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  4. Distributed primal outer approximation algorithm for sparse convex programming with separable structures
 
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2023
Journal Article
Title

Distributed primal outer approximation algorithm for sparse convex programming with separable structures

Abstract
This paper presents the distributed primal outer approximation (DiPOA) algorithm for solving sparse convex programming (SCP) problems with separable structures, efficiently, and in a decentralized manner. The DiPOA algorithm development consists of embedding the recently proposed relaxed hybrid alternating direction method of multipliers (RH-ADMM) algorithm into the outer approximation (OA) algorithm. We also propose two main improvements to control the quality and the number of cutting planes that approximate nonlinear functions. In particular, the RH-ADMM algorithm acts as a distributed numerical engine inside the DiPOA algorithm. DiPOA takes advantage of the multi-core architecture of modern processors to speed up optimization algorithms. The proposed distributed algorithm makes practical the solution of SCP in learning and control problems from the application side. This paper concludes with a performance analysis of DiPOA for the distributed sparse logistic regression and quadratically constrained optimization problems. Finally, the paper concludes with a numerical comparison with state-of-the-art optimization solvers.
Author(s)
Olama, Alireza
Camponogara, Eduardo
Costa Mendes, Paulo Renato da
Fraunhofer-Institut für Techno- und Wirtschaftsmathematik ITWM  
Journal
Journal of global optimization  
Open Access
DOI
10.1007/s10898-022-01266-5
Language
English
Fraunhofer-Institut für Techno- und Wirtschaftsmathematik ITWM  
Keyword(s)
  • Distributed optimization

  • Message passing interface

  • Outer approximation

  • Sparse convex programming

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