P. Raghavan. A statistical adversary for on-line algorithms. In D. D. Sleator L. A. McGeoch, editor, On-Line Algorithms, volume 7 of Series in Discrete Mathematics and Theoretical Computer Science, pages 79--83. American Mathematical Society, 1992.  Home/Search   Document Not in Database   Summary   Related Articles   Check

....our market agents must ensure that they are insured against complete destruction by market forces. To our knowledge, the nancial community has not yet introduced on line algorithms into their discussions. The on line algorithm community has treated some basic nancial problems. An example is [35], which considers optimal portfolio selection while trading in one risky security and a money market. This problem was given the name Two Way Trading by the on line algorithms community, and has been treated by subsequent authors. Most notably, 10] claims an intriguing result: that the binomial ....

Raghavan, P., A statistical adversary for on-line algorithms, DIMACS Series in Discrete Math. Theor. Comput. Sci. No. 7, 79-83, 1992.

....to the two way trading problem. There is a considerable body of work related to two way trading and portfolio selection. From the perspective of competitive analysis, there are a number of results which are somewhat related to the results presented in this section. For example, Raghavan [27] and Chou et al. 9] 10] study an online two way trading problem against statistical adversaries, which must produce exchange rate sequences that conform to some statistical constraints. Cover and Ordentlich [13] 25] Helmbold et al. 18] and Blum and Kalai [6] study the general portfolio ....

P. Raghavan. A statistical adversary for on-line algorithms. In L.A. McGeoch and D.D. Sleator, editors, On-Line Algorithms, DIMACS Series in Discrete Mathematics and Theoretical Computer Science, Volume 7, pages 79--83. American Mathematical Society, Providence, RI, 1992.

....worst case measures. He discusses search problems, replacement problems, portfolio selection, and leasing problems. In particular, in online portfolio selection problems further different classes of adversaries, e.g. statistical adversaries, are introduced. This concept goes back to Raghavan [50] who applied it successfully to special portfolio problems (cf. also [17, 23] Discrete Optimization Problems In classical bin packing, n items of different size have to be packed into bins of fixed size. The number of bins used is to be minimized. Bin packing is NP hard. In the online ....

Prabhakar Raghavan. A statistical adversary for on-line algorithms. In Lyle A. McGeoch and Daniel D. Sleator, editors, On-line Algorithms, volume 7 of DIMACS Series in Discrete Mathematics and Theoretical Computer Science, pages 79--84. AMS/ACM, February 1991.

....best investment strategy might be never to invest (and instead spend it all skiing) Therefore research in this area has focused on creating on line models that, while remaining worst case, allow some realistic constraints to be included. Raghavan s statistical adversary model [Chou et al. 1995, Raghavan, 1992] allows an adversary to pick any input sequence (for example a sequence of daily share prices) as long as the sequence exhibits a particular statistical property (for example, a mean value within some range) Al Binali [al Binali, 1997] introduces a competitive risk reward framework, in which an ....

Raghavan, P. (1992). A statistical adversary for on-line algorithms. dimacs Series in Discrete Mathematics and Theoretical Computer Science, 7:79--83.

....Access graphs can model more realistic request sequences that exhibit locality of reference. It was shown that, using the access graph, it is possible to overcome some negative aspects of conventional competitive paging results [17, 20, 21, 27] With respect to online financial games, Raghavan [38] introduced a statistical adversary: The input generated by the adversary must satisfy certain statistical assumptions. In [19] Chou et al. developed further results in this model. More generally, Koutsoupias and Papadimitriou [31] proposed the diffuse adversary model. An adversary must generate ....

P. Raghavan. A statistical adversary for on-line algorithms. In On-Line Algorithms, DIMACS Series in Discrete Mathematics and Theoretical Computer Science, 79--83, 1991.

....are really of a different flavor in that they are primarily concerned with the effect of partial clairvoyance. Other methods have been suggested to address the limitations of competitive analysis. These methods include restricting the input distribution to satisfy some special properties (e.g. [10, 16]) and comparing the cost of a solution produced by an online algorithm on input I to the worst case optimal cost of any input of the same size as I [5] 3 Average Response Time The following well known lemma explains why we first consider the queue size. Lemma 3.1 For any scheduling algorithm A ....

P. Raghavan, "A statistical adversary for on-line algorithms", Online Algorithms, DIMACS Series in Discrete Mathematics and Computer Science, 7:79--83, 1992.

....access graph. Access graphs can model more realistic request sequences that exhibit locality of reference. It was shown [26, 45] that, using access graphs, it is possible to overcome some negative aspects of conventional competitive paging results. With respect to online financial games, Raghavan [60] introduced a statistical adversary: The input generated by the adversary must satisfy certain statistical assumptions. In [29] Chou et al. developed further results in this model. More generally, Koutsoupias and Papdimitriou [53] proposed the diffuse adversary model. An adversary must generate ....

P. Raghavan. A statistical adversary for on-line algorithms. In On-Line Algorithms, DIMACS Series in Discrete Mathematics and Theoretical Computer Science, 79--83, 1991.

No context found.

P. Raghavan. A statistical adversary for on-line algorithms. In D. D. Sleator L. A. McGeoch, editor, On-Line Algorithms, volume 7 of Series in Discrete Mathematics and Theoretical Computer Science, pages 79--83. American Mathematical Society, 1992.

No context found.

P. Raghavan, "A statistical adversary for on--line algorithms," in On-Line Algorithms, D. Sleator and L. McGeoch, Eds., DIMACS Series in Discrete Mathematics and Theoretical Computer Science (vol. 7), 1992.

No context found.

P. Raghavan. A statistical adversary for on-line algorithms. In DIMACS Series in Discrete Mathematics and Theoretical Computer Scie nce, pages 79--83, 1991.

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