Any discontinuous PWA function is optimal solution to a parametric linear programming problem

Recent studies have investigated the continuous functions in terms of inverse optimality. The continuity is a primordial structural property which is exploited in order to link a given piecewise affine (PWA) function to an optimization problem. The aim of this work is to deepen the study of the PWA functions in the inverse optimality context and specifically deal with the presence of discontinuities. First, it will be shown that a solution to the inverse optimality problem exists via a constructive argument. The loss of continuity will have an implication on the structure of the optimization problem which, albeit convex, turns to have a set-valued optimal solution. As a consequence, the original PWA function will represent an optimal solution but the uniqueness is lost. From the numerical point of view, we introduce an algorithm to construct an optimization problem that admits a given discontinuous PWA function as an optimal solution. This construction is shown to rely on convex liftings. A numerical example is considered to illustrate the proposal.

[1]  John Lygeros,et al.  Every continuous piecewise affine function can be obtained by solving a parametric linear program , 2013, 2013 European Control Conference (ECC).

[2]  Tor Arne Johansen,et al.  Approximate explicit receding horizon control of constrained nonlinear systems , 2004, Autom..

[3]  Hoai-Nam Nguyen,et al.  Constrained Control of Uncertain, Time-Varying, Discrete-Time Systems: An Interpolation-Based Approach , 2013 .

[4]  Alexander Schrijver,et al.  Theory of linear and integer programming , 1986, Wiley-Interscience series in discrete mathematics and optimization.

[5]  Efstratios N. Pistikopoulos,et al.  Perspectives in Multiparametric Programming and Explicit Model Predictive Control , 2009 .

[6]  Miroslav Fikar,et al.  Clipping-Based Complexity Reduction in Explicit MPC , 2012, IEEE Transactions on Automatic Control.

[7]  Sorin Olaru,et al.  On the complexity of the convex liftings-based solution to inverse parametric convex programming problems , 2015, 2015 European Control Conference (ECC).

[8]  Ion Necoara,et al.  On the lifting problems and their connections with piecewise affine control law design , 2014, 2014 European Control Conference (ECC).

[9]  Ion Necoara,et al.  Inverse Parametric Convex Programming Problems Via Convex Liftings , 2014 .

[10]  Alberto Bemporad,et al.  The explicit linear quadratic regulator for constrained systems , 2003, Autom..

[11]  Miroslav Fikar,et al.  Complexity reduction of explicit model predictive control via separation , 2013, Autom..

[12]  Sorin Olaru,et al.  Inverse parametric linear/quadratic programming problem for continuous PWA functions defined on polyhedral partitions of polyhedra , 2015, 2015 54th IEEE Conference on Decision and Control (CDC).

[13]  Tor Arne Johansen,et al.  Explicit nonlinear model predictive control : theory and applications , 2012 .

[14]  T. Johansen On multi-parametric nonlinear programming and explicit nonlinear model predictive control , 2002, Proceedings of the 41st IEEE Conference on Decision and Control, 2002..

[15]  Sorin Olaru,et al.  Implications of Inverse Parametric Optimization in Model Predictive Control , 2015 .

[16]  Sorin Olaru,et al.  Recognition of additively weighted Voronoi diagrams and weighted Delaunay decompositions , 2015, 2015 European Control Conference (ECC).

[17]  Alberto Bemporad,et al.  An Algorithm for Approximate Multiparametric Convex Programming , 2006, Comput. Optim. Appl..

[18]  Michael C. Georgiadis,et al.  Multi-Parametric Programming , 2011 .

[19]  Didier Dumur,et al.  A parameterized polyhedra approach for explicit constrained predictive control , 2004, 2004 43rd IEEE Conference on Decision and Control (CDC) (IEEE Cat. No.04CH37601).

[20]  Graham C. Goodwin,et al.  Characterisation Of Receding Horizon Control For Constrained Linear Systems , 2003 .

[21]  Moritz Diehl,et al.  Every Continuous Nonlinear Control System Can be Obtained by Parametric Convex Programming , 2008, IEEE Transactions on Automatic Control.