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==Introduction to Structured Optimization==

The Analytica Optimizer releases from v,4.3 onward include a set of features, collectively called Structured Optimization, designed to simplify the optimization modeling process. In Structured Optimization format, Linear Programming (LP), Quadratic Programming (QP) and Nonlinear Programming (NLP) optimizations can all be modeled in similar ways. All types of optimization are specified using the «<tt>DefineOptimization()</tt>» function. 

<tip title="Note">The «<tt>DefineOptimization()</tt>» function supersedes the «<tt>LPDefine()</tt>», «<tt>QPDefine()</tt>» and «<tt>NLPDefine()</tt>» functions that were used to specify optimizations prior to Analytica 4.2. These functions remain available for backward compatibility, but are now deprecated.</Tip>

The «<tt>DefineOptimization()</tt>» function automatically analyzes your model to determine the type of optimization and selects the appropriate optimization engine, although you can still override this process if desired.

Another significant change associated with Structured Optimization is the introduction of the «Constraint» object type. «Constraint» objects give users the ability to specify constraints, or arrays of constraints, in common expression format using equality or inequality operators. This intuitive interface allows users to easily integrate different types of constraints and to organize constraint arrays efficiently.

This chapter includes simple NLP examples to demonstrate the roles of Decision variables, Constraint objects, Objective variables, and Decision attributes in the Structured Optimization framework. The same basic structure applies to LP and QP optimizations as well.