Difference between revisions of "Analytica Optimizer Guide"
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| − | + | [[Category: 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. |
| − | + | ||
| − | #* Using the Analytica Optimizer Guide | + | __NOTOC__ |
| + | == Chapters == | ||
| + | * [[Introduction to Optimizer]] | ||
| + | <!--#* Using the Analytica Optimizer Guide | ||
#* Obtaining Analytica Optimizer | #* Obtaining Analytica Optimizer | ||
#* Activating Analytica Optimizer | #* Activating Analytica Optimizer | ||
| − | #* Installing Add-On Engines to Analytica Optimizer | + | #* Installing Add-On Engines to Analytica Optimizer--> |
| − | + | * [[Optimizer Quick Start]] | |
| − | #* Intro to Structured Optimization | + | <!--#* Intro to Structured Optimization |
#* Notation | #* Notation | ||
#* The Optimum Can Example | #* The Optimum Can Example | ||
| Line 16: | Line 19: | ||
#** Constraints | #** Constraints | ||
#** Objectives | #** Objectives | ||
| − | #** The DefineOptimization() | + | #** The DefineOptimization() function |
#** Viewing the Optimization Object | #** Viewing the Optimization Object | ||
#** Obtaining the Solution | #** Obtaining the Solution | ||
| Line 26: | Line 29: | ||
#** Using Parametric Analysis with Optimization | #** Using Parametric Analysis with Optimization | ||
#** The Initial Guess Attribute | #** The Initial Guess Attribute | ||
| − | #* Summary | + | #* Summary--> |
| − | + | * [[Optimization Characteristics]] | |
| − | #* Introduction | + | <!--#* Introduction |
#* Parts of an Optimization Problem: General Description | #* Parts of an Optimization Problem: General Description | ||
#* Identifying the Type of Optimization | #* Identifying the Type of Optimization | ||
#* Specific Optimization Characteristics | #* Specific Optimization Characteristics | ||
#* Continuous, Integer and Mixed-Integer Programs | #* Continuous, Integer and Mixed-Integer Programs | ||
| − | #* Solving Simultaneous Equations | + | #* Solving Simultaneous Equations--> |
| − | # | + | * [[Optimizing with Arrays]] |
| − | # Key Concepts: The Airline NLP Example | + | * [[Optimizer key concepts: Airline Example]] |
| − | # | + | * [[Optimizer Attributes]] |
| − | + | * [[Optimizer 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 [[Analytica_Tutorial#Working_with_Arrays_.28Tables.29|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 [[Quick_Start#Introduction_to_Structured_Optimization|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|parametric analysis]]. [[Key Concepts: The Airline NLP Example]] explains dynamic and stochastic optimization with models that are [[dynamic]] (changing over time) and/or [[Probabilistic calculation|uncertain]] using Monte Carlo. | ||
| + | |||
| + | == Conventions Used in this Guide == | ||
| + | |||
| + | You can read the pages in this guide in any order. But, if you want to go through them sequentially, you can use the links to the previous and next pages at the bottom of each page. | ||
| + | |||
| + | This guide uses a simple shorthand to show the definition of a variable or function. It lists the [[class]] of the object (Variable, Decision, Constraint, Objective, etc), its [[identifier]], followed by [[Assignment Operator :=|:=]], and its [[definition]]: | ||
| + | |||
| + | : [[File:1-1-new.png|400px]] | ||
| + | |||
| + | In this example, a '''Constraint''' with identifier '''Volume_Constraint''' has the '''Definition''' '''Volume >= Required Volume'''. | ||
| + | |||
| + | ==See Also== | ||
| + | * [[Analytica Tutorial]] | ||
| + | * [[Analytica User Guide]] | ||
| + | * [[About_Analytica#Conventions_used_in_this_tutorial|Conventions used in Analytica Tutorial]] | ||
| + | * [[Typographic conventions in this guide|Conventions used in Analytica User Guide]] | ||
| + | * [[Expression Syntax]] | ||
| + | * [[Expression Assist]] | ||
| + | |||
| + | |||
| + | <footer>Analytica Docs / {{PAGENAME}} / Introduction to Optimizer</footer> | ||
Latest revision as of 21:12, 7 August 2024
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.
Chapters
- Introduction to Optimizer
- Optimizer Quick Start
- Optimization Characteristics
- Optimizing with Arrays
- Optimizer key concepts: Airline Example
- Optimizer Attributes
- Optimizer 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.
Conventions Used in this Guide
You can read the pages in this guide in any order. But, if you want to go through them sequentially, you can use the links to the previous and next pages at the bottom of each page.
This guide uses a simple shorthand to show the definition 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.
See Also
- Analytica Tutorial
- Analytica User Guide
- Conventions used in Analytica Tutorial
- Conventions used in Analytica User Guide
- Expression Syntax
- Expression Assist
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