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Prune-and-search is an excellent algorithmic paradigm for solving various optimization problems. We provide a general scheme for prune-and-search technique and show how to implement it in space-efficient manner. We consider both the in-place and read-only model which have several advantages compared to the traditional model of computation. Our technique can be applied to a large number of problems which accept prune-and-search. For examples, we study the following problems each of which has tremendous practical usage apart from theoretical implication: • computing the minimum enclosing circle (MEC) of a set of n points in R 2 , and • linear programming problems with two and three variables and n constraints. In the in-place setting, all these problems can be solved in O ( n ) time using O ( 1 ) extra-space. In the read-only setup, the time and extra-space complexities of the proposed algorithms for all these problems are O ( n polylog ( n ) ) and O ( polylog ( n ) ) , respectively. [ABSTRACT FROM AUTHOR]

This work generalizes the classical problem of finding the largest empty rectangle among obstacles in 2D. Given a set P of n points, here a maximal empty rectangle (MER) is defined as a rectangle of arbitrary orientation such that each of its four boundaries contain at least one member of P and the interior of the rectangle is empty. We propose a very simple algorithm based on standard data structure to locate a MER of largest area in the plane. The worst-case time complexity of our algorithm is O(n3). Though the worst-case space complexity is O(n2), it reserves O(nlogn) space on an average to maintain the required data structure during the execution of the algorithm. [Copyright &y& Elsevier]

Abstract: In the context of mobile communication, an efficient algorithm for the base-station placement problem is developed in this paper. The objective is to place a given number of base-stations in a given convex region, and to assign range to each of them such that every point in the region is covered by at least one base-station, and the maximum range assigned is minimized. It is basically covering a region by a given number of equal radius circles where the objective is to minimize the radius. We develop an efficient algorithm for this problem using Voronoi diagram which works for covering a convex region of arbitrary shape. Existing results for this problem are available when the region is a square [K.J. Nurmela, P.R.J. Ostergard, Covering a square with up to 30 equal circles, Research Report HUT-TCS-A62, Laboratory for Theoretical Computer Science, Helsinky University of Technology, 2000] and an equilateral triangle [K.J. Nurmela, Conjecturally optimal coverings of an equilateral triangle with up to 36 equal circles, Exp. Math. 9 (2) (2000)]. The minimum radius obtained by our method favorably compares with the results presented in [K.J. Nurmela, P.R.J. Ostergard, Covering a square with up to 30 equal circles, Research Report HUT-TCS-A62, Laboratory for Theoretical Computer Science, Helsinky University of Technology, 2000; K.J. Nurmela, Conjecturally optimal coverings of an equilateral triangle with up to 36 equal circles, Exp. Math. 9 (2) (2000)]. But the execution time of our algorithm is a fraction of a second, whereas the existing methods may even take about two weeks’ time for a reasonable value of the number of circles () as reported in [K.J. Nurmela, P.R.J. Ostergard, Covering a square with up to 30 equal circles, Research Report HUT-TCS-A62, Laboratory for Theoretical Computer Science, Helsinky University of Technology, 2000; K.J. Nurmela, Conjecturally optimal coverings of an equilateral triangle with up to 36 equal circles, Exp. Math. 9 (2) (2000)]. [Copyright &y& Elsevier]

Abstract: The coverage problem in wireless sensor networks addresses the problem of covering a region with sensors. Many different definitions of coverage are there in the literature depending on the goal of the coverage. In this paper, we address the problem of determining the quality of a sensor deployment against an intruder who can walk along a straight line. A line segment is said to be k-covered if it intersects the sensing regions of at least sensors distributed in . Similarly, it is said to be k-uncovered if it intersects the sensing regions, that is assumed to be circular, of at most sensors. We introduce two new metrics, smallest -covered line segment and longest -uncovered line segment, for measuring the quality of line coverage achieved by a sensor deployment. The intruder can walk a distance less than the smallest -covered line segment without ever being detected by sensors. So, this metric gives an estimate on the distance an intruder can walk in a straight line path before being detected by sensors. On the other side, the defender would want to deploy sensors so that the length of the longest -uncovered line segment is minimized. Given a deployment of sensors, we propose deterministic algorithms to determine the smallest -covered line segment and longest -uncovered line segment where the line segments can be of the following types: (i) axis-parallel (horizontal and vertical) line segments, (ii) line segments whose one endpoint is fixed and is of arbitrary orientation and (iii) arbitrary line segments. The time complexities for the first and second types of line segments are for both smallest -covered line segment and longest -uncovered line segment, where is the number of intersections among circles. For the arbitrary line segment case, the smallest -covered segment can be determined in time, whereas, the longest -uncovered segment can be determined in time, where and is a small value greater than or equal to . All our algorithms take linear space. [Copyright &y& Elsevier]