namespace GiNaC {
+/** Flags to control the behavior of expand(). */
class expand_options {
public:
enum {
};
};
+/** Flags to control the behavior of subs(). */
+class subs_options {
+public:
+ enum {
+ subs_no_pattern = 0x0001,
+ subs_algebraic = 0x0002
+ };
+};
+
/** Flags to control series expansion. */
class series_options {
public:
enum {
+ /** Suppress branch cuts in series expansion. Branch cuts manifest
+ * themselves as step functions, if this option is not passed. If
+ * it is passed and expansion at a point on a cut is performed, then
+ * the analytic continuation of the function is expanded. */
suppress_branchcut = 0x0001
};
};
class determinant_algo {
public:
enum {
- automatic, ///< Let the system choose
- gauss, ///< Gauss elimiation
- divfree, ///< Division-free elimination
- laplace, ///< Laplace (or minor) elimination
- bareiss ///< Bareiss fraction-free elimination
+ /** 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]
+ * The determinant is then just the product of diagonal elements.
+ * Choose this algorithm only for purely numerical matrices. */
+ 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]
+ * The determinant can later be computed by inspecting the diagonal
+ * elements only. This algorithm is only there for the purpose of
+ * cross-checks. It is never fast. */
+ divfree,
+ /** Laplace elimination. This is plain recursive elimination along
+ * minors although multiple minors are avoided by the algorithm.
+ * Although the algorithm is exponential in complexity it is
+ * frequently the fastest one when the matrix is populated by
+ * complicated symbolic expressions. */
+ laplace,
+ /** 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. The determinant can then be read of from the lower
+ * right entry. This algorithm is rarely fast for computing
+ * determinants. */
+ bareiss
};
};
class solve_algo {
public:
enum {
- automatic, ///< Let the system choose
- gauss, ///< Gauss elimiation
- divfree, ///< Division-free elimination
- bareiss ///< Bareiss fraction-free elimination
+ /** 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
};
};
git://www.ginac.de / ginac.git / blobdiff