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          <dc:title>Scheduling with Setup Costs and Monotone Penalties</dc:title>
          <dc:creator>Khandekar, Rohit</dc:creator>
          <dc:creator>Hildrum, Kirsten</dc:creator>
          <dc:creator>Rajan, Deepak</dc:creator>
          <dc:creator>Wolf, Joel</dc:creator>
          <dc:subject>Scheduling</dc:subject>
          <dc:subject>resource augmentation</dc:subject>
          <dc:subject>approximation algorithm</dc:subject>
          <dc:subject>preemption</dc:subject>
          <dc:subject>setup times</dc:subject>
          <dc:description>We consider single processor preemptive scheduling with job-dependent setup times. In this model, a job-dependent setup time is incurred when a job is started for the first time, and each time it is restarted after preemption. This model is a common generalization of preemptive scheduling, and actually of non-preemptive scheduling as well. The objective is to minimize the sum of any general non-negative, non-decreasing cost functions of the completion times of the jobs -- this generalizes objectives of minimizing weighted flow time, flow-time squared, tardiness or the number of tardy jobs among many others. Our main result is a randomized polynomial time O(1)-speed O(1)-approximation algorithm for this problem. Without speedup, no polynomial time finite multiplicative approximation is possible unless P=NP.&#13;
&#13;
We extend the approach of Bansal et al. (FOCS 2007) of rounding a linear programming relaxation which accounts for costs incurred due to the non-preemptive nature of the schedule. A key new idea used in the rounding is that a point in the intersection polytope of two matroids can be decomposed as a convex combination of incidence vectors of sets that are independent in both matroids. In fact, we use this for the intersection of a partition matroid and a laminar matroid, in which case the decomposition can be found efficiently using network flows.&#13;
Our approach gives a randomized polynomial time offline O(1)-speed O(1)-approximation algorithm for the broadcast scheduling problem with general cost functions as well.</dc:description>
          <dc:publisher>Schloss Dagstuhl – Leibniz-Zentrum für Informatik</dc:publisher>
          <dc:contributor>Rohit Khandekar and Kirsten Hildrum and Deepak Rajan and Joel Wolf</dc:contributor>
          <dc:date>2012</dc:date>
          <dc:relation>Is Part Of LIPIcs, Volume 18, IARCS Annual Conference on Foundations of Software Technology and Theoretical Computer Science (FSTTCS 2012)</dc:relation>
          <dc:type>InProceedings</dc:type>
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          <dc:identifier>doi:10.4230/LIPIcs.FSTTCS.2012.185</dc:identifier>
          <dc:identifier>urn:nbn:de:0030-drops-38576</dc:identifier>
          <dc:identifier>https://drops.dagstuhl.de/entities/document/10.4230/LIPIcs.FSTTCS.2012.185</dc:identifier>
          <dc:language>eng</dc:language>
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