Class CholeskyOuterSolver_MT_FDRB
- All Implemented Interfaces:
LinearSolver<FMatrixRBlock,,FMatrixRBlock> LinearSolverDense<FMatrixRBlock>
Linear solver that uses a block cholesky decomposition.
Solver works by using the standard Cholesky solving strategy:
A=L*LT
A*x=b
L*LT*x = b
L*y = b
LT*x = y
x = L-Ty
It is also possible to use the upper triangular cholesky decomposition.
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Constructor Summary
Constructors -
Method Summary
Modifier and TypeMethodDescriptionIf a decomposition class was used internally then this will return that class.voidinvert(FMatrixRBlock A_inv) Computes the inverse of of the 'A' matrix passed intoLinearSolver.setA(Matrix)and writes the results to the provided matrix.booleanReturns true if the passed in matrix toLinearSolver.setA(Matrix)is modified.booleanReturns true if the passed in 'B' matrix toLinearSolver.solve(Matrix, Matrix)is modified.doublequality()Returns a very quick to compute measure of how singular the system is.booleanDecomposes and overwrites the input matrix.voidsolve(FMatrixRBlock B, @Nullable FMatrixRBlock X) If X == null then the solution is written into B.
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Constructor Details
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CholeskyOuterSolver_MT_FDRB
public CholeskyOuterSolver_MT_FDRB()
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Method Details
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setA
Decomposes and overwrites the input matrix.- Specified by:
setAin interfaceLinearSolver<FMatrixRBlock,FMatrixRBlock> - Parameters:
A- Semi-Positive Definite (SPD) system matrix. Modified. Reference saved.- Returns:
- If the matrix can be decomposed. Will always return false of not SPD.
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quality
public double quality()Description copied from interface:LinearSolverReturns a very quick to compute measure of how singular the system is. This measure will be invariant to the scale of the matrix and always be positive, with larger values indicating it is less singular. If not supported by the solver then the runtime exception IllegalArgumentException is thrown. This is NOT the matrix's condition.
How this function is implemented is not specified. One possible implementation is the following: In many decompositions a triangular matrix is extracted. The determinant of a triangular matrix is easily computed and once normalized to be scale invariant and its absolute value taken it will provide functionality described above.
- Specified by:
qualityin interfaceLinearSolver<FMatrixRBlock,FMatrixRBlock> - Returns:
- The quality of the linear system.
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solve
If X == null then the solution is written into B. Otherwise the solution is copied from B into X.- Specified by:
solvein interfaceLinearSolver<FMatrixRBlock,FMatrixRBlock> - Parameters:
B- A matrix ℜ m × p. Might be modified.X- A matrix ℜ n × p, where the solution is written to. Modified.
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invert
Description copied from interface:LinearSolverDenseComputes the inverse of of the 'A' matrix passed intoLinearSolver.setA(Matrix)and writes the results to the provided matrix. If 'A_inv' needs to be different from 'A' is implementation dependent.- Specified by:
invertin interfaceLinearSolverDense<FMatrixRBlock>- Parameters:
A_inv- Where the inverted matrix saved. Modified.
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modifiesA
public boolean modifiesA()Description copied from interface:LinearSolverReturns true if the passed in matrix toLinearSolver.setA(Matrix)is modified.- Specified by:
modifiesAin interfaceLinearSolver<FMatrixRBlock,FMatrixRBlock> - Returns:
- true if A is modified in setA().
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modifiesB
public boolean modifiesB()Description copied from interface:LinearSolverReturns true if the passed in 'B' matrix toLinearSolver.solve(Matrix, Matrix)is modified.- Specified by:
modifiesBin interfaceLinearSolver<FMatrixRBlock,FMatrixRBlock> - Returns:
- true if B is modified in solve(B,X).
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getDecomposition
Description copied from interface:LinearSolverIf a decomposition class was used internally then this will return that class. Most linear solvers decompose the input matrix into a more simplistic form. However some solutions do not require decomposition, e.g. inverse by minor.- Specified by:
getDecompositionin interfaceLinearSolver<FMatrixRBlock,FMatrixRBlock> - Returns:
- Internal decomposition class. If there is none then null.
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