Zobrazeno 1 - 10
of 264
pro vyhledávání: '"Nemirovski, Arkadi"'
Publikováno v:
Open Journal of Mathematical Optimization, Vol 3, Iss , Pp 1-38 (2022)
We address the problems of computing operator norms of matrices induced by given norms on the argument and the image space. It is known that aside of a fistful of “solvable cases”, most notably, the case when both given norms are Euclidean, compu
Externí odkaz:
https://doaj.org/article/2461c5f817714b6c981e84db44dad4ba
It was recently shown [7, 9] that "properly built" linear and polyhedral estimates nearly attain minimax accuracy bounds in the problem of recovery of unknown signal from noisy observations of linear images of the signal when the signal set is an ell
Externí odkaz:
http://arxiv.org/abs/2312.14691
Our focus is on robust recovery algorithms in statistical linear inverse problem. We consider two recovery routines - the much studied linear estimate originating from Kuks and Olman [42] and polyhedral estimate introduced in [37]. It was shown in [3
Externí odkaz:
http://arxiv.org/abs/2309.06563
We introduce a new computational framework for estimating parameters in generalized generalized linear models (GGLM), a class of models that extends the popular generalized linear models (GLM) to account for dependencies among observations in spatio-
Externí odkaz:
http://arxiv.org/abs/2304.13793
Proper X-ray radiation design (via dynamic fluence field modulation, FFM) allows to reduce effective radiation dose in computed tomography without compromising image quality. It takes into account patient anatomy, radiation sensitivity of different o
Externí odkaz:
http://arxiv.org/abs/2301.03379
Autor:
Juditsky, Anatoli, Nemirovski, Arkadi
Polyhedral estimate is a generic efficiently computable nonlinear in observations routine for recovering unknown signal belonging to a given convex compact set from noisy observation of signal's linear image. Risk analysis and optimal design of polyh
Externí odkaz:
http://arxiv.org/abs/2212.12516
Autor:
Juditsky, Anatoli, Nemirovski, Arkadi
The subject of this paper is regularity-preserving aggregation of regular norms on finite-dimensional linear spaces. Regular norms were introduced in [5] and are closely related to ``type 2'' spaces [9, Chapter 9] playing important role in 1) high-di
Externí odkaz:
http://arxiv.org/abs/2210.16664
Publikováno v:
Open Journal of Mathematical Optimization Volume 3 (2022)
We address the problems of computing operator norms of matrices induced by given norms on the argument and the image space. It is known that aside of a fistful of "solvable cases," most notably, the case when both given norms are Euclidean, computing
Externí odkaz:
http://arxiv.org/abs/2110.04389
Autor:
Juditsky, Anatoli, Nemirovski, Arkadi
We discuss the approach to estimate aggregation and adaptive estimation based upon (nearly optimal) testing of convex hypotheses. We show that in the situation where the observations stem from {\em simple observation schemes} and where set of unknown
Externí odkaz:
http://arxiv.org/abs/2107.07836
Autor:
Juditsky, Anatoli, Nemirovski, Arkadi
For those acquainted with CVX (aka disciplined convex programming) of M. Grant and S. Boyd, the motivation of this work is the desire to extend the scope of CVX beyond convex minimization -- to convex-concave saddle point problems and variational ine
Externí odkaz:
http://arxiv.org/abs/2102.01002