(Enter summary)
Abstract: . In order to generate valid convex lower bounding problems for nonconvex
twice--differentiable optimization problems, a method that is based on second--
order information of general twice--differentiable functions is presented. Using interval
Hessian matrices, valid lower bounds on the eigenvalues of such functions are
obtained and used in constructing convex underestimators. By solving several nonlinear
example problems, it is shown that the lower bounds are sufficiently tight to
ensure... (Update)
Context of citations to this paper: More
...expressions with the addition of separable quadratic functions, which aim to overpower the nonconvexities. In Adjiman and Floudas [1] convex lower bound functions for general twice differentiable functions are based on second order information. Using so called interval...
.... global optimisation problems and nonlinear systems which are de ned by arbitrary arithmetical expressions, see Adjiman and Floudas [1] and Androulakis, Maranas, and Floudas [3] There convex lower bound functions are constructed by augmenting the nonconvex expressions...
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BibTeX entry: (Update)
C. S. Adjiman and C. A. Floudas, Rigorous convex underestimators for general twice--differentiable problems, J. Glob. Opt. 9 (1996), 23--40. http://citeseer.ist.psu.edu/146850.html More
@misc{ adjiman96rigorous,
author = "C. Adjiman and C. Floudas",
title = "Rigorous convex underestimators for general twice--differentiable problems",
text = "C. S. Adjiman and C. A. Floudas, Rigorous convex underestimators for general
twice--differentiable problems, J. Glob. Opt. 9 (1996), 23--40.",
year = "1996",
url = "citeseer.ist.psu.edu/146850.html" }
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