Difference between revisions of "Optimizer key concepts: Airline Example"
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* [[NLP Characteristics]] | * [[NLP Characteristics]] | ||
* [[Airline NLP Module 1: Base Case]] | * [[Airline NLP Module 1: Base Case]] | ||
− | * Using | + | * [[Using Parametric Analysis: Airline NLP Module 2]] |
− | * Optimizing with Uncertainty | + | * [[Optimizing with Uncertainty]] |
− | * Improving Computational Efficiency of NLPs | + | ** [[Optimizing_with_Uncertainty#Module_3:_Stochastic_Optimization_.28FAST.29|Module 3: Stochastic Optimization (FAST)]] |
− | * Module 5: Time as an Extrinsic index | + | ** [[Optimizing_with_Uncertainty#Module_4:_Multiple_Optimizations_of_Separate_Samples_.28MOSS.29|Module 4: Multiple Optimizations of Separate Samples (MOSS)]] |
− | * Identifying the Source of an Extrinsic Index | + | * [[Improving Computational Efficiency of NLPs]] |
− | * Module 6: Time as an Intrinsic Index | + | * [[Module 5: Time as an Extrinsic index]] |
− | * Module 7: Embedding an NLP in a Dynamic Loop | + | * [[Identifying the Source of an Extrinsic Index]] |
− | * Controlling Engine Selection and Setting | + | * [[Module 6: Time as an Intrinsic Index]] |
+ | * [[Module 7: Embedding an NLP in a Dynamic Loop]] | ||
+ | * [[Controlling Engine Selection and Setting]] | ||
<br /> | <br /> | ||
− | <footer> Optimizing with Arrays / {{PAGENAME}} / Optimizer | + | <footer> Optimizing with Arrays / {{PAGENAME}} / Optimizer Attributes</footer> |
Latest revision as of 17:40, 7 June 2016
The Airline Non-Linear Program (NLP) Example demonstrates a set of key concepts that Analytica Optimizer modelers should be familiar with. Although it includes some topics that apply only to NLP models, each module includes content that is relevant to all optimization types.
Sections
- Concepts Covered in the Airline NLP Example
- NLP Characteristics
- Airline NLP Module 1: Base Case
- Using Parametric Analysis: Airline NLP Module 2
- Optimizing with Uncertainty
- Improving Computational Efficiency of NLPs
- Module 5: Time as an Extrinsic index
- Identifying the Source of an Extrinsic Index
- Module 6: Time as an Intrinsic Index
- Module 7: Embedding an NLP in a Dynamic Loop
- Controlling Engine Selection and Setting
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