MISQP solves mixed-integer nonlinear programming problems by a modified sequential quadratic programming (SQP) method. 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 variable, successive quadratic approximations are applied. It is not assumed that integer variables are relaxable, i.e., problem functions are evaluated only at integer points. The code is applicable also to nonconvex optimization problems.
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References in zbMATH (referenced in 9 articles , 1 standard article )
Showing results 1 to 9 of 9.
- Exler, Oliver; Lehmann, Thomas; Schittkowski, Klaus: A comparative study of SQP-type algorithms for nonlinear and nonconvex mixed-integer optimization (2012)
- Gill, Philip E.; Wong, Elizabeth: Sequential quadratic programming methods (2012)
- Pakdaman, Morteza: A note on “A new local and global optimization method for mixed integer quadratic programming problems” by G.Q. Li et al. (2011)
- Zhu, Wenxing; Lin, Geng: A dynamic convexized method for nonconvex mixed integer nonlinear programming (2011)
- Schlüter, Martin; Gerdts, Matthias: The oracle penalty method (2010)
- Schlüter, Martin; Egea, Jose A.; Banga, Julio R.: Extended ant colony optimization for non-convex mixed integer nonlinear programming (2009)
- Dai, Yu-Hong; Schittkowski, Klaus: A sequential quadratic programming algorithm with non-monotone line search (2008)
- Holmström, Kenneth; Quttineh, Nils-Hassan; Edvall, Marcus M.: An adaptive radial basis algorithm (ARBF) for expensive black-box mixed-integer constrained global optimization (2008)
- Exler, Oliver; Schittkowski, Klaus: A trust region SQP algorithm for mixed-integer nonlinear programming (2007)