Zobrazeno 1 - 10
of 11
pro vyhledávání: '"Loris Cannelli"'
Publikováno v:
Journal of Finance and Data Science, Vol 9, Iss , Pp 100101- (2023)
The construction of replication strategies for contingent claims in the presence of risk and market friction is a key problem of financial engineering. In real markets, continuous replication, such as in the model of Black, Scholes and Merton (BSM),
Externí odkaz:
https://doaj.org/article/fc8addca19e14fd1950fbb64a32b1026
Publikováno v:
IEEE Control Systems Letters. 7:1488-1493
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::0377fc37fe48ef8214d3bffbbde07f99
https://doi.org/10.1109/lcsys.2023.3270347
https://doi.org/10.1109/lcsys.2023.3270347
Autor:
Loris Cannelli, Gaurav N. Shetty, Leslie Ying, Gesualdo Scutari, Ukash Nakarmi, Konstantinos Slavakis
This paper introduces a non-parametric kernel-based modeling framework for imputation by regression on data that are assumed to lie close to an unknown-to-the-user smooth manifold in a Euclidean space. The proposed framework, coined kernel regression
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::8acfa99d5942111d2ea7d4d3c342fa8b
https://doi.org/10.36227/techrxiv.14813673.v2
https://doi.org/10.36227/techrxiv.14813673.v2
Dissertation/ Thesis
Autor:
Loris Cannelli (6933851)
The focus of this Dissertation is to provide a unified and efficient solution method for an important class of nonconvex, nonsmooth, constrained optimization problems. Specifically, we are interested in problems where the objective function can be wr
We consider convex and nonconvex constrained optimization with a partially separable objective function: agents minimize the sum of local objective functions, each of which is known only by the associated agent and depends on the variables of that ag
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::3f0570d5a5c08a723bb576f59fd53ca8
http://arxiv.org/abs/2010.09057
http://arxiv.org/abs/2010.09057
Publikováno v:
CAMSAP
We propose a novel algorithmic framework for the asynchronous and distributed optimization of multi-agent systems. We consider the constrained minimization of a nonconvex and nonsmooth partially separable sum-utility function, i.e., the cost function
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::11b5c0647191874b529d5c78151617e2
https://doi.org/10.1109/camsap.2017.8313161
https://doi.org/10.1109/camsap.2017.8313161
Publikováno v:
ICASSP
We propose a novel parallel asynchronous algorithmic framework for the minimization of the sum of a smooth (nonconvex) function and a convex (nonsmooth) regularizer. The framework hinges on Successive Convex Approximation (SCA) techniques and on a no
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::5f8c9e310dd6071e59e6394adcc0b6c2
https://doi.org/10.1109/icassp.2017.7953049
https://doi.org/10.1109/icassp.2017.7953049
Publikováno v:
SPAWC
We propose a novel parallel essentially cyclic asynchronous algorithm for the minimization of the sum of a smooth (nonconvex) function and a convex (nonsmooth) regularizer. The framework hinges on Successive Convex Approximation (SCA) techniques and
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::1ad6b4aaad0468f903bd83f32cefb7d3
http://hdl.handle.net/11573/1100979
http://hdl.handle.net/11573/1100979
Publikováno v:
ACSSC
We propose a novel parallel asynchronous lock-free algorithmic framework for the minimization of the sum of a smooth nonconvex function and a convex nonsmooth regularizer. This class of problems arises in many big-data applications, including deep le
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::d2d8edd48781317fe0ee8597fc641079
https://doi.org/10.1109/acssc.2016.7869520
https://doi.org/10.1109/acssc.2016.7869520
We propose a new asynchronous parallel block-descent algorithmic framework for the minimization of the sum of a smooth nonconvex function and a nonsmooth convex one, subject to both convex and nonconvex constraints. The proposed framework hinges on s
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::1914c6e2473c69649b6497fed6dcd18c