Matrix Inverse: Difference between revisions

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{{Built-in|Matrix Inverse|⌹}} is a [[monadic]] [[primitive function]] that returns the [[wikipedia:matrix inverse|inverse]] of a [[simple]] [[numeric]] array of [[rank]] 2 or lower. Some dialects automatically apply it to rank-2 [[subarray]]s of higher-rank [[argument]]s. It shares the [[glyph]] ''Quad Divide'' <source lang=apl inline>⌹</source> (often called ''Domino'') with the dyadic function [[Matrix Divide]].
{{Built-in|Matrix Inverse|⌹}} is a [[monadic]] [[primitive function]] that returns the [[wikipedia:matrix inverse|inverse]] of a [[simple]] [[numeric]] array of [[rank]] 2 or lower. Some dialects automatically apply it to rank-2 [[subarray]]s of higher-rank [[argument]]s. It shares the [[glyph]] ''Quad Divide'' <source lang=apl inline>⌹</syntaxhighlight> (often called ''Domino'') with the dyadic function [[Matrix Divide]].


== Examples ==
== Examples ==
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  ⌹2 2⍴0     
  ⌹2 2⍴0     
  ∧     
  ∧     
</source>
</syntaxhighlight>


When the argument is a [[scalar]] or [[vector]], or the given matrix has more rows than columns (<source lang=apl inline>r>c</source> where <source lang=apl inline>r c≡⍴X</source>), Matrix Inverse computes specific forms of generalized inverse called [[wikipedia:Moore-Penrose inverse|Moore-Penrose inverse]]. For a scalar, the result is the [[reciprocal]] of the argument; for a vector, the result equals <source lang=apl inline>(+X)÷X+.×+X</source>. For a non-square matrix, the result equals <source lang=apl inline>(+⍉X)⌹(+⍉X)+.×X</source> (where <source lang=apl inline>+⍉X</source> is the [[wikipedia:conjugate transpose|conjugate transpose]] of X).
When the argument is a [[scalar]] or [[vector]], or the given matrix has more rows than columns (<source lang=apl inline>r>c</syntaxhighlight> where <source lang=apl inline>r c≡⍴X</syntaxhighlight>), Matrix Inverse computes specific forms of generalized inverse called [[wikipedia:Moore-Penrose inverse|Moore-Penrose inverse]]. For a scalar, the result is the [[reciprocal]] of the argument; for a vector, the result equals <source lang=apl inline>(+X)÷X+.×+X</syntaxhighlight>. For a non-square matrix, the result equals <source lang=apl inline>(+⍉X)⌹(+⍉X)+.×X</syntaxhighlight> (where <source lang=apl inline>+⍉X</syntaxhighlight> is the [[wikipedia:conjugate transpose|conjugate transpose]] of X).


<source lang=apl>
<source lang=apl>
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  1.0000E000J¯5.5511E¯17 0
  1.0000E000J¯5.5511E¯17 0
¯2.7756E¯17J05.5511E¯17 1
¯2.7756E¯17J05.5511E¯17 1
</source>
</syntaxhighlight>


== External links ==
== External links ==
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* [http://microapl.com/apl_help/ch_020_020_270.htm APLX]
* [http://microapl.com/apl_help/ch_020_020_270.htm APLX]
* [http://wiki.nars2000.org/index.php/Matrix_Inverse/Divide NARS2000]
* [http://wiki.nars2000.org/index.php/Matrix_Inverse/Divide NARS2000]
* J [https://www.jsoftware.com/help/dictionary/d131.htm Dictionary], [https://code.jsoftware.com/wiki/Vocabulary/percentdot NuVoc] (as <source lang=j inline>%.</source>)
* J [https://www.jsoftware.com/help/dictionary/d131.htm Dictionary], [https://code.jsoftware.com/wiki/Vocabulary/percentdot NuVoc] (as <source lang=j inline>%.</syntaxhighlight>)


{{APL built-ins}}[[Category:Primitive functions]]
{{APL built-ins}}[[Category:Primitive functions]]