+ /** Let the system choose. A heuristics is applied for automatic
+ * determination of a suitable algorithm. */
+ automatic,
+ /** Gauss elimination. If \f$m_{i,j}^{(0)}\f$ are the entries of the
+ * original matrix, then the matrix is transformed into triangular
+ * form by applying the rules
+ * \f[
+ * m_{i,j}^{(k+1)} = m_{i,j}^{(k)} - m_{i,k}^{(k)} m_{k,j}^{(k)} / m_{k,k}^{(k)}
+ * \f]
+ * This algorithm is well-suited for numerical matrices but generally
+ * suffers from the expensive division (and computation of GCDs) at
+ * each step. */
+ gauss,
+ /** Division-free elimination. This is a modification of Gauss
+ * elimination where the division by the pivot element is not
+ * carried out. If \f$m_{i,j}^{(0)}\f$ are the entries of the
+ * original matrix, then the matrix is transformed into triangular
+ * form by applying the rules
+ * \f[
+ * m_{i,j}^{(k+1)} = m_{i,j}^{(k)} m_{k,k}^{(k)} - m_{i,k}^{(k)} m_{k,j}^{(k)}
+ * \f]
+ * This algorithm is only there for the purpose of cross-checks.
+ * It suffers from exponential intermediate expression swell. Use it
+ * only for small systems. */
+ divfree,
+ /** Bareiss fraction-free elimination. This is a modification of
+ * Gauss elimination where the division by the pivot element is
+ * <EM>delayed</EM> until it can be carried out without computing
+ * GCDs. If \f$m_{i,j}^{(0)}\f$ are the entries of the original
+ * matrix, then the matrix is transformed into triangular form by
+ * applying the rules
+ * \f[
+ * m_{i,j}^{(k+1)} = (m_{i,j}^{(k)} m_{k,k}^{(k)} - m_{i,k}^{(k)} m_{k,j}^{(k)}) / m_{k-1,k-1}^{(k-1)}
+ * \f]
+ * (We have set \f$m_{-1,-1}^{(-1)}=1\f$ in order to avoid a case
+ * distinction in above formula.) It can be shown that nothing more
+ * than polynomial long division is needed for carrying out the
+ * division. This is generally the fastest algorithm for solving
+ * linear systems. In contrast to division-free elimination it only
+ * has a linear expression swell. For two-dimensional systems, the
+ * two algorithms are equivalent, however. */
+ bareiss,
+ /** Markowitz-ordered Gaussian elimination. Same as the usual
+ * Gaussian elimination, but with additional effort spent on
+ * selecting pivots that minimize fill-in. Faster than the
+ * methods above for large sparse matrices (particularly with
+ * symbolic coefficients), otherwise slightly slower than
+ * Gaussian elimination.
+ */
+ markowitz