The decomposition-based outer approximation algorithm for convex mixed-integer nonlinear programming
The decomposition-based outer approximation algorithm for convex mixed-integer nonlinear programming
Abstract
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The decomposition-based outer approximation algorithm for convex mixed-integer nonlinear programming
No abstract recorded for this item.
In this paper we compare the performance of two codes for convex mixed integer nonlinear programs on a number of test problems The rst code uses a branch and bound algorithm The second code is a commer cially available implementation of an outer approximation algorithm The compar
Interior-point methods for nonlinear programming have been demonstrated to be quite efficient, especially for large scale problems, and, as such, they are ideal candidates for solving the nonlinear subproblems that arise in the solution of mixed-integer nonlinear programming prob
This paper is motivated by the fact that mixed integer nonlinear programming is an important and difficult area for which there is a need for developing new methods and software for solving large-scale problems. Moreover, both fundamental building blocks, namely mixed integer lin
This paper provides a survey of recent progress and software for solving convex mixed integer nonlinear programs (MINLP)s, where the objective and constraints are defined by convex functions and integrality restrictions are imposed on a subset of the decision variables. Convex MI
Introduction This paper is concerned with zero{one mixed integer nonlinear programming problems of the form (MINLP) min f(x; y) subject to g(x; y) 0 x 2 f0; 1g m y u y l Here x is a vector of m zero{one variables, y is a vector of n continuous variables, and u and l are vectors o
We propose a modified sequential quadratic programming (SQP) method for solving mixed-integer nonlinear programming problems. Under the assumption that integer variables have a smooth influence on the model functions, i.e., that function values do not change drastically when inor