The Analytica Optimizer adds powerful solvers for finding optimal decisions and solving equations subject to constraints. Most Optimizer models aim to find the best decision strategy — that is, the values for decision variables that maximize (or minimize) an Objective subject to a set of equality or inequality constraints. Some models seek a feasible solution that satisfies the constraints without an explicit objective.
The Optimizer edition of Analytica finds the best strategies for models with multiple decision variables and constraints. It adds powerful solvers to all the features of the Enterprise edition.
Lumina Analytica Optimizer Edition 2024 v6.4.8 Tested Picture
Experienced modelers know that the biggest challenge in optimization is to formulate your problem in a way that is clear and computationally tractable. Traditional approaches have limitations:
- Spreadsheet add-ins are fine for simple problems but are inherently two-dimensional and difficult to scale.
- Algebraic modeling languages lack visual context making complex models incomprehensible to anyone but their designer.
The basics
Analytica’s structured optimization makes modeling as clear and intuitive as it should be, at any level of complexity. You use influence diagrams to define the key elements: decision variables, objectives, and constraints. You use indexes to specify the dimensions. The advantages include:
- Influence diagrams let you define the problem structure visually.
- It can formulate the problem automatically based on the decision variables, objective, and constraints.
- It chooses the appropriate solver to match the problem type: linear, mixed-integer, quadratic, or nonlinear.
- You can treat uncertainty simply by specifying chance variables with probability distributions, for stochastic optimization.
- Intelligent arrays let you easily modify and add dimensions, including multiple scenarios for sensitivity analysis, and time for dynamic models, without changing the diagram structure.
“Analytica is hands-down the best platform out there for optimization problems.”, Scott Robinson, Director, Guidehouse Consulting.
Power & scalability
The optimizer edition includes solvers to handle all types of optimization problems: Linear (LP), Quadratic (QP), and NonLinear Programs (NLP). These can handle
- Up to 8,000 decision variables and 8,000 constraints for LP and convex QP problems
- Continuous, Mixed-Integer (MIP), and semi-continuous variables.
- Up to 500 variables and 250 constraints for NLP problems.
