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On the pervasiveness of difference-convexity in optimization and statistics

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Abstract

With the increasing interest in applying the methodology of difference-of-convex (dc) optimization to diverse problems in engineering and statistics, this paper establishes the dc property of many functions in various areas of applications not previously known to be of this class. Motivated by a quadratic programming based recourse function in two-stage stochastic programming, we show that the (optimal) value function of a copositive (thus not necessarily convex) quadratic program is dc on the domain of finiteness of the program when the matrix in the objective function’s quadratic term and the constraint matrix are fixed. The proof of this result is based on a dc decomposition of a piecewise \(\hbox {LC}^1\) function (i.e., functions with Lipschitz gradients). Armed with these new results and known properties of dc functions existed in the literature, we show that many composite statistical functions in risk analysis, including the value-at-risk (VaR), conditional value-at-risk (CVaR), optimized certainty equivalent, and the expectation-based, VaR-based, and CVaR-based random deviation functionals are all dc. Adding the known class of dc surrogate sparsity functions that are employed as approximations of the \(\ell _0\) function in statistical learning, our work significantly expands the classes of dc functions and positions them for fruitful applications.

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Acknowledgements

The second author gratefully acknowledges the discussion with Professors Le Thi Hoi An and Pham Dinh Tao in the early stage of this work during his visit to the Université de Lorraine, Metz in June 2016. The three authors acknowledge their fruitful discussion with Professor Defeng Sun at the National University of Singapore during his visit to the University of Southern California. They are also grateful to Professor Marc Teboulle for drawing their attention to the references [5,6,7] that introduce and revisit the OCE. The constructive comments of two referees are also gratefully acknowledged. In particular, the authors are particularly grateful to a referee who has been very patient with our repeated misunderstanding of the work [57] that is now correctly summarized at the end of Sect. 3.1.

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  1. Daniel J. Epstein Department of Industrial and Systems Engineering, University of Southern California, Los Angeles, CA, 90089-0193, USA

    Maher Nouiehed, Jong-Shi Pang & Meisam Razaviyayn

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  1. Maher Nouiehed
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Correspondence to Meisam Razaviyayn.

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J.-S. Pang: This work of this author was based on research partially supported by the U.S. National Science Foundation Grants CMMI 1538605 and IIS-1632971.

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Nouiehed, M., Pang, JS. & Razaviyayn, M. On the pervasiveness of difference-convexity in optimization and statistics. Math. Program. 174, 195–222 (2019). https://doi.org/10.1007/s10107-018-1286-0

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