This book introduces unifying techniques in the analysis of approximation algorithms, intended for computer scientists and operations researchers interested in specific algorithm implementations, as well as design tools for algorithms.Expand

The unified technique that is introduced here, referred to as the shifting strategy, is applicable to numerous geometric covering and packing problems and how it varies with problem parameters is illustrated.Expand

A new approach to constructing approximation algorithms, which the aim is find superoptimal, but infeasible solutions, and the performance is measured by the degree of infeasibility allowed, which should find wide applicability for any optimization problem where traditional approximation algorithms have been particularly elusive.Expand

A 2-approximation algorithm for the k-center problem with triangle inequality is presented, the key combinatorial object used is called a strong stable set, and the NP-completeness of the corresponding decision problem is proved.Expand

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… Expand

A family of polynomial-time algorithms are given such that the last job to finish is completed as quickly as possible and the algorithm delivers a solution that is within a relative error of the optimum.Expand

The model proposed here bypasses measurement of the histogram differences in a direct fashion and enables obtaining efficient solutions to the underlying optimization model, and can be solved to optimality in polynomial time using a maximum flow procedure on an appropriately constructed graph.Expand

We describe in this paper polynomial heuristics for three important hard problems—the discrete fixed cost median problem (the plant location problem), the continuous fixed cost median problem in a… Expand

A collection of efficient algorithms that deliver approximate solution to the weighted stable set, vertex cover and set packing problems and guarantee bounds on the ratio of the heuristic Solution to the optimal solution.Expand

An algorithm for solving feasibility in linear programs with up to two variables per inequality which is derived directly from the Fourier--Motzkin elimination method is presented, showing that both a feasible solution and an optimal solution with respect to an arbitrary objective function can be computed in pseudo-polynomial time.Expand