Interface FieldDecompositionSolver<T extends FieldElement<T>>
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- Type Parameters:
- T- the type of the field elements
 
 public interface FieldDecompositionSolver<T extends FieldElement<T>>Interface handling decomposition algorithms that can solve A × X = B.Decomposition algorithms decompose an A matrix has a product of several specific matrices from which they can solve A × X = B in least squares sense: they find X such that ||A × X - B|| is minimal. Some solvers like LUDecompositioncan only find the solution for square matrices and when the solution is an exact linear solution, i.e. when ||A × X - B|| is exactly 0. Other solvers can also find solutions with non-square matrix A and with non-null minimal norm. If an exact linear solution exists it is also the minimal norm solution.- Since:
- 2.0
- Version:
- $Revision: 781122 $ $Date: 2009-06-02 20:53:23 +0200 (mar. 02 juin 2009) $
 
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Method SummaryAll Methods Instance Methods Abstract Methods Modifier and Type Method Description FieldMatrix<T>getInverse()Get the inverse (or pseudo-inverse) of the decomposed matrix.booleanisNonSingular()Check if the decomposed matrix is non-singular.FieldMatrix<T>solve(FieldMatrix<T> b)Solve the linear equation A × X = B for matrices A.FieldVector<T>solve(FieldVector<T> b)Solve the linear equation A × X = B for matrices A.T[]solve(T[] b)Solve the linear equation A × X = B for matrices A.
 
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Method Detail- 
solveT[] solve(T[] b) throws java.lang.IllegalArgumentException, InvalidMatrixException Solve the linear equation A × X = B for matrices A.The A matrix is implicit, it is provided by the underlying decomposition algorithm. - Parameters:
- b- right-hand side of the equation A × X = B
- Returns:
- a vector X that minimizes the two norm of A × X - B
- Throws:
- java.lang.IllegalArgumentException- if matrices dimensions don't match
- InvalidMatrixException- if decomposed matrix is singular
 
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solveFieldVector<T> solve(FieldVector<T> b) throws java.lang.IllegalArgumentException, InvalidMatrixException Solve the linear equation A × X = B for matrices A.The A matrix is implicit, it is provided by the underlying decomposition algorithm. - Parameters:
- b- right-hand side of the equation A × X = B
- Returns:
- a vector X that minimizes the two norm of A × X - B
- Throws:
- java.lang.IllegalArgumentException- if matrices dimensions don't match
- InvalidMatrixException- if decomposed matrix is singular
 
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solveFieldMatrix<T> solve(FieldMatrix<T> b) throws java.lang.IllegalArgumentException, InvalidMatrixException Solve the linear equation A × X = B for matrices A.The A matrix is implicit, it is provided by the underlying decomposition algorithm. - Parameters:
- b- right-hand side of the equation A × X = B
- Returns:
- a matrix X that minimizes the two norm of A × X - B
- Throws:
- java.lang.IllegalArgumentException- if matrices dimensions don't match
- InvalidMatrixException- if decomposed matrix is singular
 
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isNonSingularboolean isNonSingular() Check if the decomposed matrix is non-singular.- Returns:
- true if the decomposed matrix is non-singular
 
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getInverseFieldMatrix<T> getInverse() throws InvalidMatrixException Get the inverse (or pseudo-inverse) of the decomposed matrix.- Returns:
- inverse matrix
- Throws:
- InvalidMatrixException- if decomposed matrix is singular
 
 
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