**Reference:**

P. Giselsson,
M.D. Doan,
T. Keviczky,
B. De Schutter, and
A. Rantzer,
"A distributed optimization algorithm with convergence rate
O(1/k^{2}) for distributed model predictive control," Tech.
rep. 12-011, Delft Center for Systems and Control, Delft University of
Technology, Delft, The Netherlands, Mar. 2012.

**Abstract:**

We propose a distributed optimization algorithm for mixed
*L*_{1}/*L*_{2}-norm optimization based
on accelerated gradient methods using dual decomposition. The
algorithm achieves convergence rate O(1/k^{2}), where k is the
iteration number, which significantly improves the convergence rates
of existing duality-based distributed optimization algorithms that
achieve O(1/k). The performance of the developed algorithm is
evaluated on randomly generated optimization problems arising in
distributed Model Predictive Control (MPC). The evaluation shows that,
when the problem data is sparse and large-scale, our algorithm
outperforms state-of-the-art optimization software CPLEX and MOSEK.

Technical report: pdf file (168 KB)

@techreport{GisDoa:12-011,

author={P. Giselsson and M.D. Doan and T. Keviczky and B. {D}e Schutter and A. Rantzer},

title={A distributed optimization algorithm with convergence rate ${O}(\frac{1}{k^2})$ for distributed model predictive control},

number={12-011},

institution={Delft Center for Systems and Control, Delft University of Technology},

address={Delft, The Netherlands},

month=mar,

year={2012}

}

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