2019/03/20 by Matthew Ubl, Matthew Hale, Ubl, Matthew +1
Computer Science · Engineering · #Advanced Control Systems Optimization #Distributed Control Multi-Agent Systems #FOS: Mathematics #Optimization and Control (math.OC) #Optimization and Search Problems
paper · pdf · doi:10.48550/arxiv.1903.08618
openalex publication_date 2019/03/20 · openalex created_date 2022/07/29 · openalex updated_date 2026/07/28
Quadratic programs arise in robotics, communications, smart grids, and many\nother applications. As these problems grow in size, finding solutions becomes\nmuch more computationally demanding, and new algorithms are needed to\nefficiently solve them. Targeting large-scale problems, we develop a\nmulti-agent quadratic programming framework in which each agent updates only a\nsmall number of the total decision variables in a problem. Agents communicate\ntheir updated values to each other, though we do not impose any restrictions on\nthe timing with which they do so, nor on the delays in these transmissions.\nFurthermore, we allow weak parametric coupling among agents, in the sense that\nthey are free to independently choose their stepsizes, subject to mild\nrestrictions. We show that these stepsize restrictions depend upon a problem's\ncondition number. We further provide the means for agents to independently\nregularize the problem they solve, thereby improving condition numbers and, as\nwe will show, convergence properties, while preserving agents' independence in\nselecting parameters. Simulation results are provided to demonstrate the\nsuccess of this framework on a practical quadratic program.\n