Difference between revisions of "Concepts Covered in the Airline NLP Example"
Line 9: | Line 9: | ||
Topics relevant to all optimization types (LP, QP, and NLP) are: | Topics relevant to all optimization types (LP, QP, and NLP) are: | ||
− | * '''[ | + | * '''[[Airline_NLP_Module_1%3A_Base_Case|Module 1]]'''[[Airline_NLP_Module_1%3A_Base_Case|: Setting up basic Airline NLP example]] |
− | * '''[ | + | * '''[[Using_Parametric_Analysis%3A_Airline_NLP_Module_2|Module 2]]'''[[Using_Parametric_Analysis%3A_Airline_NLP_Module_2|: Parametric Analysis]] |
* Combining uncertainty with optimization: | * Combining uncertainty with optimization: | ||
− | ** '''[ | + | ** '''[[Optimizing_with_Uncertainty#Module_3:_Stochastic_Optimization_.28FAST.29|Module 3]]'''[[Optimizing_with_Uncertainty#Module_3:_Stochastic_Optimization_.28FAST.29|: Optimizing on Fractiles or Averages Stochastically (FAST)]] |
− | ** '''[ | + | ** '''[[Optimizing_with_Uncertainty#Module_4:_Multiple_Optimizations_of_Separate_Samples_.28MOSS.29|Module 4]]'''[[Optimizing_with_Uncertainty#Module_4:_Multiple_Optimizations_of_Separate_Samples_.28MOSS.29|: Multiple Optimizations of Separate Samples (MOSS) method]] |
− | * '''[ | + | * '''[[Module_5%3A_Time_as_an_Extrinsic_index|Module 5]]'''[[Module_5%3A_Time_as_an_Extrinsic_index|: Abstracted objectives; example of Time as an extrinsic index]] |
− | * '''[ | + | * '''[[Module_6%3A_Time_as_an_Intrinsic_Index|Module 6]]'''[[Module_6%3A_Time_as_an_Intrinsic_Index| : Intrinsic decision arrays; example of Time as an intrinsic index]]<br /> |
<br /> | <br /> | ||
Embedded topics relevant only to Non-Linear Problems (NLPs) are:<br /> | Embedded topics relevant only to Non-Linear Problems (NLPs) are:<br /> | ||
− | * Improving efficiency using context variables ('''[ | + | * Improving efficiency using context variables ('''[[Optimizing_with_Uncertainty#Module_4:_Multiple_Optimizations_of_Separate_Samples_.28MOSS.29|Modules 4]] and [[Module_5%3A_Time_as_an_Extrinsic_index|5]]''') |
− | * '''[ | + | * '''[[Module_7%3A_Embedding_an_NLP_in_a_Dynamic_Loop|Module 7]]'''[[Module_7%3A_Embedding_an_NLP_in_a_Dynamic_Loop|: Embedding an NLP inside a dynamic loop]] |
<footer> Optimizing with Arrays / {{PAGENAME}} / NLP Characteristics</footer> | <footer> Optimizing with Arrays / {{PAGENAME}} / NLP Characteristics</footer> |
Revision as of 22:28, 29 March 2016
Before reading this chapter, you should already be familiar with the basic parameters of DefineOptimization() and OptSolution() functions, as discussed in the Quick Start, and the roles of intrinsic and extrinsic indexes in optimization, as discussed in Arrays in Optimization Models and Array Abstraction.
Additionally, Modules 3 and 4 of the Airline NLP example assume familiarity with Monte Carlo simulation and Probability Distributions (see Statistics, Sensitivity, and Uncertainty Analysis in the Analytica User Guide).
Module 7 assumes familiarity with the Dynamic() function (see Dynamic Simulation in the Analytica User Guide).
Topics relevant to all optimization types (LP, QP, and NLP) are:
- Module 1: Setting up basic Airline NLP example
- Module 2: Parametric Analysis
- Combining uncertainty with optimization:
- Module 5: Abstracted objectives; example of Time as an extrinsic index
- Module 6 : Intrinsic decision arrays; example of Time as an intrinsic index
Embedded topics relevant only to Non-Linear Problems (NLPs) are:
- Improving efficiency using context variables (Modules 4 and 5)
- Module 7: Embedding an NLP inside a dynamic loop
Enable comment auto-refresher