Zobrazeno 1 - 10
of 15
pro vyhledávání: '"Angelia Nedich"'
Publikováno v:
ISIT
The problem of estimating the support of a distribution is of great importance in many areas of machine learning, computer science, physics and biology. Most of the existing work in this domain has focused on settings that assume perfectly accurate s
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::698e39791e386ca2e23db4cfedbdae6e
https://doi.org/10.1109/isit45174.2021.9517892
https://doi.org/10.1109/isit45174.2021.9517892
Publikováno v:
UPCommons. Portal del coneixement obert de la UPC
Universitat Politècnica de Catalunya (UPC)
IEEE Transactions on Automatic Control, 67(6)
Universitat Politècnica de Catalunya (UPC)
IEEE Transactions on Automatic Control, 67(6)
A multi-agent optimization problem motivated by the management of energy systems is discussed. The associated cost function is separable and convex although not necessarily strongly convex and there exist edge-based coupling equality constraints. In
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::d29a9e94774e999d561661ba45accdef
http://hdl.handle.net/2117/362457
http://hdl.handle.net/2117/362457
Autor:
Angelia Nedich, A. Uribe C´Esar
Publikováno v:
ИНТЕЛЛЕКТУАЛИЗАЦИЯ ОБРАБОТКИ ИНФОРМАЦИИ ISBN: 9785945882362
ИНТЕЛЛЕКТУАЛИЗАЦИЯ ОБРАБОТКИ ИНФОРМАЦИИ
ИНТЕЛЛЕКТУАЛИЗАЦИЯ ОБРАБОТКИ ИНФОРМАЦИИ
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::296c07c24332471f7907373a8ed25878
https://doi.org/10.30826/idp201833
https://doi.org/10.30826/idp201833
Autor:
Angelia Nedich, Tatiana Tatarenko
In this work, we consider a constrained convex problem with linear inequalities and provide an inexact penalty re-formulation of the problem. The novelty is in the choice of the penalty functions, which are smooth and can induce a non-zero penalty ov
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::e8d379e88d426ad23d5caeeb915c0ab6
http://arxiv.org/abs/1808.07749
http://arxiv.org/abs/1808.07749
Autor:
Angelia Nedich
Publikováno v:
Foundations and Trends® in Systems and Control. 2:1-100
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::83509eb3a5725bd5aa5ad7c468352117
https://doi.org/10.1561/2600000004
https://doi.org/10.1561/2600000004
Publikováno v:
CDC
This paper considers the problem of distributed optimization over time-varying undirected graphs. We discuss a distributed algorithm, which we call DIGing, for solving this problem based on a combination of an inexact gradient method and a gradient t
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::60a8c772977b05a5c8da0548fdef298c
https://doi.org/10.1109/cdc.2016.7798402
https://doi.org/10.1109/cdc.2016.7798402
Autor:
Angelia Nedich, Minh Do
Publikováno v:
2015 53rd Annual Allerton Conference on Communication, Control, and Computing (Allerton).
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::44ae9454893f7671c8a010d66e748457
https://doi.org/10.1109/allerton.2015.7446975
https://doi.org/10.1109/allerton.2015.7446975
Autor:
Ion Necoara, Angelia Nedich
Publikováno v:
ECC
In this paper we propose a distributed dual gradient algorithm for minimizing linearly constrained separable convex problems and analyze its rate of convergence. In particular, we show that under the assumption that the Hessian of the primal objectiv
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::571e0514dde13511acd44e1948541aed
https://doi.org/10.1109/ecc.2015.7330561
https://doi.org/10.1109/ecc.2015.7330561
Autor:
Asuman Ozdaglar, Angelia Nedich
Publikováno v:
Journal of Global Optimization. 40:545-573
We provide a unifying geometric framework for the analysis of general classes of duality schemes and penalty methods for nonconvex constrained optimization problems. We present a separation result for nonconvex sets via general concave surfaces. We u
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::dc8c8342363eac172ebff6e255c6b49e
https://doi.org/10.1007/s10898-006-9122-0
https://doi.org/10.1007/s10898-006-9122-0
Publikováno v:
Computational Mathematics and Modeling. 8:85-94
A continuous regularization method based on the proximal method is proposed for minimization problems with an inexact objective function. Sufficient convergence conditions are given, and the regularizing operator is constructed.
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::1efe72fa918063d0707a19a1af63f85e
https://doi.org/10.1007/bf02405157
https://doi.org/10.1007/bf02405157