Difference between revisions of "Analytica Optimizer Guide"

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# Optimizer Function Reference
 
# Optimizer Function Reference
 
# Control Settings
 
# Control Settings
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=Introduction=
 
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This Guide explains how to use the Analytica Optimizer, which enhances Analytica 4.6 with powerful functions for finding optimal decisions and solving difficult equations.
 
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It provides a Quick Start Tutorial in Chapter 1 and an introduction to the basic concepts of optimization, including linear, quadratic, and nonlinear programming in Chapter 2. Special topics for NLPs are also covered in Chapter 4. But, it's not a complete textbook on optimization. You might find it useful, especially for more challenging applications, to consult one of the many good textbooks on optimization.
 
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'''If you're new to Analytica'''
 
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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 Chapter 5, Working with Arrays. It's important to have a good understanding of Intelligent Arrays to make good use of the Optimizer.
 
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'''If you're new to the Analytica Optimizer'''
 
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We suggest you start with Chapter 1, Quick Start, an introductory tutorial that takes you through the key steps to create a simple optimization.
 
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Then you should go on to Chapter 2, Optimization characteristics, which 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 Chapter 3, Optimizing with Arrays to master optimization with parametric analysis. Chapter 4 explains how to use optimization in models having dynamic influences and Monte Carlo-based uncertainties.
 
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'''If you've used Analytica Optimizer 4.2 or earlier'''
 
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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 learn about these new features so you can take full advantage of them and then read Chapter 1, Quick Start, and Chapter 4, Optimizing with Intelligent Arrays (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.
 
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<!--==Introduction==
 
<!--==Introduction==
 
==== What is the Analytica Optimizer? ====
 
==== What is the Analytica Optimizer? ====

Revision as of 03:20, 6 November 2015

Table of Contents

  1. Introduction
    • Using the Analytica Optimizer Guide
    • Obtaining Analytica Optimizer
    • Activating Analytica Optimizer
    • Installing Add-On Engines to Analytica Optimizer
  2. Quick Start
    • Intro to Structured Optimization
    • Notation
    • The Optimum Can Example
      • Decisions
      • Constants
      • Variables
      • Constraints
      • Objectives
      • The DefineOptimization() Function
      • Viewing the Optimization Object
      • Obtaining the Solution
      • Obtaining the Optimized Objective Value
      • Viewing Optimization Status
      • Copying Optimized Results to Definitions
      • Changing Variable Types (Domain)
      • Setting Bounds on Decision Values
      • Using Parametric Analysis with Optimization
      • The Initial Guess Attribute
    • Summary
  3. Optimization Characteristics
    • Introduction
    • Parts of an Optimization Problem: General Description
    • Identifying the Type of Optimization
    • Specific Optimization Characteristics
    • Continuous, Integer and Mixed-Integer Programs
    • Solving Simultaneous Equations
  4. Optimizing with Arrays
  5. Key Concepts: The Airline NLP Example
  6. Optimizer Attribute Reference
  7. Optimizer Function Reference
  8. Control Settings
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