Difference between revisions of "Concepts Covered in the Airline NLP Example"

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Topics relevant to all optimization types (LP, QP, and NLP) are:
 
Topics relevant to all optimization types (LP, QP, and NLP) are:
* '''[http://wiki.analytica.com/index.php?title=Airline_NLP_Module_1%3A_Base_Case Module 1]'''[http://wiki.analytica.com/index.php?title=Airline_NLP_Module_1%3A_Base_Case : Setting up basic Airline NLP example]
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* '''[[Airline_NLP_Module_1%3A_Base_Case|Module 1]]'''[[Airline_NLP_Module_1%3A_Base_Case|: Setting up basic Airline NLP example]]
* '''[http://wiki.analytica.com/index.php?title=Using_Parametric_Analysis%3A_Airline_NLP_Module_2 Module 2]'''[http://wiki.analytica.com/index.php?title=Using_Parametric_Analysis%3A_Airline_NLP_Module_2 : Parametric Analysis]
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* '''[[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:
** '''[http://wiki.analytica.com/index.php?title=Optimizing_with_Uncertainty#Module_3:_Stochastic_Optimization_.28FAST.29 Module 3]'''[http://wiki.analytica.com/index.php?title=Optimizing_with_Uncertainty#Module_3:_Stochastic_Optimization_.28FAST.29 : Optimizing on Fractiles or Averages Stochastically (FAST)]
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** '''[[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)]]
** '''[http://wiki.analytica.com/index.php?title=Optimizing_with_Uncertainty#Module_4:_Multiple_Optimizations_of_Separate_Samples_.28MOSS.29 Module 4]'''[http://wiki.analytica.com/index.php?title=Optimizing_with_Uncertainty#Module_4:_Multiple_Optimizations_of_Separate_Samples_.28MOSS.29 : Multiple Optimizations of Separate Samples (MOSS) method]
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** '''[[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]]
  
* '''[http://wiki.analytica.com/index.php?title=Module_5%3A_Time_as_an_Extrinsic_index Module 5]'''[http://wiki.analytica.com/index.php?title=Module_5%3A_Time_as_an_Extrinsic_index : Abstracted objectives; example of Time as an extrinsic index]
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* '''[[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]]
* '''[http://wiki.analytica.com/index.php?title=Module_6%3A_Time_as_an_Intrinsic_Index Module 6]'''[http://wiki.analytica.com/index.php?title=Module_6%3A_Time_as_an_Intrinsic_Index : Intrinsic decision arrays; example of Time as an intrinsic index]<br />
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* '''[[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 ('''[http://wiki.analytica.com/index.php?title=Optimizing_with_Uncertainty#Module_4:_Multiple_Optimizations_of_Separate_Samples_.28MOSS.29 Modules 4] and [http://wiki.analytica.com/index.php?title=Module_5%3A_Time_as_an_Extrinsic_index 5]''')
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* 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]]''')
* '''[http://wiki.analytica.com/index.php?title=Module_7%3A_Embedding_an_NLP_in_a_Dynamic_Loop Module 7]'''[http://wiki.analytica.com/index.php?title=Module_7%3A_Embedding_an_NLP_in_a_Dynamic_Loop : Embedding an NLP inside a dynamic loop<br />]
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* '''[[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:


Embedded topics relevant only to Non-Linear Problems (NLPs) are:


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