Zobrazeno 1 - 10
of 13
pro vyhledávání: '"Rafael Massambone"'
Publikováno v:
Biblioteca Digital de Teses e Dissertações da USPUniversidade de São PauloUSP.
In this doctoral thesis, we propose new iterative methods for solving a class of convex optimization problems. In general, we consider problems in which the objective function is composed of a finite sum of convex functions and the set of constraints
Publikováno v:
Inverse Problems, Volume 32, Number 11, 2016
We present a method for non-smooth convex minimization which is based on subgradient directions and string-averaging techniques. In this approach, the set of available data is split into sequences (strings) and a given iterate is processed independen
Externí odkaz:
http://arxiv.org/abs/1610.05823
Publikováno v:
Biblioteca Digital de Teses e Dissertações da UELUniversidade Estadual de LondrinaUEL.
Um modelo de memória associativa (AM, Associative Memory), dado por uma rede neural fuzzy em que os neurônios efetuam operações elementares da morfologia matemática (MM) e que é usado para o armazenamento e recordação de padrões fuzzy, é ch
Publikováno v:
Repositório Institucional da USP (Biblioteca Digital da Produção Intelectual)
Universidade de São Paulo (USP)
instacron:USP
Universidade de São Paulo (USP)
instacron:USP
The problem of covering a region of the plane with a fixed number of minimum-radius identical balls is studied in the present work. An explicit construction of bi-Lipschitz mappings is provided to model small perturbations of the union of balls. This
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::533d5852c8788fc9d3301702503dd94f
https://doi.org/10.1137/21m1426067
https://doi.org/10.1137/21m1426067
Publikováno v:
Repositório Institucional da USP (Biblioteca Digital da Produção Intelectual)
Universidade de São Paulo (USP)
instacron:USP
Universidade de São Paulo (USP)
instacron:USP
We investigate the problem of covering a region in the plane with the union of $m$ identical balls of minimum radius. The region to be covered may be disconnected, be nonconvex, have Lipschitz boun...
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::61ee8ee16d4173462992bdc9767f14a4
https://doi.org/10.1137/20m135950x
https://doi.org/10.1137/20m135950x
Publikováno v:
Repositório Institucional da USP (Biblioteca Digital da Produção Intelectual)
Universidade de São Paulo (USP)
instacron:USP
Universidade de São Paulo (USP)
instacron:USP
We present a method to solve constrained convex stochastic optimization problems when the objective is a finite sum of convex functions fi. Our method is based on Incremental Stochastic Subgradient...
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::eff6ac44f50b525475979f9d413a50b7
https://doi.org/10.1080/10556788.2018.1496432
https://doi.org/10.1080/10556788.2018.1496432
Publikováno v:
Biblioteca Digital de Teses e Dissertações da USP
Universidade de São Paulo (USP)
instacron:USP
Universidade de São Paulo (USP)
instacron:USP
In this doctoral thesis, we propose new iterative methods for solving a class of convex optimization problems. In general, we consider problems in which the objective function is composed of a finite sum of convex functions and the set of constraints
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::dabd55b39e14743d8b70fbc46cc31765
https://doi.org/10.11606/t.55.2017.tde-14112017-150512
https://doi.org/10.11606/t.55.2017.tde-14112017-150512
In this doctoral thesis, we propose new iterative methods for solving a class of convex optimization problems. In general, we consider problems in which the objective function is composed of a finite sum of convex functions and the set of constraints
Publikováno v:
Anais do 9. Congresso Brasileiro de Redes Neurais.
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::d4af1a9d58071a5ce9216a10b583dc2b
https://doi.org/10.21528/cbrn2009-003
https://doi.org/10.21528/cbrn2009-003
Publikováno v:
Repositório Institucional da USP (Biblioteca Digital da Produção Intelectual)
Universidade de São Paulo (USP)
instacron:USP
Universidade de São Paulo (USP)
instacron:USP
We present a method for non-smooth convex minimization which is based on subgradient directions and string-averaging techniques. In this approach, the set of available data is split into sequences (strings) and a given iterate is processed independen
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::94bdd6082d2a48b6f49c38f22ae57fdc