Analytica Optimizer Guide

This Guide explains how to use the Analytica Optimizer. It provides a Quick Start Tutorial and an introduction to the basic concepts of optimization, including linear, quadratic, and nonlinear programming (NLP), as well as special topics in NLP. However, it is not a complete textbook on optimization. For more challenging applications, you might find it useful to consult one of the many good textbooks on optimization.

Sections

• [Introduction to Optimizer]]
• Optimizer Quick Start
• Optimization Characteristics
• Optimizing with Arrays
• Optimizer key concepts: Airline Example
• Optimizer Attribute Reference
• Primary Optimization Functions
• Optimization Status Functions
• Optimizer control settings

Using this Guide

If you're new to Analytica

You will find it easier if you first learn the essentials of Analytica before learning the Analytica Optimizer described here. Start with the Analytica Tutorial to learn the basics of interacting with Analytica and its modeling language, especially Working with Arrays. It's important to have a good understanding of Intelligent Arrays to make good use of the Optimizer.

If you're new to the Analytica Optimizer

We recommend you to start with the Quick Start, an introductory tutorial that takes you through the key steps to create a simple optimization, including Structured Optimization, and Optimizing with Arrays. The section on Optimization Characteristics explains the general principles of optimization and the types of optimization, including Linear Programming (LP), Quadratic Programming (QP), and Non-Linear Programming (NLP). We also recommend reading Optimizing with Arrays to understand optimization with parametric analysis. Key Concepts: The Airline NLP Example explains dynamic and stochastic optimization with models that are dynamic (changing over time) and/or uncertain using Monte Carlo.

Structured Optimization

The 4.3 release of Analytica introduced Structured Optimization, a new set of features that eliminates many difficult steps previously required for structuring optimizations in Analytica. For example, it can use a set of decision variables of varying dimensions, instead of requiring you to combine and flatten them manually. It introduces Constraints as a new object class. It can discover automatically whether your objective is linear, quadratic, or nonlinear, and apply the appropriate solver engine --and a whole lot more.

So if you’ve used the Optimizer before, we strongly recommend that you read the Quick Start, which introduces Structured Optimization, and the Optimizing with Arrays section of this guide (at least). Even though Analytica 4.4 still supports functions from releases 4.2 and earlier of Optimizer for backward compatibility, you will likely want to learn and use the new functions instead.

Conventions Used in this Guide

Under the title of each page on this guide, the page's hierarchy and any parent pages are listed.

The pages in this guide can be read in any order. However, if you want to read the guide sequentially, there are links to the previous and next pages at the bottom of each page which will take the reader through all of the guide's pages in order.

Throughout this guide, we use a shorthand notation to show the definitions of a variable or function. It lists the class of the object (Variable, Decision, Constraint, Objective, etc), its identifier, followed by :=, and its definition:

In this example, a Constraint with identifier Volume_Constraint has the Definition Volume >= Required Volume.