Computational geometry in C (2nd ed.)
Computational geometry in C (2nd ed.)
Analysis of Bounds for Multilinear Functions
Journal of Global Optimization
Global Optimization of Nonlinear Bilevel Programming Problems
Journal of Global Optimization
Semidefinite Relaxations of Fractional Programs via Novel Convexification Techniques
Journal of Global Optimization
Convex Envelopes of Monomials of Odd Degree
Journal of Global Optimization
Journal of Global Optimization
Convexification and Global Optimization in Continuous And
Convexification and Global Optimization in Continuous And
Trilinear Monomials with Mixed Sign Domains: Facets of the Convex and Concave Envelopes
Journal of Global Optimization
Computational Experience with a New Class of Convex Underestimators: Box-constrained NLP Problems
Journal of Global Optimization
Journal of Global Optimization
Deterministic Global Optimization: Theory, Methods and (NONCONVEX OPTIMIZATION AND ITS APPLICATIONS Volume 37) (Nonconvex Optimization and Its Applications)
Introduction to Global Optimization (Nonconvex Optimization and Its Applications)
Introduction to Global Optimization (Nonconvex Optimization and Its Applications)
Journal of Global Optimization
Journal of Global Optimization
A review of recent advances in global optimization
Journal of Global Optimization
Convex relaxation for solving posynomial programs
Journal of Global Optimization
On convex relaxations of quadrilinear terms
Journal of Global Optimization
Convergence rate of McCormick relaxations
Journal of Global Optimization
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A novel method for the convex underestimation of univariate functions is presented in this paper. The method is based on a piecewise application of the well-known 驴BB underestimator, which produces an overall underestimator that is piecewise convex. Subsequently, two algorithms are used to identify the linear segments needed for the construction of its $${{\mathcal C}^1}$$ -continuous convex envelope, which is itself a valid convex underestimator of the original function. The resulting convex underestimators are very tight, and their tightness benefits from finer partitioning of the initial domain. It is theoretically proven that there is always some finite level of partitioning for which the method yields the convex envelope of the function of interest. The method was applied on a set of univariate test functions previously presented in the literature, and the results indicate that the method produces convex underestimators of high quality in terms of both lower bound and tightness over the whole domain under consideration.