2013/12/19 by Sina Khoshfetrat Pakazad, Pakazad, Sina Khoshfetrat, Anders Hansson +3
Computer Science · Engineering · Mathematics · #Advanced Optimization Algorithms Research #FOS: Mathematics #Matrix Theory and Algorithms #Optimization and Control (math.OC) #Sparse and Compressive Sensing Techniques #math.OC
paper · pdf · doi:10.48550/arxiv.1312.5440
Submitted to the 19th IFAC World Congress 2014
arxiv created 2013/12/19 · openalex publication_date 2013/12/19 · arxiv updated 2013/12/20 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28
In this paper, we put forth distributed algorithms for solving loosely coupled unconstrained and constrained optimization problems. Such problems are usually solved using algorithms that are based on a combination of decomposition and first order methods. These algorithms are commonly very slow and require many iterations to converge. In order to alleviate this issue, we propose algorithms that combine the Newton and interior-point methods with proximal splitting methods for solving such problems. Particularly, the algorithm for solving unconstrained loosely coupled problems, is based on Newton's method and utilizes proximal splitting to distribute the computations for calculating the Newton step at each iteration. A combination of this algorithm and the interior-point method is then used to introduce a distributed algorithm for solving constrained loosely coupled problems. We also provide guidelines on how to implement the proposed methods efficiently and briefly discuss the properties of the resulting solutions.