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Optimization Analytics Webinar Series
Analytica Optimizer is being used by companies, government agencies and universities around the world. Given Analytica's highly visual and interactive nature, the fastest way to learn what it is and how it works is to see a demonstration. In April 2011, Lumina hosted a complimentary webinar series on the Optimization features available in Analytica Optimizer. Please download the recorded webinars and presentation files to learn more about Optimization in Analytica.
1. Introduction to Structured Optimization
Presenter: Paul Sanford, Lumina.
Analytica 4.3 was released in March 2011. The new version includes expanded optimization capabilities and simplified workflow for encoding optimization problems. The new Structured Optimization framework in 4.3 is centered around a new function, DefineOptimization(), which replaces all three of the previous type-specific functions: LPDefine(), QPDefine() and NLPDefine(). It also introduces a new node type, Constraint, which allows you to specify constraints using ordinary expressions. In this webinar Paul builds up some basic examples using Structured Optimization and fields questions from users.
Watch a recording of this webinar.
The example models used during this webinar are: Beer Distribution LP1.ana,Beer Distribution LP2.ana, File:Plane Allocation LP.ana,File:Polynomial NLP.ana
2. Introduction to Linear and Quadratic Programming
Presenter: Dr. Lonnie Chrisman, CTO, Lumina
This talk is an introduction to linear programming and quadratic programming, and an introduction to solving LPs and QPs from inside an Analytica model (via Analytica Optimizer). LPs and QPs can be efficiently encoded using the Analytica Optimizer functions LpDefine and QpDefine. I'll introduce what a linear program is for the sake of those who are not already familiar, and examine some example problems that fit into this formalism. We'll encode a few in Analytica and compute optimal solutions. Although LPs and QPs are special cases of non-linear programs (NLPs), they are much more efficient and reliable to solve, avoid many of the complications present in non-linear optimization, and fully array abstract. Many problems that initially appear to be non-linear can often be reformulated as an LP or QP. We'll also see how to compute secondary solutions such as dual values (slack variables and reduced prices) and coefficient sensitivies. Finally, LpFindIIS can be useful for debugging an LP to isolate why there are no feasible solutions.
3. Non-Linear Optimization
Presenter: Dr. Lonnie Chrisman, CTO, Lumina
This webinar focuses on the problem of maximizing or minimizing an objective criteria in the presence of contraints. This problem is referred to as a non-linear program, and the capability to solve problems of this form is provided by the Analytica Optimizer via the NlpDefine function. In this talk, I'll introduce the use of NlpDefine for those who have not previously used this function, and demonstrate how NLPs are structured within Analytica models. I'll examine various challenges inherent in non-linear optimization, tricks for diagnosing these and some ways to address these. We'll also examine various ways in which to structure models for parametric analyses (e.g., array abstraction over optimization problems), and optimizations in the presence of uncertainty.
4. Interactive Optimization Workshop
Presenter: Paul Sanford, Lumina
This is an interactive workshop where you will learn the basics of creating Structured Optimization models and challenge yourself to set up and solve some basic examples on your own! No prior training in optimization is required. Trial Downloads of Analytica Optimizer are now available. Attendees are encouraged to have Analytica Optimizer 4.3 installed and running during the workshop.
5. Optimizing Parameters in a Complex Model to Match Historical Data
Presenter: Dr. Lonnie Chrisman, CTO, Lumina
Almost all quantitative models have parameters that must be assessed by experts or estimated from historical data. Estimation from historical data can be complicated by the presence of variables that are either unobservable or unavailable in the historical record. Maximum likelihood estimation addresses this by finding the parameter settings that maximize the likelihood of the historical data predicted by the model. In this talk, I will formulate the parameter fitting task as a structured optimization problem (NLP), providing a hands-on demonstration of the new structured optimization features in Analytica 4.3.
6. Optimization with Uncertainty
Presenter: Dr. Lonnie Chrisman, CTO, Lumina
Analytica analyzes uncertainty by conducting a Monte Carlo analysis. When you optimize decision variables in a model containing uncertainty, you have a choice: You can perform one optimization over the Monte Carlo analysis, or you can perform a Monte Carlo sampling of optimizations (i.e., the Monte Carlo is inside the optimization, or the optimization is inside the Monte Carlo). The first case is used when the decision must be taken while the quantities are still uncertain. The second case is used when the values of the uncertain quantities will be resolved before the decisions are taken. To illustrate, consider the situation faced by a relief organization that provides aid to victims of large natural disasters. In one situation, a decision must be made regarding how many resources to deploy to one particular location that has been hit by a large tsunami. At the time the decision must be made, the number of casualties is highly uncertain. In a different situation, the organization wants to characterize the uncertainty in its need for resources, given that it will optimally deploy resources in response to natural disasters as they occur.
