TY - GEN
T1 - Distributed ℓ0 Sparse Aggregative Optimization.
AU - Olama, Alireza
AU - Carnevale, Guido
AU - Notarstefano, Giuseppe
AU - Camponogara, Eduardo
N1 - Publisher Copyright:
© 2024 IEEE.
PY - 2024/10/23
Y1 - 2024/10/23
N2 - Sparse convex optimization involves optimization problems where the decision variables are constrained to have a certain number of entries equal to zero. In this paper, we focus on the sparse version of the so-called aggregative optimization scenario, i.e., on optimization problems in which the cost reads as the sum of local functions each depending on both a local decision variable and an aggregation of all of them. In this framework, we propose a novel fully-distributed scheme to address the problem over a network of cooperating agents. Specifically, by taking advantage of a suitable problem reformulation, we define an Augmented Lagrangian function. Then, we address such an Augmented Lagrangian by suitably interlacing the so-called Projected Aggregative Tracking distributed algorithm and the Block Coordinated Descent method giving rise to a novel fully-distributed scheme. The effectiveness of the proposed algorithm is corroborated via numerical simulations in problems arising in machine learning scenarios with both synthetic and real-world data sets.
AB - Sparse convex optimization involves optimization problems where the decision variables are constrained to have a certain number of entries equal to zero. In this paper, we focus on the sparse version of the so-called aggregative optimization scenario, i.e., on optimization problems in which the cost reads as the sum of local functions each depending on both a local decision variable and an aggregation of all of them. In this framework, we propose a novel fully-distributed scheme to address the problem over a network of cooperating agents. Specifically, by taking advantage of a suitable problem reformulation, we define an Augmented Lagrangian function. Then, we address such an Augmented Lagrangian by suitably interlacing the so-called Projected Aggregative Tracking distributed algorithm and the Block Coordinated Descent method giving rise to a novel fully-distributed scheme. The effectiveness of the proposed algorithm is corroborated via numerical simulations in problems arising in machine learning scenarios with both synthetic and real-world data sets.
UR - http://www.scopus.com/inward/record.url?scp=85208278885&partnerID=8YFLogxK
U2 - 10.1109/CASE59546.2024.10711465
DO - 10.1109/CASE59546.2024.10711465
M3 - Conference contribution
SN - 979-8-3503-5852-0
T3 - IEEE International Conference on Automation Science and Engineering
SP - 1747
EP - 1752
BT - 2024 IEEE 20th International Conference on Automation Science and Engineering, CASE 2024
PB - IEEE
T2 - IEEE International Conference on Automation Science and Engineering
Y2 - 28 August 2024
ER -