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AIMMS (an acronym for "Advanced Interactive Multidimensional Modeling System") is a software system designed for modeling and solving large-scale optimization and scheduling-type problems.〔 〕〔 〕 It consists of an algebraic modeling language, an integrated development environment for both editing models and creating a graphical user interface around these models, and a graphical end-user environment.〔 〕 AIMMS is linked to multiple solvers through the AIMMS Open Solver Interface, not to be confused with COIN-OR Open Solver Interface (OSI) which unlike AIMMS OSI is an open-source project.〔 (【引用サイトリンク】 author = Paragon Decision Technology ) 〕 Supported solvers include CPLEX, Gurobi, MOSEK, CBC, Conopt, MINOS, IPOPT, SNOPT, KNITRO and (CP Optimizer ). AIMMS is considered to be one of the five most important algebraic modeling languages (others are: AMPL, GAMS, LINDO/LINGO, and MPL), and the creator (Johannes J. Bisschop) has been awarded with INFORMS Impact Prize for his work in this language.〔http://www.informs.org/Blogs/E-News-Blog/INFORMS-Impact-Prize〕 ==Features== AIMMS features a mixture of declarative and imperative programming styles. Formulation of optimization models takes place through declarative language elements such as sets and indices, as well as scalar and multidimensional parameters, variables and constraints, which are common to all algebraic modeling languages, and allow for a concise description of most problems in the domain of mathematical optimization. Units of measurement are natively supported in the language, and compile- and runtime unit analysis may be employed to detect modeling errors. Procedures and control flow statements are available in AIMMS for * the exchange of data with external data sources such as spreadsheets, databases, XML and text files * data pre- and post-processing tasks around optimization models * user interface event handling * the construction of hybrid algorithms for problem types for which no direct efficient solvers are available. To support the re-use of common modeling components, AIMMS allows modelers to organize their model in user model libraries. AIMMS supports a wide range of mathematical optimization problem types: * Linear programming * Quadratic programming * Nonlinear programming * Mixed-integer programming * Mixed-integer nonlinear programming * Global optimization * Complementarity problems (MPECs) * Stochastic programming * Robust optimization * Constraint programming Uncertainty can be taken into account in deterministic linear and mixed integer optimization models in AIMMS through the specification of additional attributes, such that stochastic or robust optimization techniques can be applied alongside the existing deterministic solution techniques. Custom hybrid and decomposition algorithms can be constructed using the GMP system library which makes available at the modeling level many of the basic building blocks used internally by the higher level solution methods present in AIMMS, matrix modification methods, as well as specialized steps for customizing solution algorithms for specific problem types. Optimization solutions created with AIMMS can be used either as a standalone desktop application or can be embedded as a software component in other applications. 抄文引用元・出典: フリー百科事典『 ウィキペディア（Wikipedia）』 ■ウィキペディアで「AIMMS」の詳細全文を読む スポンサード リンク
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