@cindex container
@cindex atom
-To get an idea about what kinds of symbolic composits may be built we
+To get an idea about what kinds of symbolic composites may be built we
have a look at the most important classes in the class hierarchy and
some of the relations among the classes:
For storing numerical things, GiNaC uses Bruno Haible's library CLN.
The classes therein serve as foundation classes for GiNaC. CLN stands
for Class Library for Numbers or alternatively for Common Lisp Numbers.
-In order to find out more about CLN's internals the reader is refered to
+In order to find out more about CLN's internals, the reader is referred to
the documentation of that library. @inforef{Introduction, , cln}, for
more information. Suffice to say that it is by itself build on top of
another library, the GNU Multiple Precision library GMP, which is an
that in both cases you got a couple of extra digits. This is because
numbers are internally stored by CLN as chunks of binary digits in order
to match your machine's word size and to not waste precision. Thus, on
-architectures with differnt word size, the above output might even
+architectures with different word size, the above output might even
differ with regard to actually computed digits.
It should be clear that objects of class @code{numeric} should be used
There are a couple of ways to construct matrices, with or without preset
elements:
+@cindex @code{lst_to_matrix()}
+@cindex @code{diag_matrix()}
+@cindex @code{unit_matrix()}
+@cindex @code{symbolic_matrix()}
@example
matrix::matrix(unsigned r, unsigned c);
matrix::matrix(unsigned r, unsigned c, const lst & l);
ex lst_to_matrix(const lst & l);
ex diag_matrix(const lst & l);
+ex unit_matrix(unsigned x);
+ex unit_matrix(unsigned r, unsigned c);
+ex symbolic_matrix(unsigned r, unsigned c, const string & base_name);
+ex symbolic_matrix(unsigned r, unsigned c, const string & base_name, const string & tex_base_name);
@end example
The first two functions are @code{matrix} constructors which create a matrix
with @samp{r} rows and @samp{c} columns. The matrix elements can be
initialized from a (flat) list of expressions @samp{l}. Otherwise they are
all set to zero. The @code{lst_to_matrix()} function constructs a matrix
-from a list of lists, each list representing a matrix row. Finally,
-@code{diag_matrix()} constructs a diagonal matrix given the list of diagonal
-elements. Note that the last two functions return expressions, not matrix
-objects.
+from a list of lists, each list representing a matrix row. @code{diag_matrix()}
+constructs a diagonal matrix given the list of diagonal elements.
+@code{unit_matrix()} creates an @samp{x} by @samp{x} (or @samp{r} by @samp{c})
+unit matrix. And finally, @code{symbolic_matrix} constructs a matrix filled
+with newly generated symbols made of the specified base name and the
+position of each element in the matrix.
Matrix elements can be accessed and set using the parenthesis (function call)
operator:
the @code{op()} method. But C++-style subscripting with square brackets
@samp{[]} is not available.
-Here are a couple of examples that all construct the same 2x2 diagonal
-matrix:
+Here are a couple of examples of constructing matrices:
@example
@{
symbol a("a"), b("b");
- ex e;
matrix M(2, 2);
M(0, 0) = a;
M(1, 1) = b;
- e = M;
-
- e = matrix(2, 2, lst(a, 0, 0, b));
+ cout << M << endl;
+ // -> [[a,0],[0,b]]
- e = lst_to_matrix(lst(lst(a, 0), lst(0, b)));
+ cout << matrix(2, 2, lst(a, 0, 0, b)) << endl;
+ // -> [[a,0],[0,b]]
- e = diag_matrix(lst(a, b));
+ cout << lst_to_matrix(lst(lst(a, 0), lst(0, b))) << endl;
+ // -> [[a,0],[0,b]]
- cout << e << endl;
+ cout << diag_matrix(lst(a, b)) << endl;
// -> [[a,0],[0,b]]
+
+ cout << unit_matrix(3) << endl;
+ // -> [[1,0,0],[0,1,0],[0,0,1]]
+
+ cout << symbolic_matrix(2, 3, "x") << endl;
+ // -> [[x00,x01,x02],[x10,x11,x12]]
@}
@end example
The @code{matrix} class provides a couple of additional methods for
computing determinants, traces, and characteristic polynomials:
+@cindex @code{determinant()}
+@cindex @code{trace()}
+@cindex @code{charpoly()}
@example
ex matrix::determinant(unsigned algo = determinant_algo::automatic) const;
ex matrix::trace(void) const;
multiples of the unity element, even though it's customary to omit it.
E.g. instead of @code{dirac_gamma(mu)*(dirac_slash(q,4)+m)} you have to
write @code{dirac_gamma(mu)*(dirac_slash(q,4)+m*dirac_ONE())}. Otherwise,
-GiNaC may produce incorrect results.
+GiNaC will complain and/or produce incorrect results.
@cindex @code{dirac_gamma5()}
-There's a special element @samp{gamma5} that commutes with all other
-gammas and in 4 dimensions equals @samp{gamma~0 gamma~1 gamma~2 gamma~3},
-provided by
+There is a special element @samp{gamma5} that commutes with all other
+gammas, has a unit square, and in 4 dimensions equals
+@samp{gamma~0 gamma~1 gamma~2 gamma~3}, provided by
@example
ex dirac_gamma5(unsigned char rl = 0);
@end example
-@cindex @code{dirac_gamma6()}
-@cindex @code{dirac_gamma7()}
-The two additional functions
+@cindex @code{dirac_gammaL()}
+@cindex @code{dirac_gammaR()}
+The chiral projectors @samp{(1+/-gamma5)/2} are also available as proper
+objects, constructed by
@example
-ex dirac_gamma6(unsigned char rl = 0);
-ex dirac_gamma7(unsigned char rl = 0);
+ex dirac_gammaL(unsigned char rl = 0);
+ex dirac_gammaR(unsigned char rl = 0);
@end example
-return @code{dirac_ONE(rl) + dirac_gamma5(rl)} and @code{dirac_ONE(rl) - dirac_gamma5(rl)},
-respectively.
+They observe the relations @samp{gammaL^2 = gammaL}, @samp{gammaR^2 = gammaR},
+and @samp{gammaL gammaR = gammaR gammaL = 0}.
@cindex @code{dirac_slash()}
Finally, the function
Such slashed expressions are printed with a trailing backslash, e.g. @samp{e\}.
In products of dirac gammas, superfluous unity elements are automatically
-removed, squares are replaced by their values and @samp{gamma5} is
-anticommuted to the front. The @code{simplify_indexed()} function performs
-contractions in gamma strings, for example
+removed, squares are replaced by their values, and @samp{gamma5}, @samp{gammaL}
+and @samp{gammaR} are moved to the front.
+
+The @code{simplify_indexed()} function performs contractions in gamma strings,
+for example
@example
@{
are also valid patterns.
@end itemize
+@subsection Matching expressions
@cindex @code{match()}
The most basic application of patterns is to check whether an expression
matches a given pattern. This is done by the function
@{$0==x^2@}
@end example
+@subsection Matching parts of expressions
@cindex @code{has()}
A more general way to look for patterns in expressions is provided by the
member function
@end example
works a bit like @code{has()} but it doesn't stop upon finding the first
-match. Instead, it appends all found matches to the specified list. If there
-are multiple occurrences of the same expression, it is entered only once to
-the list. @code{find()} returns false if no matches were found (in
+match. Instead, it inserts all found matches into the specified list. If
+there are multiple occurrences of the same expression, it is entered only
+once to the list. @code{find()} returns false if no matches were found (in
@command{ginsh}, it returns an empty list):
@example
@{sin(y),sin(x)@}
@end example
+@subsection Substituting expressions
@cindex @code{subs()}
Probably the most useful application of patterns is to use them for
substituting expressions with the @code{subs()} method. Wildcards can be
@subsection Expanding and collecting
@cindex @code{expand()}
@cindex @code{collect()}
+@cindex @code{collect_common_factors()}
A polynomial in one or more variables has many equivalent
representations. Some useful ones serve a specific purpose. Consider
(1+q+d*(1+q+p)+p)*sin(y)+(1+q+d*(1+q+p)+p)*sin(x)
@end example
+Polynomials can often be brought into a more compact form by collecting
+common factors from the terms of sums. This is accomplished by the function
+
+@example
+ex collect_common_factors(const ex & e);
+@end example
+
+This function doesn't perform a full factorization but only looks for
+factors which are already explicitly present:
+
+@example
+> collect_common_factors(a*x+a*y);
+(x+y)*a
+> collect_common_factors(a*x^2+2*a*x*y+a*y^2);
+a*(2*x*y+y^2+x^2)
+> collect_common_factors(a*(b*(a+c)*x+b*((a+c)*x+(a+c)*y)*y));
+(c+a)*a*(x*y+y^2+x)*b
+@end example
+
@subsection Degree and coefficients
@cindex @code{degree()}
@cindex @code{ldegree()}
@end example
This @file{Makefile.am}, says that we are building a single executable,
-from a single sourcefile @file{simple.cpp}. Since every program
+from a single source file @file{simple.cpp}. Since every program
we are building uses GiNaC we simply added the GiNaC options
to @env{$LIBS} and @env{$CPPFLAGS}, but in other circumstances, we might
want to specify them on a per-program basis: for instance by
git://www.ginac.de / ginac.git / blobdiff