References of "Pesavento, Marius"
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See detailInexact Block Coordinate Descent Algorithms for Nonsmooth Nonconvex Optimization
Yang, Yang UL; Pesavento, Marius; Luo, Zhi-Quan et al

E-print/Working paper (2019)

In this paper, we propose an inexact block coordinate descent algorithm for large-scale nonsmooth nonconvex optimization problems. At each iteration, a particular block variable is selected and updated by ... [more ▼]

In this paper, we propose an inexact block coordinate descent algorithm for large-scale nonsmooth nonconvex optimization problems. At each iteration, a particular block variable is selected and updated by solving the original optimization problem with respect to that block variable inexactly. More precisely, a local approximation of the original optimization problem is solved. The proposed algorithm has several attractive features, namely, i) high flexibility, as the approximation function only needs to be strictly convex and it does not have to be a global upper bound of the original function; ii) fast convergence, as the approximation function can be designed to exploit the problem structure at hand and the stepsize is calculated by the line search; iii) low complexity, as the approximation subproblems are much easier to solve and the line search scheme is carried out over a properly constructed differentiable function; iv) guaranteed convergence to a stationary point, even when the objective function does not have a Lipschitz continuous gradient. Interestingly, when the approximation subproblem is solved by a descent algorithm, convergence to a stationary point is still guaranteed even if the approximation subproblem is solved inexactly by terminating the descent algorithm after a finite number of iterations. These features make the proposed algorithm suitable for large-scale problems where the dimension exceeds the memory and/or the processing capability of the existing hardware. These features are also illustrated by several applications in signal processing and machine learning, for instance, network anomaly detection and phase retrieval. [less ▲]

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See detailParallel coordinate descent algorithms for sparse phase retrieval
Yang, Yang UL; Pesavento, Marius; Eldar, Yonina C. et al

in Proc. 2019 IEEE International Conference on Acoustics, Speech and Signal (ICASSP) (2019, May)

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See detailEnergy efficiency optimization in MIMO interference channels: A successive pseudoconvex approximation approach
Yang, Yang UL; Pesavento, Marius; Chatzinotas, Symeon UL et al

in IEEE Transactions on Signal Processing (2019)

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See detailSuccessive convex approximation algorithms for sparse signal estimation with nonconvex regularizations
Yang, Yang UL; Pesavento, Marius; Chatzinotas, Symeon UL et al

in IEEE Journal of Selected Topics in Signal Processing (2018), 12(6), 1286-1302

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See detailParallel and hybrid soft-thresholding algorithms with line search for sparse nonlinear regression
Yang, Yang UL; Pesavento, Marius; Chatzinotas, Symeon UL et al

in Proc. 26th European Signal Processing Conference (2018, September)

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See detailSuccessive convex approximation algorithms for sparse signal estimation with nonconvex regularizations
Yang, Yang UL; Pesavento, Marius; Chatzinotas, Symeon UL et al

in Proc. The 10th IEEE Sensor Array and Multichannel Signal Processing Workshop (2018, July)

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See detailEnergy efficiency in MIMO interference channels: Social optimality and max-min fairness
Yang, Yang UL; Pesavento, Marius

in Proc. 2018 IEEE International Conference on Acoustics, Speech and Signal (2018, April)

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See detailA parallel best-response algorithm with exact line search for nonconvex sparsity-regularized rank minimization
Yang, Yang UL; Pesavento, Marius

in Proc. 2018 IEEE International Conference on Acoustics, Speech and Signal (2018, April)

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See detailEnergy efficient transmission in MIMO interference channels with QoS constraints
Yang, Yang UL; Pesavento, Marius

in Proc. 8th IEEE International Workshop on Computational Advances in Multi-Sensor Adaptive Processing (2017, December)

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See detailA unified successive pseudoconvex approximation framework
Yang, Yang UL; Pesavento, Marius

in IEEE Transactions on Signal Processing (2017), 65(13), 3313-3328

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See detailA parallel algorithm for energy efficiency maximization in massive MIMO networks
Yang, Yang UL; Pesavento, Marius

in Proc. 2016 IEEE Global Communications Conference (2016, December)

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See detailMultidimensional sparse recovery for MIMO channel parameter estimation
Steffens, Christian; Yang, Yang UL; Pesavento, Marius

in Proc. 24th European Signal Processing Conference (2016, August)

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See detailA parallel decomposition method for nonconvex stochastic multi-agent optimization problems
Yang, Yang UL; Scutari, Gesualdo; Palomar, Daniel et al

in IEEE Transactions on Signal Processing (2016), 64(11), 2949-2964

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See detailParallel low-complexity $M$-PSK Detector for large-scale MIMO systems
Hedge, Ganapati; Yang, Yang UL; Steffens, Christian et al

in Proc. 2016 IEEE Sensor Array and Multichannel Signal Processing Workshop (2016)

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See detailA novel iterative convex approximation method
Yang, Yang UL; Pesavento, Marius

in Proc. IEEE 6th International Workshop on Computational Advances in Multi-Sensor Adaptive Processing (2015, August)

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See detailA novel line search method for nonsmooth optimization problems
Yang, Yang UL; Pesavento, Marius

in Proc. 23rd European Signal Processing Conference (2015, August)

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See detailAn online parallel algorithm for spectrum sensing in cognitive radio networks
Yang, Yang UL; Zhang, Mengyi; Pesavento, Marius et al

in Proc. 48th Asilomar Conference on Signals, Systems and Computers (2014, November)

Detailed reference viewed: 23 (0 UL)