
<oai_dc:dc xmlns:dc="http://purl.org/dc/elements/1.1/" xmlns:oai_dc="http://www.openarchives.org/OAI/2.0/oai_dc/" xmlns:xsi="http://www.w3.org/2001/XMLSchema-instance" xsi:schemaLocation="http://www.openarchives.org/OAI/2.0/oai_dc/ http://www.openarchives.org/OAI/2.0/oai_dc.xsd">
  <dc:title>EFFICIENT ALGORITHMS FOR POWER ASSIGNMENT PROBLEMS</dc:title>
  <dc:creator>Qiao, Kan</dc:creator>
  <dc:description>Power assignment problems take as input a directed simple graph G = (V;E) and a cost function c : E ! R+. A solution to this problem assigns every vertex a nonnegative power, p(v). We use H = (V;B(p)) to denote the spanning subgraph of G created by this power assignment. Let B(p) denote the set of all the links established between pairs of nodes in V under the power assignment p. The minimization problem then is to find the minimum power assignment, Pp(v), subject to H satisfying a specific property. 4 variants of this problem are discussed in this paper (a) Min-Power Strong Connectivity: H = (V;B(p)) is strongly connected. (b) Min-Power Broadcast: H = (V;B(p)) has a path from the fixed source z to every other vertex. (c) Min-Power Connectivity with 2-level power (Symmetric): c : E ! f0; 1g and H = (V;B(p)) is connected. (d) Min- Power Strong Connectivity with 2-level power (Asymmetric): c : E ! f0; 1g and H = (V;B(p)) is strongly connected. We give the exact solution using an improved integer linear program for problem (a) and (b) (We do not have a section for the integer linear program of Min-Power Broadcast problem since it is very similar to Min-Power Strong connectivity). Then we try to speedup current best approximation algorithms while preserving their approximation ratio. For problem (a), we give a fast variant of 1:85-approximation algorithm with running time O(n2 log2 n). For problem (b), we give a fast variant of 2(1 + ln n)-approximation algorithm for the most general cost model with running time O(n3) and a fast variant of 4:2- approximation algorithm for 2-dimensional cost model with running time O(nm), where n = jV j and m = jEj. For both problem (c) and (d), We give 5 3-approximation algorithms that run in O(m (n)), where (n) is the inverse Ackermann function.</dc:description>
  <dc:description>Ph.D. in Computer Science, May 2015</dc:description>
  <dc:contributor>Calinescu, Gruia</dc:contributor>
  <dc:date>2015</dc:date>
  <dc:date>2015-05</dc:date>
  <dc:type>Dissertation</dc:type>
  <dc:format>application/pdf</dc:format>
  <dc:identifier>islandora:7257</dc:identifier>
  <dc:identifier>http://hdl.handle.net/10560/3565</dc:identifier>
  <dc:source>CS / Computer Science</dc:source>
  <dc:source>Illinois Institute of Technology</dc:source>
  <dc:language>en</dc:language>
  <dc:rights>In Copyright</dc:rights>
  <dc:rights>http://rightsstatements.org/page/InC/1.0/</dc:rights>
  <dc:rights>Restricted Access</dc:rights>
</oai_dc:dc>
