By Amir Abboud, Kevin Lewi (auth.), Fedor V. Fomin, Rūsiņš Freivalds, Marta Kwiatkowska, David Peleg (eds.)
This two-volume set of LNCS 7965 and LNCS 7966 constitutes the refereed complaints of the fortieth foreign Colloquium on Automata, Languages and Programming, ICALP 2013, held in Riga, Latvia, in July 2013. the full of 124 revised complete papers provided have been rigorously reviewed and chosen from 422 submissions. they're prepared in 3 tracks focussing on algorithms, complexity and video games; common sense, semantics, automata and conception of programming; and foundations of networked computation.
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Extra info for Automata, Languages, and Programming: 40th International Colloquium, ICALP 2013, Riga, Latvia, July 8-12, 2013, Proceedings, Part I
Then we will show that the opt(I) must be more than T , which will contradict our assumption. In the discussion below, we only look at jobs which were considered before j by A. We build a set S of jobs recursively. Initially S just contains j . We add a job j of type (k , l ) to S if there is a job j of type (k, l) in S satisfying the following conditions: – The class k of j is at most k . – The algorithm A processes j on a machine i which is valid for j as well. , I(k , l ) ⊆ I(k, l). We use this rule to add jobs to S as long as possible.
Competitive algorithm with (1 + 3ε)- Proof. Consider a job j of class type (k, l). Suppose it gets processed on machine i. The algorithm B adds j to the queue Q(i, k, l). Property (ii) above implies that the total remaining processing time of these jobs (on i) is at most (1+2ε)|I(k, l)|. Consider an interval I which starts at the beginning of I(k, l) and has length (1+2ε)|I(k,l)| = (1+2ε)T . The jobs of J≥k that B can process on i during ε ε2 2k I are either (i) jobs in Q(i, k, l), or (ii) jobs processed by A on machine i during I (using property (i) above).
199–210 (2008) 11. : An online scalable algorithm for minimizing k -norms of weighted ﬂow time on unrelated machines. In: 22nd Symp. Discrete Algorithms (SODA), pp. 95–108 (2011) 12. : Optimal time-critical scheduling via resource augmentation. Algorithmica 32(2), 163–200 (2002) Tight Lower Bound for Linear Sketches of Moments Alexandr Andoni1 , Huy L. edu Abstract. The problem of estimating frequency moments of a data stream has attracted a lot of attention since the onset of streaming algorithms [AMS99].