A trust region SQP algorithm for mixed-integer nonlinear programming |
| |
Authors: | Oliver Exler Klaus Schittkowski |
| |
Institution: | (1) Process Engineering Group, IIM-CSIC, Spanish Council for Scientific Research, C/Eduardo Cabello 6, 36208 Vigo, Spain;(2) Department of Computer Science, University of Bayreuth, 95440 Bayreuth, Germany |
| |
Abstract: | We propose a modified sequential quadratic programming 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 in- or decrementing an integer
value, successive quadratic approximations are applied. The algorithm is stabilized by a trust region method with Yuan’s second
order corrections. It is not assumed that the mixed-integer program is relaxable or, in other words, function values are evaluated
only at integer points. The Hessian of the Lagrangian function is approximated by a quasi-Newton update formula subject to
the continuous and integer variables. Numerical results are presented for a set of 80 mixed-integer test problems taken from
the literature. The surprising result is that the number of function evaluations, the most important performance criterion
in practice, is less than the number of function calls needed for solving the corresponding relaxed problem without integer
variables. |
| |
Keywords: | Mixed-integer nonlinear programming Sequential quadratic programming SQP Trust region methods |
本文献已被 SpringerLink 等数据库收录! |
|