Examples of org.ejml.alg.dense.linsol.AdjustableLinearSolver

org.ejml.alg.dense.linsol.AdjustableLinearSolver
In many situations solutions to linear systems that share many of the same data points are needed. This can happen when solving using the most recent data or when rejecting outliers. In these situations it is possible to solve these related systems much faster than solving the entire data set again. @see org.ejml.factory.LinearSolver @author Peter Abeles

    public void adjustable() {
        DenseMatrix64F A = RandomMatrices.createRandom(5,4,rand);
        DenseMatrix64F x = RandomMatrices.createRandom(4,1,rand);
        DenseMatrix64F y = new DenseMatrix64F(5,1);


        AdjustableLinearSolver solver = LinearSolverFactory.adjustable();


        standardTest(A, x, y, solver);


        // remove the last observation
        solver.removeRowFromA(y.numRows-1);


        // compute the adjusted solution
        y.numRows--;
        DenseMatrix64F x_adj = new DenseMatrix64F(4,1);
        solver.solve(y,x_adj);


        // The solution should still be the same
        assertTrue(MatrixFeatures.isIdentical(x,x_adj,1e-8));
    }

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        DenseMatrix64F Y = new DenseMatrix64F(A_e.numRows,X.numCols);
        CommonOps.mult(A_e,X,Y);


        // create the solver from A then add a A.  The solver
        // should be equivalent to one created from A_e
        AdjustableLinearSolver adjSolver = new AdjLinearSolverQr();


        assertTrue(adjSolver.setA(A));
        adjSolver.addRowToA(row,insert);


        // solve the system and see if it gets the expected solution
        DenseMatrix64F X_found = RandomMatrices.createRandom(X.numRows,X.numCols,rand);
        adjSolver.solve(Y,X_found);


        // see if they produce the same results
        assertTrue(MatrixFeatures.isIdentical(X_found,X,1e-8));
    }

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        DenseMatrix64F X = RandomMatrices.createRandom(n,2,rand);
        DenseMatrix64F Y = new DenseMatrix64F(A_e.numRows,X.numCols);
        CommonOps.mult(A_e,X,Y);


        // create the solver from the original system then modify it
        AdjustableLinearSolver adjSolver = new AdjLinearSolverQr();


        adjSolver.setA(A);
        adjSolver.removeRowFromA(remove);


        // see if it produces the epected results


        // solve the system and see if it gets the expected solution
        DenseMatrix64F X_found = RandomMatrices.createRandom(X.numRows,X.numCols,rand);
        adjSolver.solve(Y,X_found);


        // see if they produce the same results
        assertTrue(MatrixFeatures.isIdentical(X_found,X,1e-8));
    }

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Related Classes of org.ejml.alg.dense.linsol.AdjustableLinearSolver

org.ejml.alg.dense.linsol.qr.TestAdjLinearSolverQr

org.ejml.factory.TestLinearSolverFactory

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