Dorit S. Hochbaum

Learn More
A unified and powerful approach is presented for devising polynomial approximation schemes for many strongly NP-complete problems. Such schemes consist of families of approximation algorithms for each desired performance bound on the relative error ε > &Ogr;, with running time that is polynomial when ε is fixed. Though the polynomiality of(More)
The problem of scheduling a set of <italic>n</italic> jobs on <italic>m</italic> identical machines so as to minimize the makespan time is perhaps the most well-studied problem in the theory of approximation algorithms for NP-hard optimization problems. In this paper the strongest possible type of result for this problem, a polynomial approximation scheme,(More)
We present a polynomial approximation scheme for the minimum makespan problem on uniform parallel processors. More specifically, the problem is to find a schedule for a set of independent jobs on a collection of machines of different speeds so that the last job to finish is completed as quickly as possible. We give a family of polynomial-time algorithms {A}(More)
A cover in a graph G is a set of vertices C such that each edge of G has at least one endpoint in C. The vertex cover problem is the problem of finding a cover of the smallest weight in a graph whose vertices carry positive weights. This problem is known to be NP-complete even when the input is restricted to planar cubic graphs with unit weights [8]. A(More)
In this paper a powerful, and yet simple, technique for devising approximation algorithms for a wide variety of NP-complete problems in routing, location, and communication network design is investigated. Each of the algorithms presented here delivers an approximate solution guaranteed to be within a constant factor of the optimal solution. In addition, for(More)
This paper is focused on the Co-segmentation problem [1] - where the objective is to segment a similar object from a pair of images. The background in the two images may be arbitrary; therefore, simultaneous segmentation of both images must be performed with a requirement that the appearance of the two sets of foreground pixels in the respective images are(More)
The authors present an O(inn log m) algorithm for solving feasibility in linear programs with up to two variables per inequality which is derived directly from the Fourier-Motzkin elimination method. (The number of variables and inequalities are denoted by n and m, respectively.) The running time of the algorithm dominates that of the best known algorithm(More)