Standard

Approximation of additive random fields based on standard information: Average case and probabilistic settings. / Lifshits, M.; Zani, M.

In: Journal of Complexity, No. 5, 2015, p. 659-674.

Research output: Contribution to journal › Article

Harvard

Lifshits, M & Zani, M 2015, 'Approximation of additive random fields based on standard information: Average case and probabilistic settings', Journal of Complexity, no. 5, pp. 659-674. https://doi.org/10.1016/j.jco.2015.05.002

APA

Lifshits, M., & Zani, M. (2015). Approximation of additive random fields based on standard information: Average case and probabilistic settings. Journal of Complexity, (5), 659-674. https://doi.org/10.1016/j.jco.2015.05.002

Vancouver

Lifshits M, Zani M. Approximation of additive random fields based on standard information: Average case and probabilistic settings. Journal of Complexity. 2015;(5):659-674. https://doi.org/10.1016/j.jco.2015.05.002

Author

Lifshits, M. ; Zani, M. / Approximation of additive random fields based on standard information: Average case and probabilistic settings. In: Journal of Complexity. 2015 ; No. 5. pp. 659-674.

BibTeX

@article{b81953c1243c478b9d6d457e179fe198,
title = "Approximation of additive random fields based on standard information: Average case and probabilistic settings",
abstract = "{\textcopyright} 2015 Elsevier Inc.Abstract We consider approximation problems for tensor product and additive random fields based on standard information in the average case setting. We also study the probabilistic setting of the mentioned problem for tensor products. The main question we are concerned with in this paper is {"}How much do we loose by considering standard information algorithms against those using general linear information?{"} For both types of the fields, the error of linear algorithms has been studied in great detail; however, the power of standard information was not addressed so far, which we do here. Our main result is that in most interesting cases there is no more than a logarithmic loss in approximation error when information is being restricted to the standard one. The results are obtained by randomization techniques.",
author = "M. Lifshits and M. Zani",
year = "2015",
doi = "10.1016/j.jco.2015.05.002",
language = "English",
pages = "659--674",
journal = "Journal of Complexity",
issn = "0885-064X",
publisher = "Elsevier",
number = "5",

}

RIS

TY - JOUR

T1 - Approximation of additive random fields based on standard information: Average case and probabilistic settings

AU - Lifshits, M.

AU - Zani, M.

PY - 2015

Y1 - 2015

N2 - © 2015 Elsevier Inc.Abstract We consider approximation problems for tensor product and additive random fields based on standard information in the average case setting. We also study the probabilistic setting of the mentioned problem for tensor products. The main question we are concerned with in this paper is "How much do we loose by considering standard information algorithms against those using general linear information?" For both types of the fields, the error of linear algorithms has been studied in great detail; however, the power of standard information was not addressed so far, which we do here. Our main result is that in most interesting cases there is no more than a logarithmic loss in approximation error when information is being restricted to the standard one. The results are obtained by randomization techniques.

AB - © 2015 Elsevier Inc.Abstract We consider approximation problems for tensor product and additive random fields based on standard information in the average case setting. We also study the probabilistic setting of the mentioned problem for tensor products. The main question we are concerned with in this paper is "How much do we loose by considering standard information algorithms against those using general linear information?" For both types of the fields, the error of linear algorithms has been studied in great detail; however, the power of standard information was not addressed so far, which we do here. Our main result is that in most interesting cases there is no more than a logarithmic loss in approximation error when information is being restricted to the standard one. The results are obtained by randomization techniques.

U2 - 10.1016/j.jco.2015.05.002

DO - 10.1016/j.jco.2015.05.002

M3 - Article

SP - 659

EP - 674

JO - Journal of Complexity

JF - Journal of Complexity

SN - 0885-064X

IS - 5

ER -

ID: 4007771