MAiNGO: McCormick based Algorithm for mixed integer Nonlinear Global Optimization. MAiNGO is a deterministic global optimization software for solving mixed-integernonlinear programs (MINLP). It is applicable to a wide range of MINLPs and has been shownto have computational advantages for classes of problems that admit reduced-space formulations.Furthermore, it can also serve as a framework for simulation and local optimization. Main algo-rithmic features of MAiNGO are the operation in the original variable space through the use ofMcCormick relaxations (i.e., no introduction of auxiliary variables) through MC++(Chachuat etal.,IFAC-PapersOnline48 (2015), 981), custom relaxations for various functions (including severalfunctions relevant to process systems engineering), and significant flexibility in model formulation.In addition to a basic branch-and-bound with some state-of-the-art bound tightening techniqueslike duality-based bound tightening and optimization-based bound tightening, it implements spe-cialized heuristics for tightening McCormick relaxations as well as a multistart heuristic. Thisreport summarizes the capabilities, algorithm, and software structure of the current version ofMAiNGO (v0.1.12)
Keywords for this software
References in zbMATH (referenced in 4 articles )
Showing results 1 to 4 of 4.
- Bongartz, Dominik; Najman, Jaromił; Mitsos, Alexander: Deterministic global optimization of steam cycles using the IAPWS-IF97 model (2020)
- Huster, Wolfgang R.; Schweidtmann, Artur M.; Mitsos, Alexander: Working fluid selection for organic rankine cycles via deterministic global optimization of design and operation (2020)
- Najman, Jaromił; Mitsos, Alexander: Tighter McCormick relaxations through subgradient propagation (2019)
- Schweidtmann, Artur M.; Mitsos, Alexander: Deterministic global optimization with artificial neural networks embedded (2019)