Zobrazeno 1 - 10
of 54
pro vyhledávání: '"Pougkakiotis, Spyridon"'
We develop an efficient data-driven and model-free unsupervised learning algorithm for achieving fully passive intelligent reflective surface (IRS)-assisted optimal short/long-term beamforming in wireless communication networks. The proposed algorith
Externí odkaz:
http://arxiv.org/abs/2410.24154
In this paper we present an efficient active-set method for the solution of convex quadratic programming problems with general piecewise-linear terms in the objective, with applications to sparse approximations and risk-minimization. The algorithm is
Externí odkaz:
http://arxiv.org/abs/2405.04172
We establish strong duality relations for functional two-step compositional risk-constrained learning problems with multiple nonconvex loss functions and/or learning constraints, regardless of nonconvexity and under a minimal set of technical assumpt
Externí odkaz:
http://arxiv.org/abs/2312.01110
Electronically tunable metasurfaces, or Intelligent Reflective Surfaces (IRSs), are a popular technology for achieving high spectral efficiency in modern wireless systems by shaping channels using a multitude of tunable passive reflective elements. C
Externí odkaz:
http://arxiv.org/abs/2304.11464
In this paper we present an efficient active-set method for the solution of convex quadratic programming problems with general piecewise-linear terms in the objective, with applications to sparse approximations and risk-minimization. The method explo
Externí odkaz:
http://arxiv.org/abs/2302.14497
Although Intelligent Reflective Surfaces (IRSs) are a cost-effective technology promising high spectral efficiency in future wireless networks, obtaining optimal IRS beamformers is a challenging problem with several practical limitations. Assuming fu
Externí odkaz:
http://arxiv.org/abs/2210.16712
We show that risk-constrained functional optimization problems with general integrable nonconvex instantaneous reward/constraint functions exhibit strong duality, regardless of nonconvexity. We consider risk constraints featuring convex and positivel
Externí odkaz:
http://arxiv.org/abs/2206.11948
In this paper we analyze a zeroth-order proximal stochastic gradient method suitable for the minimization of weakly convex stochastic optimization problems. We consider nonsmooth and nonlinear stochastic composite problems, for which (sub-)gradient i
Externí odkaz:
http://arxiv.org/abs/2205.01633
In this paper we present an active-set method for the solution of $\ell_1$-regularized convex quadratic optimization problems. It is derived by combining a proximal method of multipliers (PMM) strategy with a standard semismooth Newton method (SSN).
Externí odkaz:
http://arxiv.org/abs/2201.10211
In this paper we present general-purpose preconditioners for regularized augmented systems arising from optimization problems, and their corresponding normal equations. We discuss positive definite preconditioners, suitable for CG and MINRES. We cons
Externí odkaz:
http://arxiv.org/abs/2107.06822