Exemplo n.º 1
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        /// <summary>
        /// Solve a square Toeplitz system with a right-side matrix.
        /// </summary>
        /// <param name="col">The left-most column of the Toeplitz matrix.</param>
        /// <param name="row">The top-most row of the Toeplitz matrix.</param>
        /// <param name="Y">The right-side matrix of the system.</param>
        /// <returns>The solution matrix.</returns>
        /// <exception cref="ArgumentNullException">
        /// <EM>col</EM> is a null reference,
        /// <para>or</para>
        /// <para><EM>row</EM> is a null reference,</para>
        /// <para>or</para>
        /// <para><EM>Y</EM> is a null reference.</para>
        /// </exception>
        /// <exception cref="RankException">
        /// The length of <EM>col</EM> is 0,
        /// <para>or</para>
        /// <para>the lengths of <EM>col</EM> and <EM>row</EM> are not equal,</para>
        /// <para>or</para>
        /// <para>the number of rows in <EM>Y</EM> does not the length of <EM>col</EM> and <EM>row</EM>.</para>
        /// </exception>
        /// <exception cref="SingularMatrixException">
        /// The Toeplitz matrix or one of the the leading sub-matrices is singular.
        /// </exception>
        /// <exception cref="ArithmeticException">
        /// The values of the first element of <EM>col</EM> and <EM>row</EM> are not equal.
        /// </exception>
        /// <remarks>
        /// This method solves the linear system <B>AX</B> = <B>Y</B>. Where
        /// <B>T</B> is a square Toeplitz matrix, <B>X</B> is an unknown
        /// matrix and <B>Y</B> is a known matrix.
        /// <para>
        /// The classic Levinson algorithm is used to solve the system. The algorithm
        /// assumes that all the leading sub-matrices of the Toeplitz matrix are
        /// non-singular. When a sub-matrix is near singular, accuracy will
        /// be degraded. This member requires approximately <B>N</B> squared
        /// FLOPS to calculate a solution, where <B>N</B> is the matrix order.
        /// </para>
        /// <para>
        /// This static method has minimal storage requirements as it combines
        /// the <b>UDL</b> decomposition with the calculation of the solution vector
        /// in a single algorithm.
        /// </para>
        /// </remarks>
        public static ComplexDoubleMatrix Solve(IROComplexDoubleVector col, IROComplexDoubleVector row, IROComplexDoubleMatrix Y)
        {
            // check parameters
            if (col == null)
            {
                throw new System.ArgumentNullException("col");
            }
            else if (col.Length == 0)
            {
                throw new RankException("The length of col is zero.");
            }
            else if (row == null)
            {
                throw new System.ArgumentNullException("row");
            }
            else if (col.Length != row.Length)
            {
                throw new RankException("The lengths of col and row are not equal.");
            }
            else if (col[0] != row[0])
            {
                throw new ArithmeticException("The values of the first element of col and row are not equal.");
            }
            else if (Y == null)
            {
                throw new System.ArgumentNullException("Y");
            }
            else if (col.Length != Y.Columns)
            {
                throw new RankException("The numer of rows in Y does not match the length of col and row.");
            }

            // check if leading diagonal is zero
            if (col[0] == Complex.Zero)
            {
                throw new SingularMatrixException("One of the leading sub-matrices is singular.");
            }

            // decompose matrix
            int order = col.Length;

            Complex[]           A = new Complex[order];
            Complex[]           B = new Complex[order];
            Complex[]           Z = new Complex[order];
            ComplexDoubleMatrix X = new ComplexDoubleMatrix(order);
            Complex             Q, S, Ke, Kr, e;
            Complex             Inner;
            int i, j, l;

            // setup the zero order solution
            A[0] = Complex.One;
            B[0] = Complex.One;
            e    = Complex.One / col[0];
            X.SetRow(0, e * ComplexDoubleVector.GetRow(Y, 0));

            for (i = 1; i < order; i++)
            {
                // calculate inner products
                Q = Complex.Zero;
                for (j = 0, l = 1; j < i; j++, l++)
                {
                    Q += col[l] * A[j];
                }

                S = Complex.Zero;
                for (j = 0, l = 1; j < i; j++, l++)
                {
                    S += row[l] * B[j];
                }

                // reflection coefficients
                Kr = -S * e;
                Ke = -Q * e;

                // update lower triangle (in temporary storage)
                Z[0] = Complex.Zero;
                Array.Copy(A, 0, Z, 1, i);
                for (j = 0, l = i - 1; j < i; j++, l--)
                {
                    Z[j] += Ke * B[l];
                }

                // update upper triangle
                for (j = i; j > 0; j--)
                {
                    B[j] = B[j - 1];
                }

                B[0] = Complex.Zero;
                for (j = 0, l = i - 1; j < i; j++, l--)
                {
                    B[j] += Kr * A[l];
                }

                // copy from temporary storage to lower triangle
                Array.Copy(Z, 0, A, 0, i + 1);

                // check for singular sub-matrix)
                if (Ke * Kr == Complex.One)
                {
                    throw new SingularMatrixException("One of the leading sub-matrices is singular.");
                }

                // update diagonal
                e = e / (Complex.One - Ke * Kr);

                for (l = 0; l < Y.Rows; l++)
                {
                    ComplexDoubleVector W = X.GetColumn(l);
                    ComplexDoubleVector M = ComplexDoubleVector.GetColumn(Y, l);

                    Inner = M[i];
                    for (j = 0; j < i; j++)
                    {
                        Inner += A[j] * M[j];
                    }
                    Inner *= e;

                    W[i] = Inner;
                    for (j = 0; j < i; j++)
                    {
                        W[j] += Inner * B[j];
                    }

                    X.SetColumn(l, W);
                }
            }

            return(X);
        }