Rigorous Convex
نویسندگان
چکیده
In order to generate valid convex lower bounding problems for noncon-vex twice{diierentiable optimization problems, a method that is based on second{ order information of general twice{diierentiable 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 non-linear example problems, it is shown that the lower bounds are suuciently tight to ensure satisfactory convergence of the BB, a branch and bound algorithm which relies on this underestimation procedure 3].
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