public interface LinearSolver<T extends Matrix>
An implementation of LinearSolver solves a linear system or inverts a matrix. It masks more complex
implementation details, while giving the programmer control over memory management and performance.
To quickly detect nearly singular matrices without computing the SVD the quality()
function is provided.
A linear system is defined as:
A*X = B.
where A ∈ ℜ m × n, X ∈ ℜ n × p,
B ∈ ℜ m × p. Different implementations can solve different
types and shapes in input matrices and have different memory and runtime performance.
To solve a system:
To invert a matrix:
IMPORTANT: Depending upon the implementation, input matrices might be overwritten by
the solver. This
reduces memory and computational requirements and give more control to the programmer. If
the input matrices need to be not modified then LinearSolverSafe can be used. The
functions modifiesA() and modifiesB() specify which input matrices are being
modified.
| Modifier and Type | Method and Description |
|---|---|
<D extends DecompositionInterface> |
getDecomposition()
If a decomposition class was used internally then this will return that class.
|
void |
invert(T A_inv)
Computes the inverse of of the 'A' matrix passed into
setA(org.ejml.data.Matrix)
and writes the results to the provided matrix. |
boolean |
modifiesA()
Returns true if the passed in matrix to
setA(org.ejml.data.Matrix)
is modified. |
boolean |
modifiesB()
Returns true if the passed in 'B' matrix to
solve(org.ejml.data.Matrix, org.ejml.data.Matrix)
is modified. |
double |
quality()
Returns a very quick to compute measure of how singular the system is.
|
boolean |
setA(T A)
Specifies the A matrix in the linear equation.
|
void |
solve(T B,
T X)
Solves for X in the linear system, A*X=B.
|
boolean setA(T A)
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 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.
A - The 'A' matrix in the linear equation. Might be modified or save the reference.double quality()
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.
void solve(T B, T X)
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
modifiesB() to determine if 'B' is modified.
B - A matrix ℜ m × p. Might be modified.X - A matrix ℜ n × p, where the solution is written to. Modified.void invert(T A_inv)
setA(org.ejml.data.Matrix)
and writes the results to the provided matrix. If 'A_inv' needs to be different from 'A'
is implementation dependent.A_inv - Where the inverted matrix saved. Modified.boolean modifiesA()
setA(org.ejml.data.Matrix)
is modified.boolean modifiesB()
solve(org.ejml.data.Matrix, org.ejml.data.Matrix)
is modified.<D extends DecompositionInterface> D getDecomposition()
D - Decomposition type