Difference between revisions of "Chapter Summary"
Jhernandez3 (talk | contribs) |
|||
| (2 intermediate revisions by the same user not shown) | |||
| Line 1: | Line 1: | ||
| + | [[Category: Analytica Optimizer Guide]] | ||
| + | |||
<breadcrumbs> Analytica Optimizer Guide > Optimizing with Arrays > {{PAGENAME}}</breadcrumbs> | <breadcrumbs> Analytica Optimizer Guide > Optimizing with Arrays > {{PAGENAME}}</breadcrumbs> | ||
<br /> | <br /> | ||
| Line 5: | Line 7: | ||
The Intrinsic Index attribute in Decision and Constraint arrays controls how the optimizer interprets arrays. The optimizer incorporates intrinsic arrays into the optimization while leaving extrinsic arrays eligible for array abstration outside the optimization. | The Intrinsic Index attribute in Decision and Constraint arrays controls how the optimizer interprets arrays. The optimizer incorporates intrinsic arrays into the optimization while leaving extrinsic arrays eligible for array abstration outside the optimization. | ||
| − | In the [ | + | In the [[Example_1%3A_Beer_Distribution_LP,_Base_Case|first example]], the input Decision and Constraint arrays had intrinsic dimensions. A single optimization run incorporated all elements of the intrinsic indexes simultaneously. |
| − | In the [ | + | In the [[Example_2%3A_Beer_Distribution_with_Added_Scenario|second example]], we added an extrinsic dimension to the model. Analytica propagated this new dimension through the model according to the usual principles of array abstraction. As a result, the optimizer performed independent optimization runs for each element of the new index. The Optimized Solution array displayed the combined results as a single array having both intrinsic and extrinsic dimensions. |
| − | In the [ | + | In the [[Example_3%3A_Beer_Distribution_with_Limited_Routes|third example]], we restricted the solution space by substituting a new index for the input Decision and Objective arrays. We aggregated decision quantities using index maps in order to apply constraints.<br /> |
| − | <footer> | + | <footer> Piecewise-Linear (PWL) Relationships / {{PAGENAME}} / Optimizer key concepts: Airline Example</footer> |
Latest revision as of 17:42, 24 May 2016
This chapter presented an overview of how to use arrays in optimization problems. We used three examples to demonstrate the differences between intrinsic and extrinsic indexes in optimization arrays.
The Intrinsic Index attribute in Decision and Constraint arrays controls how the optimizer interprets arrays. The optimizer incorporates intrinsic arrays into the optimization while leaving extrinsic arrays eligible for array abstration outside the optimization.
In the first example, the input Decision and Constraint arrays had intrinsic dimensions. A single optimization run incorporated all elements of the intrinsic indexes simultaneously.
In the second example, we added an extrinsic dimension to the model. Analytica propagated this new dimension through the model according to the usual principles of array abstraction. As a result, the optimizer performed independent optimization runs for each element of the new index. The Optimized Solution array displayed the combined results as a single array having both intrinsic and extrinsic dimensions.
In the third example, we restricted the solution space by substituting a new index for the input Decision and Objective arrays. We aggregated decision quantities using index maps in order to apply constraints.
Enable comment auto-refresher