mixed‐integer nonlinear programmingnonconvex feasible operating regionThis paper presents an optimization framework to determine the optimal operating points of combined heat and power (CHP) units with nonlinear, nonconvex feasible operating region (FOR). The mentioned problem is the economic dispatch (...
A Generalized Iterated Shrinkage Algorithm for Non-convex :一个广义的迭代收缩算法对非凸 热度: Mixed Integer Recourse Problems:混合整数追索权的问题 热度: Linear and Nonlinear Mixed-Effect Models - Penn State :线性和非线性混合效应模型-宾夕法尼亚州立大学 热度: 相关推荐 Non-ConvexMixed-...
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mixed-integer nonlinear programming Runhong Qi 1 , Michael A. Henson * Department of Chemical Engineering, Louisiana State Uni6ersity, Baton Rouge, LA 70803-7303, USA Received 4 April 1999; received in revised form 24 August 2000; accepted 24 August 2000 Abstract An optimal design strategy for...
The solverstoaMINLPis using a Single-search Tree Outer Approximation algorithm to solve Mixed-Integer NonLinear Programming (MINLP) problems. Handles both convex or nonconvex problems, but is best suited for solving convex problems. If the nonlinear subproblems are known to be convex, setting an in...
programming (SQP) stabilised by using trust regions. It can deal with both convex and nonconvex problems and problems with possibly expensive function evaluations. In addition, it is not assumed that the mixed integer problem has to be relaxable; the function evaluations are requested only at ...
Mixed integer programming
Xinglong Ju, Jay M. Rosenberger, Victoria CP Chen, and Feng Liu. "Global optimization using mixed integer quadratic programming on non-convex two-way interaction truncated linear multivariate adaptive regression splines." arXiv preprint arXiv:2006.15707 (2020). ...
We call (MOMIP) a multiobjective mixed-integer convex optimization problem if all involved functions \(f_i, i \in [p]\) and \(g_j, j \in [q]\) are convex. Otherwise, we call it a multiobjective mixed-integer nonconvex optimization problem. Obviously, for \(m=0\) and for \(n...
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 ...