Lumina Analytica Optimizer 6.4.8.243 x64

Optimizer是Analytica的最高级版本，其包含了所有企业版功能，以及强大的处理引擎。其基于约束条件，用以发掘任何量化对象的最小化或最大化价值。或者，在某些对象数量不可用的情况下，它还可以通过内在的约束边界发现可行性解决方案。它可以处理线形编程、二次方程变成以及一般的非线性编程，以及自动地识别。决策变量可以使连续的，半连续的，离散的或者混合的。其中最好的是，它可以无缝地与Analytica核心功能，包括Monte Carlo仿真和智能数组，简化模型结构以及改善可视化能力相集成。它是一款完整的综合了处理能力、可扩展性以及比其他优化平台易于使用决策解决方案。

File Size: 62.8 MB

Optimizer is the highest edition level of Analytica. It includes all Enterprise features, plus the addition of powerful solver engines. It discovers decision values that minimize or maximize any quantified objective, subject to constraints. Or, in cases where an objective quantity is not present, it finds feasible solutions within constraint boundaries. It handles Linear Programming, Quadratic Programming, and general Non-Linear Programming, and automatically distinguishes among all of them.

Decision variables can be continuous, semi-continuous, discrete (Integer or Boolean), or mixed. Best of all, Analytica Optimizer seamlessly integrates optimization capability with all of Analytica’s core features including: Monte Carlo simulation and Intelligent Arrays, simplifying model structure, and improving visual accessibility. It’s a complete decision solution that combines solving power, scalability, and ease of use like no other optimization platform.

Optimization models should be simple, especially when they’re complex
Should optimization models be intuitively represented, transparent, scalable, and easy to build? We think they should. But traditional optimization interfaces fail to meet all of these goals.
Spreadsheet optimizations are suitable for smaller problems, but they are inherently two-dimensional and difficult to scale;
Algebraic modeling languages are much better than the straight programming notation that preceded them, but their lack of visual context can still make complex models inscrutable to anyone but the model designer.
Analytica makes optimization modeling as simple and intuitive as it should be at all levels of complexity. Influence diagrams and Intelligent Arrays keep the entire analysis path accessible, from modeling to decision making. It accomplishes this in several ways
Keeps model structure and assumptions in plain view at all times;
Combines optimization with sensitivity analysis to identify the inputs that have the most immediate influence on your objective value;
Allows you to add new scenarios for separate optimizations simply by adding a scenario dimension to any input array;
Adds Constraint nodes to allow you to specify arrays of constraints using simple inequality expressions;
Allows you to easily scale existing models using Intelligent Arrays.

Full integration with Uncertainty, Dynamic equations, and Intelligent Arrays
Analytica’s core features, including Monte Carlo sampling, Intelligent Arrays, and Dynamic definitions, are all compatible with optimization. This allows you to implement advanced methods in the same intuitive manner as with all Analytica models
Perform Stochastic Optimization by simply defining the objective as the expected value (mean) or fractile percentage threshold of a sampled distribution result;
Optimize Monte Carlo samples individually using Intelligent Array logic. It is easy to create a distribution of optimization results;
Define constraints using dynamic expressions for models with recursive dependency.

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