Algorithms and Software for Convex Mixed Integer Nonlinear Programs

By Pierre Bonami, Mustafa R. Kilinç, Jeff T. Linderoth. 2012

311 citations recorded by the source corpus.

Subfields: Control and Systems Engineering, Numerical Analysis

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Citations in the corpus

Learning Optimized Risk Scores (2016, background): “Although the risk score problem is NP-hard, we trained RiskSLIM models that were certifiably optimal or had small optimality gaps for all datasets in under 20 minutes using an LCPA implementation with the improvements in Section 4.”
On handling indicator constraints in mixed integer programming (2016, background): “[5] for algorithmic approaches to convex MINLPs), it is legitimate to ask if some alternative choice would lead to better results.”
Multiphase Mixed-Integer Optimal Control Approach to Aircraft Trajectory Optimization (2013, methodology): “Whereas, for choice of the variable v̂, a default strategy in BONMIN is used, which is a combination of strong branching and pseudocosts [28].”
Non-convex mixed-integer nonlinear programming: A survey (2012, background): “They all cover the convex case, and some cover the non-convex case.”
Increasing Supply Chain Robustness through Process Flexibility and Inventory (2018, result): “When the uncertainty set contains only linear inequalities as in equation (4), the optimization algorithm (described in section 2.2) can be implemented efﬁciently by solving a sequence of mixed integer linear programs (MILPs).”
Demand Response Optimization for Smart Home Scheduling Under Real-Time Pricing (2012, background): “In the presence of SAs, the problem in (1) would turn into a convex mixed integer nonlinear program (MINLP), which is known to be NP hard [12].”
Polyhedral approximation in mixed-integer convex optimization (2016, background): “Bonami, Kilinç, and Linderoth [10] note in a recent review that continued advances in MILP have translated into “far more modest” growth in the scale of problems which MICP solvers can solve within reasonable time limits.”
Mixed-integer nonlinear optimization*† (2013, methodology): “We mention a number of surveys and monographs on MINLP, including a review of MINLP and disjunctive programming (Grossmann, 2002), a survey of MINLP algorithms (Grossmann and Kravanja, 1997), a survey of algorithms and software for MINLP (Bonami et al., 2012), a comprehensive textbook on MINLP by Fl”