Moore–Penrose pseudoinverse: Difference between revisions
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{{Wikipedia| Moore–Penrose pseudoinverse }} | |||
The '''Moore–Penrose pseudoinverse''', denoted ''A''{{+}}, is a generalization of the {{w|invertible matrix|inverse matrix}} that can be used to compute {{w|least squares}} solutions for overdetermined {{w|system of linear equations|systems of linear equations}}. | |||
Some of | To name a few of its properties: | ||
* If ''A'' is square and invertible, then its pseudoinverse is equal to its inverse; that is, {{nowrap|''A''{{+}} {{=}} ''A''{{inv}}}} | |||
* If ''A'' has rational entries, so does ''A''{{+}} | |||
* {{nowrap|(''A''{{+}}){{+}} {{=}} ''A''}} | |||
* {{nowrap|(''A''{{t}}){{+}} {{=}} (''A''{{+}}){{t}}}}, where ''A''{{t}} is the transpose of ''A'' | |||
* ''AA''{{+}} is the orthogonal projection matrix that maps onto the space spanned by the columns of ''A'' | |||
* ''A''{{+}}''A'' is the orthogonal projection matrix that maps onto the space spanned by the rows of ''A'' | |||
* {{nowrap|''I'' − ''A''{{+}}''A''}}, where ''I'' is the identity matrix, is the orthogonal projection matrix that maps onto the kernel, or null space, of ''A'' | |||
* If the rows of ''A'' are linearly independent, then {{nowrap|''A''{{+}} {{=}} ''A''{{t}}(''AA''{{+}}){{inv}}}}. This means the pseudoinverse can be found in this important special case by people who don't have a pseudoinverse routine available by using a matrix inverse routine. | |||
* ''uA''{{+}} is the nearest point to ''u'' in the subspace spanned by the rows of ''A''; ''A''{{+}}''v'' is the nearest point to ''v'' in the space spanned by the columns of ''A''. | |||
Some of the properties are explained in the following pages: | |||
* [[Dave Keenan & Douglas Blumeyer's guide to RTT/Tuning computation#Pseudoinverse_method_for_.5Bmath.5D.F0.9D.91.9D.3D2.5B.2Fmath.5D|Dave Keenan & Douglas Blumeyer's guide to RTT/Tuning computation#Pseudoinverse method]] | * [[Dave Keenan & Douglas Blumeyer's guide to RTT/Tuning computation#Pseudoinverse_method_for_.5Bmath.5D.F0.9D.91.9D.3D2.5B.2Fmath.5D|Dave Keenan & Douglas Blumeyer's guide to RTT/Tuning computation#Pseudoinverse method]] | ||
* [[Generator embedding optimization#Pseudoinverse: the "how"]] | * [[Generator embedding optimization #Pseudoinverse: the "how"]] | ||
* [[Generator embedding optimization#Pseudoinverse: the "why"]] | * [[Generator embedding optimization #Pseudoinverse: the "why"]] | ||
[[Category: | [[Category:Math]] |
Latest revision as of 10:55, 12 June 2025
The Moore–Penrose pseudoinverse, denoted A+, is a generalization of the inverse matrix that can be used to compute least squares solutions for overdetermined systems of linear equations.
To name a few of its properties:
- If A is square and invertible, then its pseudoinverse is equal to its inverse; that is, A+ = A−1
- If A has rational entries, so does A+
- (A+)+ = A
- (A T)+ = (A+) T, where A T is the transpose of A
- AA+ is the orthogonal projection matrix that maps onto the space spanned by the columns of A
- A+A is the orthogonal projection matrix that maps onto the space spanned by the rows of A
- I − A+A, where I is the identity matrix, is the orthogonal projection matrix that maps onto the kernel, or null space, of A
- If the rows of A are linearly independent, then A+ = A T(AA+)−1. This means the pseudoinverse can be found in this important special case by people who don't have a pseudoinverse routine available by using a matrix inverse routine.
- uA+ is the nearest point to u in the subspace spanned by the rows of A; A+v is the nearest point to v in the space spanned by the columns of A.
Some of the properties are explained in the following pages: