TY - GEN
T1 - On non-differentiable time-varying optimization
AU - Simonetto, Andrea
AU - Leus, Geert
N1 - Publisher Copyright:
© 2015 IEEE.
PY - 2015/1/1
Y1 - 2015/1/1
N2 - We consider non-differentiable convex optimization problems that vary continuously in time and we propose algorithms that sample these problems at specific time instances and generate a sequence of converging near-optimal decision variables. This sequence converges up to a bounded error to the solution trajectory of the time-varying non-differentiable problems. We illustrate through analytical examples and a realistic numerical simulation the benefit of the algorithms in signal processing applications, e.g., for reconstructing time-varying sparse signals.
AB - We consider non-differentiable convex optimization problems that vary continuously in time and we propose algorithms that sample these problems at specific time instances and generate a sequence of converging near-optimal decision variables. This sequence converges up to a bounded error to the solution trajectory of the time-varying non-differentiable problems. We illustrate through analytical examples and a realistic numerical simulation the benefit of the algorithms in signal processing applications, e.g., for reconstructing time-varying sparse signals.
U2 - 10.1109/CAMSAP.2015.7383847
DO - 10.1109/CAMSAP.2015.7383847
M3 - Conference contribution
AN - SCOPUS:84963864609
T3 - 2015 IEEE 6th International Workshop on Computational Advances in Multi-Sensor Adaptive Processing, CAMSAP 2015
SP - 505
EP - 508
BT - 2015 IEEE 6th International Workshop on Computational Advances in Multi-Sensor Adaptive Processing, CAMSAP 2015
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 6th IEEE International Workshop on Computational Advances in Multi-Sensor Adaptive Processing, CAMSAP 2015
Y2 - 13 December 2015 through 16 December 2015
ER -