Online Computation And Competitive Analysis Pdf

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online computation and competitive analysis pdf

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In online computation a computer algorithm must decide how to act on incoming items of information without any knowledge of future inputs. How should it route the next telephone call? Where in memory should it store a just-closed record?

Applied Mathematics and Computation

The take home final will be posted here on Jan You must finish the final 48 hours after first reading it. The last day to submit your solutions is Mon, Jan You are not allowed to use any other sources. To get an idea of what the final might be like, take a look at last year's final. This is the final exam ps , pdf. Do not read it before you are ready to work on it. Send Moses email to let him know you have started the test in case any clarifications need to be made.

Princeton University Computer Science Department. Assigned reading. Optional reading and other links. Additional notes on hashing unedited : one , two Network applications of Bloom Filters by Broder and Mitzenmacher. Balls and bins, and a few applications. Chernoff bounds, martingales, Azuma-Hoeffding inequality.

A survey of self-organizing data structures , Albers and Westbrook pages Online algorithms contd: caching - deterministic and randomized algorithms. Lecture notes from Berkeley : deterministic algorithms Section 2 , and randomized algorithms. Online load balancing - uniform machines, restricted machines and related machines. Note: the proof in the restricted machines case is not quite correct.

See the original proof in the paper of Azar, Naor and Rom Section 3. Lecture slides courtesy Kevin Wayne. Notes by Kurt Mehlhorn. Matchings in bipartite graphs, Hall's theorem, augmenting paths. Notes by Santosh Vempala. Sanjeev Arora's notes on LP duality. Michel Goeman's notes on linear programming. Peceptron algorithm. What is machine learning by Rob Schapire.

Andrew Ng's lecture notes on the perceptron algorithm. The multiplicative updates method and connection to lagrangean relaxation and linear programming. The multiplicative weights method: a meta-algorithm and its applications by Sanjeev Arora, Elad Hazan, and Satyen Kale.

Santosh Vempala's notes on the ellipsoid method. For a discussion of number of bits of precision needed, see Section 11 of Michel Goeman's notes on linear programming. Sections in these notes have a description of the Simplex method which we briefly outlined in class. I mentioned the paper of Spielman and Teng that introduced smoothed analysis as a method for analyzing the practical behavior of algorithms, inspired by trying to understand why Simplex works well in practice.

Read Section 1 for an overview of work in this area. David Williamson's lecture notes on various ways to design approximation algorithms for set cover. Satish Rao's lecture notes on minimum congestion routing via LP. This paper showed that the ln n guarantee for set cover is almost optimal. A recent paper shows that the Raghavan-Thompson randomized rounding procedure for minimum congestion is essentially optimal. Approximation Algorithms based on Linear Programs contd.

David Williamson's lecture notes on SDP based approximation algorithms. The second Karger-Motwani-Sudan rounding procedure for graph coloring described in Williamson's notes is slightly different from the one I showed in class.

See page 11 of the paper by Karger Motwani Sudan for a description of this. Sanjeev Arora's notes on eigenvalues and expansion. Notes on randomized min-cut from CMU. Motwani and Raghavan, Chapter 7, pages Parallel and Distributed Algorithms. Notes on parallel maximal independent set from Georgia Tech.

Motwani and Raghavan, pages The space complexity of approximating the frequency moments by Alon, Matias and Szegedy.

Online computation and competitive analysis

Skip to Main Content. A not-for-profit organization, IEEE is the world's largest technical professional organization dedicated to advancing technology for the benefit of humanity. Use of this web site signifies your agreement to the terms and conditions. Competitive analysis for online scheduling in software-defined optical WAN Abstract: Modern planetary-scale online services have massive data to transfer over the wide area network WAN. Due to the tremendous cost of building WANs and the stringent timing requirement of distributed applications, it is critical for network operators to make efficient use of network resources to optimize data transfers.


theoretical foundations, applications, and examples of competitive analysis for online algorithms. An Introduction to Online Computation-.


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News Flash. Please note the change in office hours for Dilys Thomas. Mailing Lists and Newsgroup We have set up a class mailing list to help you get the latest information regarding the class. The email lists are auto-populated using current course enrolment information. The main list will be csa-autall lists.

We consider a model for online computation in which the online algorithm receives, together with each request, some information regarding the future, referred to as advice. The advice provided to the online algorithm may allow an improvement in its performance, compared to the classical model of complete lack of information regarding the future. We are interested in the impact of such advice on the competitive ratio, and in particular, in the relation between the size b of the advice, measured in terms of bits of information per request, and the improved competitive ratio.

Competitive analysis (online algorithm)

Skip to search form Skip to main content You are currently offline. Some features of the site may not work correctly. Preface 1.

The take home final will be posted here on Jan You must finish the final 48 hours after first reading it. The last day to submit your solutions is Mon, Jan You are not allowed to use any other sources. To get an idea of what the final might be like, take a look at last year's final.

Competitive analysis is a method invented for analyzing online algorithms , in which the performance of an online algorithm which must satisfy an unpredictable sequence of requests, completing each request without being able to see the future is compared to the performance of an optimal offline algorithm that can view the sequence of requests in advance. An algorithm is competitive if its competitive ratio —the ratio between its performance and the offline algorithm's performance—is bounded. Unlike traditional worst-case analysis , where the performance of an algorithm is measured only for "hard" inputs, competitive analysis requires that an algorithm perform well both on hard and easy inputs, where "hard" and "easy" are defined by the performance of the optimal offline algorithm. For many algorithms, performance is dependent not only on the size of the inputs, but also on their values. For example, sorting an array of elements varies in difficulty depending on the initial order. Such data-dependent algorithms are analysed for average-case and worst-case data. Competitive analysis is a way of doing worst case analysis for on-line and randomized algorithms , which are typically data dependent.


Review ol ~. Online Computation and Competitive Analysis. Authors" Allan Borodin and Ran EI-Yaniv. Publisher: Cambridge University Press. Hardcover: ISBN.


Applied Mathematics and Computation

3 Comments

  1. Warrane d. L. 21.01.2021 at 19:52

    Preface 1. Introduction to competitive analysis: the list accessing problem 2. Introduction to randomized algorithms: the list accessing problem 3. Paging.

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