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matrix/ai.dragonfly.math.matrix.decomposition/QR

QR

ai.dragonfly.math.matrix.decomposition.QR
See theQR companion object
class QR[M <: Int, N <: Int]

QR Decomposition, computed by Householder reflections. Structure to access R and the Householder vectors and compute Q.

Attributes

A

Rectangular matrix

Companion:
object
Source:
QR.scala
Graph
Supertypes
class Object
trait Matchable
class Any

Members list

Concise view

Value members

Concrete methods

def H: Matrix[M, N]

Return the Householder vectors

Return the Householder vectors

Attributes

Returns:

Lower trapezoidal matrix whose columns define the reflections

Source:
QR.scala
def Q: Matrix[M, N]

Generate and return the (economy-sized) orthogonal factor

Generate and return the (economy-sized) orthogonal factor

Attributes

Returns:

Q

Source:
QR.scala
def R: Matrix[N, N]

Return the upper triangular factor

Return the upper triangular factor

Attributes

Returns:

R

Source:
QR.scala
def isFullRank: Boolean

Is the matrix full rank?

Is the matrix full rank?

Attributes

Returns:

true if R, and hence A, has full rank.

Source:
QR.scala
def solve[V <: Int](b: Matrix[M, V])(using ValueOf[V]): Matrix[N, V]

Least squares solution of A*X = B

Least squares solution of A*X = B

Attributes

b

A Matrix with as many rows as A and any number of columns.

Returns:

X that minimizes the two norm of QRX-B.

Throws:
IllegalArgumentException

Matrix row dimensions must agree.

RuntimeException

Matrix is rank deficient.

Source:
QR.scala

Concrete fields

val QR: Matrix[M, N]

Attributes

Source:
QR.scala
val Rdiag: Vec[N]

Attributes

Source:
QR.scala
val columns: Int

Attributes

Source:
QR.scala
val rows: Int

Attributes

Source:
QR.scala
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