Splitting methods in control

The need for optimal control of processes under a restricted amount of resources renders first order optimization methods a viable option. Although computationally cheap, these methods typically suffer from slow convergence rates. In this work we discuss the family of first order methods known as decomposition schemes. We present three popular methods from this family, draw the connections between them and report all existing results that enable acceleration in terms of the convergence rate. The approach for splitting a problem into simpler ones so that the accelerated variants can be applied is also discussed and demonstrated via an example.

[1]  Bingsheng He,et al.  On the O(1/n) Convergence Rate of the Douglas-Rachford Alternating Direction Method , 2012, SIAM J. Numer. Anal..

[2]  Stephen P. Boyd,et al.  A Splitting Method for Optimal Control , 2013, IEEE Transactions on Control Systems Technology.

[3]  Wotao Yin,et al.  On the Global and Linear Convergence of the Generalized Alternating Direction Method of Multipliers , 2016, J. Sci. Comput..

[4]  H. H. Rachford,et al.  On the numerical solution of heat conduction problems in two and three space variables , 1956 .

[5]  B. Mercier,et al.  A dual algorithm for the solution of nonlinear variational problems via finite element approximation , 1976 .

[6]  Manfred Morari,et al.  Efficient interior point methods for multistage problems arising in receding horizon control , 2012, 2012 IEEE 51st IEEE Conference on Decision and Control (CDC).

[7]  T. Chan,et al.  Primal dual algorithms for convex models and applications to image restoration, registration and nonlocal inpainting , 2010 .

[8]  Richard G. Baraniuk,et al.  Fast Alternating Direction Optimization Methods , 2014, SIAM J. Imaging Sci..

[9]  Stanley Osher,et al.  A Unified Primal-Dual Algorithm Framework Based on Bregman Iteration , 2010, J. Sci. Comput..

[10]  H. J. Ferreau,et al.  An online active set strategy to overcome the limitations of explicit MPC , 2008 .

[11]  B. V. Dean,et al.  Studies in Linear and Non-Linear Programming. , 1959 .

[12]  Antonin Chambolle,et al.  A First-Order Primal-Dual Algorithm for Convex Problems with Applications to Imaging , 2011, Journal of Mathematical Imaging and Vision.

[13]  Stephen P. Boyd,et al.  Convex Optimization , 2004, Algorithms and Theory of Computation Handbook.

[14]  Marc Teboulle,et al.  A fast dual proximal gradient algorithm for convex minimization and applications , 2014, Oper. Res. Lett..

[15]  Stephen P. Boyd,et al.  CVXGEN: a code generator for embedded convex optimization , 2011, Optimization and Engineering.

[16]  Heinz H. Bauschke,et al.  Convex Analysis and Monotone Operator Theory in Hilbert Spaces , 2011, CMS Books in Mathematics.

[17]  Stephen P. Boyd,et al.  Distributed Optimization and Statistical Learning via the Alternating Direction Method of Multipliers , 2011, Found. Trends Mach. Learn..

[18]  P. Tseng Applications of splitting algorithm to decomposition in convex programming and variational inequalities , 1991 .

[19]  R. Tyrrell Rockafellar,et al.  Augmented Lagrangians and Applications of the Proximal Point Algorithm in Convex Programming , 1976, Math. Oper. Res..

[20]  Marc Teboulle,et al.  A Fast Iterative Shrinkage-Thresholding Algorithm for Linear Inverse Problems , 2009, SIAM J. Imaging Sci..

[21]  Y. Nesterov A method for solving the convex programming problem with convergence rate O(1/k^2) , 1983 .

[22]  Emmanuel J. Candès,et al.  Adaptive Restart for Accelerated Gradient Schemes , 2012, Foundations of Computational Mathematics.

[23]  L. Popov A modification of the Arrow-Hurwicz method for search of saddle points , 1980 .

[24]  Eric C. Kerrigan,et al.  Predictive Control Using an FPGA With Application to Aircraft Control , 2014, IEEE Transactions on Control Systems Technology.

[25]  Euhanna Ghadimi,et al.  Optimal Parameter Selection for the Alternating Direction Method of Multipliers (ADMM): Quadratic Problems , 2013, IEEE Transactions on Automatic Control.

[26]  M. Hestenes Multiplier and gradient methods , 1969 .

[27]  Marc Teboulle,et al.  Rate of Convergence Analysis of Decomposition Methods Based on the Proximal Method of Multipliers for Convex Minimization , 2014, SIAM J. Optim..

[28]  Patrick L. Combettes,et al.  Proximal Splitting Methods in Signal Processing , 2009, Fixed-Point Algorithms for Inverse Problems in Science and Engineering.