Fast and stable nonconvex constrained distributed optimization: the ELLADA algorithm

Distributed optimization using multiple computing agents in a localized and coordinated manner is a promising approach for solving large-scale optimization problems, e.g., those arising in model predictive control (MPC) of large-scale plants. However, a distributed optimization algorithm that is com...

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Vydáno v:Optimization and engineering Ročník 23; číslo 1; s. 259 - 301
Hlavní autoři: Tang, Wentao, Daoutidis, Prodromos
Médium: Journal Article
Jazyk:angličtina
Vydáno: New York Springer US 01.03.2022
Springer Nature B.V
Témata:
Algorithms
Benchmarks
Constraints
Control
Convergence
Engineering
Environmental Management
Financial Engineering
Mathematics
Mathematics and Statistics
Nonlinear programming
Operations Research/Decision Theory
Optimization
Predictive control
Research Article
Systems Theory
Model predictive control
Distributed optimization
Nonconvex optimization
Acceleration
ISSN:1389-4420, 1573-2924
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Shrnutí:Distributed optimization using multiple computing agents in a localized and coordinated manner is a promising approach for solving large-scale optimization problems, e.g., those arising in model predictive control (MPC) of large-scale plants. However, a distributed optimization algorithm that is computationally efficient, globally convergent, amenable to nonconvex constraints remains an open problem. In this paper, we combine three important modifications to the classical alternating direction method of multipliers for distributed optimization. Specifically, (1) an extra-layer architecture is adopted to accommodate nonconvexity and handle inequality constraints, (2) equality-constrained nonlinear programming (NLP) problems are allowed to be solved approximately, and (3) a modified Anderson acceleration is employed for reducing the number of iterations. Theoretical convergence of the proposed algorithm, named ELLADA, is established and its numerical performance is demonstrated on a large-scale NLP benchmark problem. Its application to distributed nonlinear MPC is also described and illustrated through a benchmark process system.
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ISSN:1389-4420
1573-2924
DOI:10.1007/s11081-020-09585-w