アブストラクト
| Date | Oct 13, 2017 |
| Speaker | Dr. Stefan Vigerske, GAMS, Zuse Institute Berlin |
| Title | MINLP Solver Technology |
| Abstract |
To solve mixed-integer nonlinear optimization problems (MINLP) to global optimality, a rich set of techniques is applied to find optimal solutions and prove their global optimality. Current state-of-the-art solvers for this problem type are based on spatial branch-and-bound, where the bound is computed from a linear or mixed-integer linear relaxation of the problem and branching may be applied to both integer and continuous variables. To construct the relaxation, the algebraic structure of the nonlinear functions that define the objective and constraints is analyzed.
In this talk, we give a short overview on the algorithmic techniques that are employed in state-of-the-art global solvers for mixed-integer nonlinear optimization problems, in particular convexification and bound tightening approaches and primal heuristics. |