforming it into a primal-dual algorithm. This was done in the case of the

Rent, Lease, or Buy: Randomized Algorithms for Multislope Ski Rental

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Approximating the 2-interval pattern problem

-approximation algorithms for the feedback vertex set problem and in the case of the first primal-dual algorithms for maximization problems. Subsequently, the nature of the connection between the two paradigms was posed as an open question by Williamson [Math. Program., 91 (2002), pp. 447--478]. In this paper we answer this question by showing that the two paradigms are equivalent.

Algorithms for capacitated rectangle stabbing and lot sizing with joint set-up costs

In the multislope ski rental problem, the user needs a certain resource for some unknown period of time. To use the resource, the user must subscribe to one of several options, each of which consists of a one-time setup cost (“buying price”) and cost proportional to the duration of the usage (“rental rate”). The larger the price, the smaller the rent. The actual usage time is determined by an adversary, and the goal of an algorithm is to minimize the cost by choosing the best alternative at any point in time. Multislope ski rental is a natural generalization of the classical ski rental problem (where there are only two available alternatives, namely pure rent and pure buy), which is one of the fundamental problems of online computation. The multislope ski rental problem is an abstraction of many problems, where online choices cannot be modeled by just two alternatives, e.g., power management in systems which can be shut down in parts. In this paper we study randomized algorithms for multislope ski rental....

Efficient algorithms for integer programs with two variables per constraint

We address the issue of approximating the 2-Interval Pattern problem over its various models and restrictions. This problem, motivated by RNA secondary structure prediction, asks to find a maximum cardinality subset of a 2-interval set with respect to some prespecified geometric constraints. We present several constant factor approximation algorithms whose performance guarantee depends on the different possible restrictions imposed on the input 2-interval set. In addition, we show that our results extend to the weighted variant of the problem.

Ski rental with two general options

In the rectangle stabbing problem, we are given a set of axis parallel rectangles and a set of horizontal and vertical lines, and our goal is to find a minimum size subset of lines that intersect all the rectangles. In this article, we study the capacitated version of this problem in which the input includes an integral capacity for each line. The capacity of a line bounds the number of rectangles that the line can cover. We consider two versions of this problem. In the first, one is allowed to use only a single copy of each line (hard capacities), and in the second, one is allowed to use multiple copies of every line, but the multiplicities are counted in the size (or weight) of the solution (soft capacities). We present an exact polynomial-time algorithm for the weighted one dimensional case with hard capacities that can be extended to the one dimensional weighted case with soft capacities. This algorithm is also extended to solve a certain capacitated multi-item lot-sizing inventory problem with joint set-up costs. For the case of d-dimensional rectangle stabbing with soft capacities, we present a 3d-approximation algorithm for the unweighted case. For d-dimensional rectangle stabbing problem with hard capacities, we present a bi-criteria algorithm that computes 4d-approximate solutions that use at most two copies of every line. Finally, we present hardness results for rectangle stabbing when the dimension is part of the input and for a two-dimensional weighted version with hard capacities.

Maximizing Barrier Coverage Lifetime with Mobile Sensors

Abstract. Given a bounded integer program with n variables and m constraints, each with two variables, we present an O(mU) time and O(m) space feasibility algorithm, where U is the maximal variable range size. We show that with the same complexity we can find an optimal solution for the positively weighted minimization problem for monotone systems. Using the local-ratio technique we develop an O(nmU) time and O(m) space 2 -approximation algorithm for the positively weighted minimization problem for the general case. We further generalize all results to nonlinear constraints (called axis-convex constraints ) and to nonlinear (but monotone) weight functions.Our algorithms are not only better in complexity than other known algorithms, but also considerably simpler, and they contribute to the understanding of these very fundamental problems.

Online set packing and competitive scheduling of multi-part tasks

We define and solve a simple extension of the ski-rental problem [A.R. Karlin, M.S. Manasse, L. Rudolph, D.D. Sleator, Competitive snoopy caching, Algorithmica 3 (1) (1988) 77-119]. In the classical version, the algorithm needs to decide when to switch from renting to buying. In our version, no pure buy option is available: even after switching to the buy option, the algorithm needs to pay some reduced rent.

Resource allocation in bounded degree trees

Sensor networks are ubiquitously used for detection and tracking and as a result covering is one of the main tasks of such networks. We study the problem of maximizing the coverage lifetime of a barrier by mobile sensors with limited battery powers, where the coverage lifetime is the time until there is a breakdown in coverage due to the death of a sensor. Sensors are first deployed and then coverage commences. Energy is consumed in proportion to the distance traveled for mobility, while for coverage, energy is consumed in direct proportion to the radius of the sensor raised to a constant exponent. We study two variants which are distinguished by whether the sensing radii are given as part of the input or can be optimized, the fixed radii problem and the variable radii problem. We design parametric search algorithms for both problems for the case where the final order of the sensors is predetermined and for the case where sensors are initially located at barrier endpoints. In contrast, we show that the variable radii problem is strongly NP-hard and provide hardness of approximation results for fixed radii for the case where all the sensors are initially co-located at an internal point of the barrier.