Examples of org.ejml.factory.LinearSolver

org.ejml.factory.LinearSolver

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 {@link #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:

Call {@link #setA(org.ejml.data.Matrix64F)}
Call {@link #solve(org.ejml.data.Matrix64F,org.ejml.data.Matrix64F)}.

To invert a matrix:

Call {@link #setA(org.ejml.data.Matrix64F)}
Call {@link #invert(org.ejml.data.Matrix64F)}.

A matrix can also be inverted by passing in an identity matrix to solve, but this will be slower and more memory intensive than the specialized invert() function.

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 {@link org.ejml.alg.dense.linsol.LinearSolverSafe} can be used. Thefunctions {@link #modifiesA()} and {@link #modifiesB()} specify which input matrices are beingmodified.

@author Peter Abeles

    public void invert() {
        DenseMatrix64F A = new DenseMatrix64F(3,3, true, 0, 1, 2, -2, 4, 9, 0.5, 0, 5);
        DenseMatrix64F A_inv = RandomMatrices.createRandom(3,3,rand);


        LUDecompositionAlt decomp = new LUDecompositionAlt();
        LinearSolver solver = new LinearSolverLu(decomp);


        solver.setA(A);
        InvertUsingSolve.invert(solver,A,A_inv);


        DenseMatrix64F I = RandomMatrices.createRandom(3,3,rand);


        CommonOps.mult(A,A_inv,I);

     * @return If it converged or not.
     */
    public boolean computeShiftInvert( DenseMatrix64F A , double alpha ) {
        initPower(A);


        LinearSolver solver = LinearSolverFactory.linear(A.numCols);


        SpecializedOps.addIdentity(A,B,-alpha);
        solver.setA(B);


        boolean converged = false;


        for( int i = 0; i < maxIterations && !converged; i++ ) {
            solver.solve(q0,q1);
            double s = NormOps.normPInf(q1);
            CommonOps.divide(s,q1,q2);


            converged = checkConverged(A);
        }

Related Classes of org.ejml.factory.LinearSolver

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