Script of Lecture 7 , Approximation Algorithms Summer term 2017
نویسندگان
چکیده
1 The Uncapacitated Facility Location Problem Input: a set D of demands (or clients); a set F of facilities; for each client j ∈ D and facility i ∈ F , there is a cost cij of assigning client j to facility i; and there is a cost fi associated with each facility i ∈ F . Output: Choose a subset of facilities F ′ ⊆ F which minimizes the total cost of facilities in F ′ plus the total cost of assigning each client to the nearest facility in F ′, i. e., ∑ i∈F ′ fi + ∑ j∈D mini∈F ′ cij . (The first term is called the facility cost, and the second term is called the assignment cost.)
منابع مشابه
Script of Lecture 3 , Approximation Algorithms Summer term 2017
We first scale the instance. If we see two points x, y as a vector, we can replace them by αx and αy. By the properties of a norm, w(αx, αy) = ‖αx − αy‖ = α‖x − y‖. Therefore all distances are scaled by α. We choose an α > 0 such that all vertices fit exactly into an axis-parallel n2 × n2 square (where n = |V |), i. e., all vertices fit into the square and there are two vertices that lie on opp...
متن کاملA Course on Independent Component Analysis
Foreword This text is a script of a two-hour a week lecture about Independent Component Analysis held at the University of Regensburg by Elmar Lang and me during the summer term 2003. The text is largely based on the textbook [Hyvärinen et al., 2001a]. Furthermore , a longer version of this text also served as the introductory chapters of my dissertation in computer science at the University of...
متن کاملApproximation Algorithms for Facility Location Problems
This paper surveys approximation algorithms for various facility location problems, mostly with detailed proofs. It resulted from lecture notes of a course held at the University of Bonn in the winter term 2004/2005.
متن کاملGenetic Algorithms: Theory and Applications
FLLL 2 Preface This is a printed collection of the contents of the lecture " Genetic Algorithms: Theory and Applications " which I gave first in the winter semester 1999/2000 at the Johannes Kepler University in Linz. The reader should be aware that this manuscript is subject to further reconsideration and improvement. Corrections, complaints, and suggestions are cordially welcome. The sources ...
متن کاملData Sparse Matrix Computation - Lecture 11
2 Randomized algorithms 4 2.1 Randomized low-rank factorization . . . . . . . . . . . . . . . . . 4 2.2 How to find such a Q . . . . . . . . . . . . . . . . . . . . . . . . . 5 2.3 How to construct Q with randomness . . . . . . . . . . . . . . . . 5 2.4 An adaptive randomized range finder algorithm . . . . . . . . . . 6 2.5 Example of implementation of the adaptive range approximation method . ...
متن کامل