Abstract
Piecewise linear-quadratic (PLQ) functions are an important class of functions in convex analysis since the result of most convex operators applied to a PLQ function is a PLQ function. We modify a recent algorithm for computing the convex (Legendre-Fenchel) conjugate of convex PLQ functions of two variables, to compute its partial conjugate i.e. the conjugate with respect to one of the variables. The structure of the original algorithm is preserved including its time complexity (linear time with some approximation and log-linear time without approximation). Applying twice the partial conjugate (and a variable switching operator) recovers the full conjugate. We present our partial conjugate algorithm, which is more flexible and simpler than the original full conjugate algorithm. We emphasize the difference with the full conjugate algorithm and illustrate results by computing partial conjugates, partial Moreau envelopes, and partial proximal averages.
Similar content being viewed by others
References
Alfeld, P., Schumaker, L.L.: Smooth macro-elements based on Powell-Sabin triangle splits. Adv. Comput. Math. 16, 29–46 (2002)
Bauschke, H.H., Goebel, R., Lucet, Y., Wang, X.: The proximal average: basic theory. SIAM J. Optim. 19, 768–785 (2008)
Bauschke, H.H., Lucet, Y., Trienis, M.: How to transform one convex function continuously into another. SIAM Rev. 50, 115–132 (2008)
Bauschke, H.H., Lucet, Y., Wang, X.: Primal-dual symmetric intrinsic methods for finding antiderivatives of cyclically monotone operators. SIAM J. Control Optim. 46, 2031–2051 (2007)
Bauschke, H.H., Moffat, S.M., Wang, X.: Self-dual smooth approximations of convex functions via the proximal average. In: Bauschke, H.H., Burachik, R.S., Combettes, P.L., Elser, V., Luke, D.R., Wolkowicz, H. (eds.) Fixed-Point Algorithms for Inverse Problems in Science and Engineering. Springer Optimization and Its Applications, vol. 49, pp. 23–32. Springer, New York (2011)
Bauschke, H.H., Wang, X., Yao, L.: Autoconjugate representers for linear monotone operators. Math. Program. 123, 5–24 (2010)
Brenier, Y.: Un algorithme rapide pour le calcul de transformées de Legendre–Fenchel discrètes. C. R. Acad. Sci. Paris, Sér. I, Math. 308, 587–589 (1989)
Cao, J., Li, X., Wang, G., Qin, H.: Surface reconstruction using bivariate simplex splines on Delaunay configurations. In: IEEE International Conference on Shape Modeling and Applications. Computers & Graphics-UK, Tsinghua Univ, Beijing, P.R. China, Jun. 26–28, 2009, vol. 33, pp. 341–350 (2009)
CGAL: Computational geometry algorithms library. http://www.cgal.org
CGLAB a Scilab toolbox for geometry based on CGAL. http://cglab.gforge.inria.fr/
Consortium, S.: Scilab (1994). http://www.scilab.org
Corrias, L.: Fast Legendre–Fenchel transform and applications to Hamilton–Jacobi equations and conservation laws. SIAM J. Numer. Anal. 33, 1534–1558 (1996)
Dæhlen, M., Lyche, T.: Bivariate interpolation with quadratic box splines. Math. Comput. 51, 219–230 (1988)
de Berg, M., van Kreveld, M., Overmars, M., Schwarzkopf, O.: Computational Geometry, 3rd edn. Algorithms and Applications. Springer, Berlin (2008)
Dierckx, P., Van Leemput, S., Vermeire, T.: Algorithms for surface fitting using Powell-Sabin splines. IMA J. Numer. Anal. 12, 271–299 (1992)
Gardiner, B., Lucet, Y.: Numerical computation of Fitzpatrick functions. J. Convex Anal. 16, 779–790 (2009)
Gardiner, B., Lucet, Y.: Convex hull algorithms for piecewise linear-quadratic functions in computational convex analysis. Set-Valued Var. Anal. 18, 467–482 (2010)
Gardiner, B., Lucet, Y.: Graph-matrix calculus for computational convex analysis. In: Bauschke, H.H., Burachik, R.S., Combettes, P.L., Elser, V., Luke, D.R., Wolkowicz, H. (eds.) Fixed-Point Algorithms for Inverse Problems in Science and Engineering. Springer Optimization and Its Applications, vol. 49, pp. 243–259. Springer, New York (2011)
Gardiner, B., Lucet, Y.: Computing the conjugate of convex piecewise linear-quadratic bivariate functions. Math. Program. 1–24 (2013)
Goebel, R.: Convexity, convergence and feedback in optimal control. PhD thesis, University of Washington, Seattle (2000)
Goebel, R.: Self-dual smoothing of convex and saddle functions. J. Convex Anal. 15, 179–190 (2008)
Hare, W.: A proximal average for nonconvex functions: a proximal stability perspective. SIAM J. Optim. 20, 650–666 (2009)
Hiriart-Urruty, J.-B., Lemaréchal, C.: Convex Analysis and Minimization Algorithms. Grundlehren der Mathematischen Wissenschaften [Fundamental Principles of Mathematical Sciences], vol. 305–306. Springer, Berlin (1993). Vol I: Fundamentals, Vol II: Advanced theory and bundle methods
Johnstone, J., Koch, V., Lucet, Y.: Convexity of the proximal average. J. Optim. Theory Appl. 148, 107–124 (2011)
Lai, M.-J., Schumaker, L.L.: Spline Functions on Triangulations. Encyclopedia of Mathematics and Its Applications, vol. 110. Cambridge University Press, Cambridge (2007)
Lucet, Y.: A fast computational algorithm for the Legendre–Fenchel transform. Comput. Optim. Appl. 6, 27–57 (1996)
Lucet, Y.: Computational convex analysis library, 1996–2011. https://people.ok.ubc.ca/ylucet/cca.html
Lucet, Y.: Faster than the fast Legendre transform, the linear-time Legendre transform. Numer. Algorithms 16, 171–185 (1997)
Lucet, Y.: Fast Moreau envelope computation I: numerical algorithms. Numer. Algorithms 43, 235–249 (2006)
Lucet, Y.: What shape is your conjugate? A survey of computational convex analysis and its applications. SIAM Rev. 52, 505–542 (2010)
Lucet, Y., Bauschke, H.H., Trienis, M.: The piecewise linear-quadratic model for computational convex analysis. Comput. Optim. Appl. 43, 95–118 (2009)
Manni, C., Sablonnière, P.: Quadratic spline quasi-interpolants on Powell-Sabin partitions. Adv. Comput. Math. 26, 283–304 (2007)
Moffat, S.M.: On the kernel average of n functions. Master’s thesis, Department of Mathematics, University of British Columbia (Dec 2009)
Noullez, A., Vergassola, M.: A fast Legendre transform algorithm and applications to the adhesion model. J. Sci. Comput. 9, 259–281 (1994)
Patrinos, P., Sarimveis, H.: Convex parametric piecewise quadratic optimization: theory, algorithms and control applications. Tech. rep., National Technical University of Athens, Greece (2010)
Patrinos, P., Sarimveis, H.: A new algorithm for solving convex parametric quadratic programs based on graphical derivatives of solution mappings. Automatica 46, 1405–1418 (2010)
Patrinos, P., Sarimveis, H.: Convex parametric piecewise quadratic optimization: theory and algorithms. Automatica 47, 1770–1777 (2011)
Penot, J.-P.: The relevance of convex analysis for the study of monotonicity. Nonlinear Anal. 58, 855–871 (2004)
Powell, M.J.D., Sabin, M.A.: Piecewise quadratic approximations on triangles. ACM Trans. Math. Softw. 3, 316–325 (1977)
Rockafellar, R.T.: Convex Analysis. Princeton University Press, Princeton (1970)
Rockafellar, R.T., Wets, R.J.-B.: Variational Analysis. Springer, Berlin (1998)
Sbibih, D., Serghini, A., Tijini, A.: Polar forms and quadratic spline quasi-interpolants on Powell-Sabin partitions. Appl. Numer. Math. 59, 938–958 (2009)
Sbibih, D., Serghini, A., Tijini, A.: Bivariate simplex spline quasi-interpolants. Numer. Math. 3, 97–118 (2010)
She, Z.-S., Aurell, E., Frisch, U.: The inviscid Burgers equation with initial data of Brownian type. Commun. Math. Phys. 148, 623–641 (1992)
Sorokina, T., Zeilfelder, F.: Optimal quasi-interpolation by quadratic C 1-splines on type-2 triangulations. In: Approximation Theory XI: Gatlinburg 2004, Mod. Methods Math., pp. 423–438. Nashboro Press, Brentwood (2005)
Speleers, H., Dierckx, P., Vandewalle, S.: Quasi-hierarchical Powell-Sabin B-splines. Comput. Aided Geom. Des. 26, 174–191 (2009)
Wiley, D.F., Childs, H.R., Hamann, B., Joy, K.I., Max, N.L.: Best quadratic spline approximation for hierarchical visualization. In: Proceedings of the Symposium on Data Visualisation 2002, VISSYM ’02, Aire-la-Ville. Eurographics Association, Switzerland, pp. 133–140 (2002)
Willemans, K., Dierckx, P.: Surface fitting using convex Powell-Sabin splines. J. Comput. Appl. Math. 56, 263–282 (1994)
Acknowledgements
Yves Lucet and Khan Jakee were partially supported by a Discovery grant from the Natural Sciences and Engineering Research Council of Canada (NSERC). Bryan Gardiner was partially supported by an NSERC Undergraduate Student Research Award. Part of the research was performed in the Computer-Aided Convex Analysis laboratory funded by the Canadian Foundation for Innovation.
Special thanks go to Rafal Goebel who pointed out the proof of Fact 3.3 in [20]. The authors are indebted to the referees for invaluable feedback that resulted in the inclusion of Fact 3.3, and greatly improved the presentation of the paper.
Author information
Authors and Affiliations
Corresponding author
Rights and permissions
About this article
Cite this article
Gardiner, B., Jakee, K. & Lucet, Y. Computing the partial conjugate of convex piecewise linear-quadratic bivariate functions. Comput Optim Appl 58, 249–272 (2014). https://doi.org/10.1007/s10589-013-9622-z
Received:
Published:
Issue Date:
DOI: https://doi.org/10.1007/s10589-013-9622-z