Softwares Lumina Analytica Optimizer 6.3.6.226

Lumina Analytica Optimizer is the highest version that includes all the Enterprise features, plus the addition of powerful resolution tools. It discovers decision values that minimize or maximize any quantitative objective, subject to constraints. Or, in the absence of an objective quantity, it finds a feasible solution within the constraints. It handles Linear Programming, Quadratic Programming and Non-Linear Programming in general and automatically differentiates between all of....

Lumina Analytica Optimizer is the highest version that includes all the Enterprise features, plus the addition of powerful resolution tools. It discovers decision values that minimize or maximize any quantitative objective, subject to constraints. Or, in the absence of an objective quantity, it finds a feasible solution within the constraints. It handles Linear Programming, Quadratic Programming and Non-Linear Programming in general and automatically differentiates between all of them.

Optimization models must be simple

  • Should optimization models be intuitive, transparent, scalable, and easy to build? We think they should. But traditional optimization interfaces do not meet all of these goals.
  • Spreadsheet optimization is fine 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 the lack of visual context can still make it incomprehensible to anyone but the modeler complex models.
  • Analysis makes optimization modeling simple and intuitive at any level of complexity. Influence Maps and Smart Arrays make the entire analytics process accessible, from modeling to decision making. It does this in many ways.
  • Always show model structure and assumptions clearly;
  • Combine optimization with sensitivity analysis to determine the inputs that have the most direct impact on your target value;
  • Allows you to add new scenarios for separate optimization simply by adding scenario dimensions to any input array;
  • Add Constraints nodes to let you specify constraint arrays using simple inequality expressions;
  • Enables you to easily scale existing models with Smart Arrays.
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