An Algorithmic Framework for MINLP with Separable Non-Convexity

DOI10.1007/978-1-4614-1927-3_11zbMath1242.90124OpenAlexW48802902MaRDI QIDQ2897302

Jon Lee, Claudia D'Ambrosio, Andreas Wächter

Publication date: 10 July 2012

Published in: Mixed Integer Nonlinear Programming (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1007/978-1-4614-1927-3_11

zbMATH Keywords

global optimizationseparablemixed-integer nonlinear programmingnon-convexspatial branch-and-bound

Mathematics Subject Classification ID

Mixed integer programming (90C11) Nonconvex programming, global optimization (90C26) Nonlinear programming (90C30)

Related Items

Sequence of polyhedral relaxations for nonlinear univariate functions, A reformulation technique to solve polynomial optimization problems with separable objective functions of bounded integer variables, On global optimization with indefinite quadratics, Valid inequalities for separable concave constraints with indicator variables, Strengthening the sequential convex MINLP technique by perspective reformulations

Uses Software

• LaGO
• Bonmin
• Ipopt

Cites Work

• Unnamed Item
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• Solving mixed integer nonlinear programs by outer approximation
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• Branching and bounds tighteningtechniques for non-convex MINLP
• A Global-Optimization Algorithm for Mixed-Integer Nonlinear Programs Having Separable Non-convexity
• An outer-approximation algorithm for a class of mixed-integer nonlinear programs
• Global Optimization and Constraint Satisfaction