A trust region SQP algorithm for mixed-integer nonlinear programming

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A trust region SQP algorithm for mixed-integer nonlinear programming
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 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 nee...
Oliver Exler, Klaus Schittkowski
Added 27 Dec 2010
Updated 27 Dec 2010
Type Journal
Year 2007
Where OL
Authors Oliver Exler, Klaus Schittkowski
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