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Closed convex function
Papers overview
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2019
2019
In this paper, we study accelerated Regularized Newton Methods for minimizing objectives formed as a sum of two functions: one is… Expand
2019
2019
For a symmetric positive semidefinite linear system of equations $$\mathcal{Q}{{\varvec{x}}}= {{\varvec{b}}}$$Qx=b, where… Expand
Highly Cited
2018
Highly Cited
2018
We consider a class of difference-of-convex (DC) optimization problems whose objective is level-bounded and is the sum of a… Expand
2016
2016
In this paper we study distributionally robust constraints on risk measures (such as standard deviation less the mean… Expand
Highly Cited
2015
Highly Cited
2015
We study accelerated mirror descent dynamics in continuous and discrete time. Combining the original continuous-time motivation… Expand
Review
2009
Review
2009
Computational convex analysis algorithms have been rediscovered several times in the past by researchers from different fields… Expand
Review
2005
Review
2005
Due to their axiomatic foundation and their favorable computational properties convex risk measures are becoming a powerful tool… Expand
2005
2005
In order to minimize a closed convex function that is approximated by a sequence of better behaved functions, we investigate the… Expand
1996
1996
Let X be a real Hilbert space endowed with inner product 〈., .〉 and associated norm ‖.‖, and let f be a proper closed convex… Expand
Highly Cited
1974
Highly Cited
1974
Kuhn-Tucker condition (0, 0) e dK(x, y) into a more explicit and familiar form. Writing where -k(y) = /0(x) + ylfl(x… Expand