Difference between revisions of "Error Messages/41323"
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| − | + | [[Category: Error messages]] | |
| − | The first constraint specified in the linear optimization problem defined in | + | == Example error messages == |
| − | (((3*(X^2))+4)<=8) | + | <pre style="background:white; border:white; margin-left: 1em; font-style:italic"> |
| + | The first constraint specified in the linear optimization problem defined in 'My_opt' is non-linear: | ||
| + | (((3*(X^2)) + 4) <= 8) | ||
| − | The linear optimization defined in | + | The linear optimization defined in 'My_opt' contains the non-linear constraint 'Inventory_requirement'. |
| − | was first detected in | + | The non-linearity was first detected in 'Variable Customer_growth_pot'. |
| − | The linear optimization defined in | + | The linear optimization defined in 'Portfolio_QP' contains a non-linear constraint 'Risk_thresh'. |
| + | </pre> | ||
| − | = Cause = | + | == Cause == |
| − | In the specification of your optimization problem using [[DefineOptimization]], you have specified the type of optimization problem in the «type» parameter. When [[DefineOptimization]] analyzed your model, it found that a constraint was not of the specified type. For example, if you declare <code>type:"LP"</code>, but a constraint turns out to be a non-linear function of the decision variables, than this message results. | + | In the specification of your optimization problem using [[DefineOptimization]], you have specified the type of optimization problem in the «type» parameter. When [[DefineOptimization]] analyzed your model, it found that a constraint was not of the specified type. For example, if you declare <code>type: "LP"</code>, but a constraint turns out to be a non-linear function of the decision variables, than this message results. |
In some cases, Analytica may conclude that a constraint is non-linear or non-quadratic when intermediate computations involve non-quadratic, or potentially non-quadratic, operations. A simple example would be: | In some cases, Analytica may conclude that a constraint is non-linear or non-quadratic when intermediate computations involve non-quadratic, or potentially non-quadratic, operations. A simple example would be: | ||
| − | + | :<code>(x^3 - 7) - x^3</code> | |
Although this is a quadratic relationship of the decision variable ''x'', an intermediate involves a non-quadratic (''x^3''), and hence Analytica will conclude that the relationship is not quadratic. | Although this is a quadratic relationship of the decision variable ''x'', an intermediate involves a non-quadratic (''x^3''), and hence Analytica will conclude that the relationship is not quadratic. | ||
| − | = Remedy = | + | == Remedy == |
[[DefineOptimization]] allows you to specify the «type» explicitly so that you can catch cases where non-linearity or non-quadraticity is accidentally introduced into the model. Since linear and quadratic problems usually solve faster and more reliably, odds are that you'll want to remove the non-linearity or non-quadratic operation. | [[DefineOptimization]] allows you to specify the «type» explicitly so that you can catch cases where non-linearity or non-quadraticity is accidentally introduced into the model. Since linear and quadratic problems usually solve faster and more reliably, odds are that you'll want to remove the non-linearity or non-quadratic operation. | ||
| − | If you decide to relax your optimization, say to a non-linear optimization, you can either remove the «type» parameter entirely from your call to [[DefineOptimization]], or set it to <code>type:"NLP"</code>. If you set it <code>type:"NLP"</code>, you can save time by allowing [[DefineOptimization]] skip its attempt to determine whether the model is linear or quadratic. | + | If you decide to relax your optimization, say to a non-linear optimization, you can either remove the «type» parameter entirely from your call to [[DefineOptimization]], or set it to <code>type: "NLP"</code>. If you set it <code>type: "NLP"</code>, you can save time by allowing [[DefineOptimization]] skip its attempt to determine whether the model is linear or quadratic. |
| − | |||
| − | |||
| + | == See Also == | ||
* [[DefineOptimization]] | * [[DefineOptimization]] | ||
| + | * [[Analytica Optimizer Guide]] | ||
Latest revision as of 22:43, 16 March 2016
Example error messages
The first constraint specified in the linear optimization problem defined in 'My_opt' is non-linear:
(((3*(X^2)) + 4) <= 8)
The linear optimization defined in 'My_opt' contains the non-linear constraint 'Inventory_requirement'.
The non-linearity was first detected in 'Variable Customer_growth_pot'.
The linear optimization defined in 'Portfolio_QP' contains a non-linear constraint 'Risk_thresh'.
Cause
In the specification of your optimization problem using DefineOptimization, you have specified the type of optimization problem in the «type» parameter. When DefineOptimization analyzed your model, it found that a constraint was not of the specified type. For example, if you declare type: "LP", but a constraint turns out to be a non-linear function of the decision variables, than this message results.
In some cases, Analytica may conclude that a constraint is non-linear or non-quadratic when intermediate computations involve non-quadratic, or potentially non-quadratic, operations. A simple example would be:
(x^3 - 7) - x^3
Although this is a quadratic relationship of the decision variable x, an intermediate involves a non-quadratic (x^3), and hence Analytica will conclude that the relationship is not quadratic.
Remedy
DefineOptimization allows you to specify the «type» explicitly so that you can catch cases where non-linearity or non-quadraticity is accidentally introduced into the model. Since linear and quadratic problems usually solve faster and more reliably, odds are that you'll want to remove the non-linearity or non-quadratic operation.
If you decide to relax your optimization, say to a non-linear optimization, you can either remove the «type» parameter entirely from your call to DefineOptimization, or set it to type: "NLP". If you set it type: "NLP", you can save time by allowing DefineOptimization skip its attempt to determine whether the model is linear or quadratic.
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