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Computational convex analysis using parametric quadratic programming

University of British Columbia

The class of piecewise linear-quadratic (PLQ) functions is a very important class of functions in convex analysis since the result of most convex operators applied to a PLQ function is a PLQ function. Although there exists a wide range of algorithms for univariate PLQ functions, recent work has focused on extending these algorithms to PLQ functions with more than one variable. First, we recall a proof in [Convexity, Convergence and Feedback in Optimal Control, Phd thesis, R. Goebel, 2000] that PLQ functions are closed under partial conjugate computation. Then we use recent results on parametric quadratic programming (pQP) to compute the inf-projection of any multivariate convex PLQ function. We implemented the algorithm for bivariate PLQ functions, and modi ed it to compute conjugates. We provide a complete space and time worst-case complexity analysis and show that for bivariate functions, the algorithm matches the performance of [Computing the Conjugate of Convex Piecewise Linear-Quadratic Bivariate Functions, Bryan Gardiner and Yves Lucet, Mathematical Programming Series B, 2011] while being easier to extend to higher dimensions.

Text

2013-10-01

Attribution-NonCommercial-NoDerivatives 4.0 International

http://hdl.handle.net/2429/45182

Interdisciplinary Studies

University of British Columbia

UBCO

http://creativecommons.org/licenses/by-nc-nd/4.0/