org.ejml

Class LinearSolverToSparse<D extends Matrix>

java.lang.Object

org.ejml.LinearSolverToSparse<D>

All Implemented Interfaces:

LinearSolver<D,D>, LinearSolverSparse<D,D>

public class LinearSolverToSparse<D extends Matrix>
extends java.lang.Object
implements LinearSolverSparse<D,D>

Wrapper which allows a regular linear solver to act like a sparse solver

Constructor Summary

Constructors

Constructor and Description
LinearSolverToSparse(LinearSolverDense<D> solver)

Method Summary

Modifier and Type	Method and Description
<D1 extends DecompositionInterface> D1	getDecomposition() If a decomposition class was used internally then this will return that class.
boolean	isStructureLocked() Checks to see if the structure is locked.
boolean	modifiesA() Returns true if the passed in matrix to LinearSolver.setA(Matrix) is modified.
boolean	modifiesB() Returns true if the passed in 'B' matrix to LinearSolver.solve(Matrix, Matrix) is modified.
double	quality() Returns a very quick to compute measure of how singular the system is.
boolean	setA(D A) Specifies the A matrix in the linear equation.
void	setStructureLocked(boolean locked) Save results from structural analysis step.
void	solve(D B, D X) Solves for X in the linear system, A*X=B.
void	solveSparse(D B, D X) Solve against sparse matrices.

Methods inherited from class java.lang.Object

clone, equals, finalize, getClass, hashCode, notify, notifyAll, toString, wait, wait, wait

Constructor Detail

LinearSolverToSparse

public LinearSolverToSparse(LinearSolverDense<D> solver)

Method Detail

solveSparse

public void solveSparse(D B,
                        D X)

Description copied from interface: LinearSolverSparse

Solve against sparse matrices. A*X=B. In most situations its more desirable to solve against a dense matrix because of fill in.

Specified by:

solveSparse in interface LinearSolverSparse<D extends Matrix,D extends Matrix>

Parameters:

B - Input. Never modified.

X - Output. Never modified.

setStructureLocked

public void setStructureLocked(boolean locked)

Description copied from interface: LinearSolverSparse

Save results from structural analysis step. This can reduce computations of a matrix with the exactly same non-zero pattern is decomposed in the future. If a matrix has yet to be processed then the structure of the next matrix is saved. If a matrix has already been processed then the structure of the most recently processed matrix will be saved.

Specified by:

setStructureLocked in interface LinearSolverSparse<D extends Matrix,D extends Matrix>

isStructureLocked

public boolean isStructureLocked()

Description copied from interface: LinearSolverSparse

Checks to see if the structure is locked.

Specified by:

isStructureLocked in interface LinearSolverSparse<D extends Matrix,D extends Matrix>

Returns:

true if locked or false if not locked.

setA

public boolean setA(D A)

Description copied from interface: LinearSolver

Specifies the A matrix in the linear equation. A reference might be saved and it might also be modified depending on the implementation. If it is modified then LinearSolver.modifiesA() will return true.

If this value returns true that does not guarantee a valid solution was generated. This is because some decompositions don't detect singular matrices.

Specified by:

setA in interface LinearSolver<D extends Matrix,D extends Matrix>

Parameters:

A - The 'A' matrix in the linear equation. Might be modified or save the reference.

Returns:

true if it can be processed.

quality

public double quality()

Description copied from interface: LinearSolver

Returns 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:

quality in interface LinearSolver<D extends Matrix,D extends Matrix>

Returns:

The quality of the linear system.

solve

public void solve(D B,
                  D X)

Description copied from interface: LinearSolver

Solves for X in the linear system, A*X=B.

In some implementations 'B' and 'X' can be the same instance of a variable. Call LinearSolver.modifiesB() to determine if 'B' is modified.

Specified by:

solve in interface LinearSolver<D extends Matrix,D extends Matrix>

Parameters:

B - A matrix ℜ ^{m × p}. Might be modified.

X - A matrix ℜ ^{n × p}, where the solution is written to. Modified.

modifiesA

public boolean modifiesA()

Description copied from interface: LinearSolver

Returns true if the passed in matrix to LinearSolver.setA(Matrix) is modified.

Specified by:

modifiesA in interface LinearSolver<D extends Matrix,D extends Matrix>

Returns:

true if A is modified in setA().

modifiesB

public boolean modifiesB()

Description copied from interface: LinearSolver

Returns true if the passed in 'B' matrix to LinearSolver.solve(Matrix, Matrix) is modified.

Specified by:

modifiesB in interface LinearSolver<D extends Matrix,D extends Matrix>

Returns:

true if B is modified in solve(B,X).

getDecomposition

public <D1 extends DecompositionInterface> D1 getDecomposition()

Description copied from interface: LinearSolver

If 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:

getDecomposition in interface LinearSolver<D extends Matrix,D extends Matrix>

Type Parameters:

D1 - Decomposition type

Returns:

Internal decomposition class. If there is none then null.