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Computing the partial conjugate of convex piecewise linear-quadratic bivariate functions

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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.

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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.

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  1. Computer Science, I. K. Barber School, University of British Columbia Okanagan, Kelowna, British Columbia, V1V 1V7, Canada

    Bryan Gardiner, Khan Jakee & Yves Lucet

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  1. Bryan Gardiner
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Correspondence to Yves Lucet.

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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

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