It includes all Enterprise features, plus the addition of powerful solver ees. 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 s;

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.

Updates: official site does not provide any info about changes in this version.



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