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On Brondsted-Rockafellar's Theorem for convex lower semicontinuous epi pointed functions in locally convex spaces
(Springer, 2018)
In this work we give an extension of the Brondsted-Rockafellar Theorem, and some of its important consequences, to proper convex lower-semicontinuous epi-pointed functions defined in locally convex spaces. We use a new ...
Subdifferential of the Supremum via Compactification of the Index Set
(Springer, 2020)
We give new characterizations for the subdifferential of the supremum of an arbitrary family of convex functions, dropping out the standard assumptions of compactness of the index set and upper semi-continuity of the ...
Subdifferential of the supremum function: moving back and forth between continuous and non-continuous settings
(Springer, 2020)
In this paper we establish general formulas for the subdifferential of the pointwise supremum of convex functions, which cover and unify both the compact continuous and the non-compact non-continuous settings. From the ...
Approximating optimization problems over convex functions
(Springer, 2008-11)
Many problems of theoretical and practical interest involve finding an optimum over a family of convex functions. For instance, finding the projection on the convex functions in $H^k(Omega)$, and some problems in economics. ...
Planning of Reserve Branches to Increase Reconfiguration Capability in Distribution Systems: A Scenario-Based Convex Programming Approach
(2021-01-01)
Distribution networks are usually designed with a fixed tree-shape topology, which limits its adapting capability against failures and excessive variation of load/generation. Reconfiguration is an important tool to optimize ...
Lagrangian penalization scheme with parallel forward-backward splitting
(Springer, 2018-05)
We propose a new iterative algorithm for the numerical approximation of the solutions to convex optimization problems and constrained variational inequalities, especially when the functions and operators involved have a ...
On variance reduction for stochastic smooth convex optimization with multiplicative noise
(Springer Verlag, 2019)
© 2018, Springer-Verlag GmbH Germany, part of Springer Nature and Mathematical Optimization Society. We propose dynamic sampled stochastic approximation (SA) methods for stochastic optimization with a heavy-tailed distribution ...
Efficient Day-Ahead Dispatch of Photovoltaic Sources in Monopolar DC Networks via an Iterative Convex Approximation
(Cartagena de Indias, 2023)
The objective of this research is to propose an efficient energy management system for photovoltaic (PV) generation units connected to monopolar DC distribution networks via convex optimization while considering a day-ahead ...