public class SparseFloatQRDecomposition extends Object
The QR decompostion always exists, even if the matrix does not have full rank. The primary use of the QR decomposition is in the least squares solution of nonsquare systems of simultaneous linear equations. This will fail if isFullRank() returns false.
Constructor and Description |
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SparseFloatQRDecomposition(FloatMatrix2D A,
int order)
Constructs and returns a new QR decomposition object; computed by
Householder reflections; If m < n then then the QR of A' is computed.
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Modifier and Type | Method and Description |
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float[] |
getBeta()
Returns a copy of the beta factors, from the Householder reflections H =
I - beta*v*v'.
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FloatMatrix2D |
getR()
Returns a copy of the upper triangular factor, R.
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edu.emory.mathcs.csparsej.tfloat.Scs_common.Scss |
getSymbolicAnalysis()
Returns a copy of the symbolic QR analysis object
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FloatMatrix2D |
getV()
Returns a copy of the Householder vectors v, from the Householder
reflections H = I - beta*v*v'.
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boolean |
hasFullRank()
Returns whether the matrix A has full rank.
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void |
solve(FloatMatrix1D b)
Solve a least-squares problem (min ||Ax-b||_2, where A is m-by-n with m
>= n) or underdetermined system (Ax=b, where m < n).
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public SparseFloatQRDecomposition(FloatMatrix2D A, int order)
A
- A rectangular matrix.order
- ordering option (0 to 3); 0: natural ordering, 1: amd(A+A'),
2: amd(S'*S), 3: amd(A'*A)IllegalArgumentException
- if A is not sparseIllegalArgumentException
- if order is not in [0,3]public FloatMatrix2D getV()
public float[] getBeta()
public FloatMatrix2D getR()
public edu.emory.mathcs.csparsej.tfloat.Scs_common.Scss getSymbolicAnalysis()
public boolean hasFullRank()
public void solve(FloatMatrix1D b)
b
- right-hand side.IllegalArgumentException
- if b.size() != max(A.rows(), A.columns()).IllegalArgumentException
- if !this.hasFullRank() (A is rank
deficient).Jump to the Parallel Colt Homepage