Linear equation systems can be solved along with basic linear
algebra manipulations over symbolic expressions. In C++ GiNaC offers
a matrix class for this purpose but we can see what it can do using
-@command{ginsh}'s notation of double brackets to type them in:
+@command{ginsh}'s bracket notation to type them in:
@example
> lsolve(a+x*y==z,x);
Generally, the top-level Makefile runs recursively to the
subdirectories. It is therfore safe to go into any subdirectory
-(@code{doc/}, @code{ginsh/}, ...) and simply type @code{make}
+(@code{doc/}, @code{ginsh/}, @dots{}) and simply type @code{make}
@var{target} there in case something went wrong.
* Lists:: Lists of expressions.
* Mathematical functions:: Mathematical functions.
* Relations:: Equality, Inequality and all that.
+* Matrices:: Matrices.
* Indexed objects:: Handling indexed quantities.
* Non-commutative objects:: Algebras with non-commutative products.
@end menu
The most common class of objects a user deals with is the expression
@code{ex}, representing a mathematical object like a variable, number,
-function, sum, product, etc... Expressions may be put together to form
+function, sum, product, etc@dots{} Expressions may be put together to form
new expressions, passed as arguments to functions, and so on. Here is a
little collection of valid expressions:
@item @code{pseries} @tab Power Series, e.g. @math{x-1/6*x^3+1/120*x^5+O(x^7)}
@item @code{function} @tab A symbolic function like @math{sin(2*x)}
@item @code{lst} @tab Lists of expressions @{@math{x}, @math{2*y}, @math{3+z}@}
-@item @code{matrix} @tab @math{n}x@math{m} matrices of expressions
+@item @code{matrix} @tab @math{m}x@math{n} matrices of expressions
@item @code{relational} @tab A relation like the identity @math{x}@code{==}@math{y}
@item @code{indexed} @tab Indexed object like @math{A_ij}
@item @code{tensor} @tab Special tensor like the delta and metric tensors
Although symbols can be assigned expressions for internal reasons, you
should not do it (and we are not going to tell you how it is done). If
you want to replace a symbol with something else in an expression, you
-can use the expression's @code{.subs()} method (@xref{Substituting Expressions},
-for more information).
+can use the expression's @code{.subs()} method (@pxref{Substituting Expressions}).
@node Numbers, Constants, Symbols, Basic Concepts
@cindex @code{op()}
@cindex @code{append()}
@cindex @code{prepend()}
+@cindex @code{remove_first()}
+@cindex @code{remove_last()}
-The GiNaC class @code{lst} serves for holding a list of arbitrary expressions.
-These are sometimes used to supply a variable number of arguments of the same
-type to GiNaC methods such as @code{subs()} and @code{to_rational()}, so you
-should have a basic understanding about them.
+The GiNaC class @code{lst} serves for holding a @dfn{list} of arbitrary
+expressions. These are sometimes used to supply a variable number of
+arguments of the same type to GiNaC methods such as @code{subs()} and
+@code{to_rational()}, so you should have a basic understanding about them.
-Lists of up to 15 expressions can be directly constructed from single
+Lists of up to 16 expressions can be directly constructed from single
expressions:
@example
// ...
@end example
-Finally you can append or prepend an expression to a list with the
-@code{append()} and @code{prepend()} methods:
+You can append or prepend an expression to a list with the @code{append()}
+and @code{prepend()} methods:
@example
// ...
l.append(4*x); // l is now @{x, 2, y, x+y, 4*x@}
l.prepend(0); // l is now @{0, x, 2, y, x+y, 4*x@}
+ // ...
+@end example
+
+Finally you can remove the first or last element of a list with
+@code{remove_first()} and @code{remove_last()}:
+
+@example
+ // ...
+ l.remove_first(); // l is now @{x, 2, y, x+y, 4*x@}
+ l.remove_last(); // l is now @{x, 2, y, x+y@}
@}
@end example
this.
-@node Relations, Indexed objects, Mathematical functions, Basic Concepts
+@node Relations, Matrices, Mathematical functions, Basic Concepts
@c node-name, next, previous, up
@section Relations
@cindex @code{relational} (class)
@code{expand()} must be called explicitly.
-@node Indexed objects, Non-commutative objects, Relations, Basic Concepts
+@node Matrices, Indexed objects, Relations, Basic Concepts
+@c node-name, next, previous, up
+@section Matrices
+@cindex @code{matrix} (class)
+
+A @dfn{matrix} is a two-dimensional array of expressions. The elements of a
+matrix with @math{m} rows and @math{n} columns are accessed with two
+@code{unsigned} indices, the first one in the range 0@dots{}@math{m-1}, the
+second one in the range 0@dots{}@math{n-1}.
+
+There are a couple of ways to construct matrices, with or without preset
+elements:
+
+@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);
+@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.
+
+Matrix elements can be accessed and set using the parenthesis (function call)
+operator:
+
+@example
+const ex & matrix::operator()(unsigned r, unsigned c) const;
+ex & matrix::operator()(unsigned r, unsigned c);
+@end example
+
+It is also possible to access the matrix elements in a linear fashion with
+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:
+
+@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));
+
+ e = lst_to_matrix(lst(lst(a, 0), lst(0, b)));
+
+ e = diag_matrix(lst(a, b));
+
+ cout << e << endl;
+ // -> [[a,0],[0,b]]
+@}
+@end example
+
+@cindex @code{transpose()}
+@cindex @code{inverse()}
+There are three ways to do arithmetic with matrices. The first (and most
+efficient one) is to use the methods provided by the @code{matrix} class:
+
+@example
+matrix matrix::add(const matrix & other) const;
+matrix matrix::sub(const matrix & other) const;
+matrix matrix::mul(const matrix & other) const;
+matrix matrix::mul_scalar(const ex & other) const;
+matrix matrix::pow(const ex & expn) const;
+matrix matrix::transpose(void) const;
+matrix matrix::inverse(void) const;
+@end example
+
+All of these methods return the result as a new matrix object. Here is an
+example that calculates @math{A*B-2*C} for three matrices @math{A}, @math{B}
+and @math{C}:
+
+@example
+@{
+ matrix A(2, 2, lst(1, 2, 3, 4));
+ matrix B(2, 2, lst(-1, 0, 2, 1));
+ matrix C(2, 2, lst(8, 4, 2, 1));
+
+ matrix result = A.mul(B).sub(C.mul_scalar(2));
+ cout << result << endl;
+ // -> [[-13,-6],[1,2]]
+ ...
+@}
+@end example
+
+@cindex @code{evalm()}
+The second (and probably the most natural) way is to construct an expression
+containing matrices with the usual arithmetic operators and @code{pow()}.
+For efficiency reasons, expressions with sums, products and powers of
+matrices are not automatically evaluated in GiNaC. You have to call the
+method
+
+@example
+ex ex::evalm() const;
+@end example
+
+to obtain the result:
+
+@example
+@{
+ ...
+ ex e = A*B - 2*C;
+ cout << e << endl;
+ // -> [[1,2],[3,4]]*[[-1,0],[2,1]]-2*[[8,4],[2,1]]
+ cout << e.evalm() << endl;
+ // -> [[-13,-6],[1,2]]
+ ...
+@}
+@end example
+
+The non-commutativity of the product @code{A*B} in this example is
+automatically recognized by GiNaC. There is no need to use a special
+operator here. @xref{Non-commutative objects}, for more information about
+dealing with non-commutative expressions.
+
+Finally, you can work with indexed matrices and call @code{simplify_indexed()}
+to perform the arithmetic:
+
+@example
+@{
+ ...
+ idx i(symbol("i"), 2), j(symbol("j"), 2), k(symbol("k"), 2);
+ e = indexed(A, i, k) * indexed(B, k, j) - 2 * indexed(C, i, j);
+ cout << e << endl;
+ // -> -2*[[8,4],[2,1]].i.j+[[-1,0],[2,1]].k.j*[[1,2],[3,4]].i.k
+ cout << e.simplify_indexed() << endl;
+ // -> [[-13,-6],[1,2]].i.j
+@}
+@end example
+
+Using indices is most useful when working with rectangular matrices and
+one-dimensional vectors because you don't have to worry about having to
+transpose matrices before multiplying them. @xref{Indexed objects}, for
+more information about using matrices with indices, and about indices in
+general.
+
+The @code{matrix} class provides a couple of additional methods for
+computing determinants, traces, and characteristic polynomials:
+
+@example
+ex matrix::determinant(unsigned algo = determinant_algo::automatic) const;
+ex matrix::trace(void) const;
+ex matrix::charpoly(const symbol & lambda) const;
+@end example
+
+The @samp{algo} argument of @code{determinant()} allows to select between
+different algorithms for calculating the determinant. The possible values
+are defined in the @file{flags.h} header file. By default, GiNaC uses a
+heuristic to automatically select an algorithm that is likely to give the
+result most quickly.
+
+
+@node Indexed objects, Non-commutative objects, Matrices, Basic Concepts
@c node-name, next, previous, up
@section Indexed objects
@subsubsection Delta tensor
The delta tensor takes two indices, is symmetric and has the matrix
-representation @code{diag(1,1,1,...)}. It is constructed by the function
+representation @code{diag(1, 1, 1, ...)}. It is constructed by the function
@code{delta_tensor()}:
@example
idx i(symbol("i"), 2), j(symbol("j"), 2);
symbol x("x"), y("y");
+ // A is a 2x2 matrix, X is a 2x1 vector
matrix A(2, 2, lst(1, 2, 3, 4)), X(2, 1, lst(x, y));
cout << indexed(A, i, i) << endl;
@end example
You can of course obtain the same results with the @code{matrix::add()},
-@code{matrix::mul()} and @code{matrix::trace()} methods but with indices you
-don't have to worry about transposing matrices.
+@code{matrix::mul()} and @code{matrix::trace()} methods (@pxref{Matrices})
+but with indices you don't have to worry about transposing matrices.
Matrix indices always start at 0 and their dimension must match the number
of rows/columns of the matrix. Matrices with one row or one column are
The @code{clifford} and @code{color} classes are subclasses of
@code{indexed} because the elements of these algebras ususally carry
-indices.
+indices. The @code{matrix} class is described in more detail in
+@ref{Matrices}.
Unlike most computer algebra systems, GiNaC does not primarily provide an
operator (often denoted @samp{&*}) for representing inert products of
but it's the same for any two objects with the same label. This is also true
for color objects.
-As a last note, positive integer powers of non-commutative objects are
-automatically expanded in GiNaC. For example, @code{pow(a*b, 2)} becomes
-@samp{a*b*a*b} if @samp{a} and @samp{b} are non-commutative expressions).
+A last note: With the exception of matrices, positive integer powers of
+non-commutative objects are automatically expanded in GiNaC. For example,
+@code{pow(a*b, 2)} becomes @samp{a*b*a*b} if @samp{a} and @samp{b} are
+non-commutative expressions).
@cindex @code{clifford} (class)
@code{false}.
Actually, if you construct an expression like @code{a == b}, this will be
-represented by an object of the @code{relational} class (@xref{Relations}.)
+represented by an object of the @code{relational} class (@pxref{Relations})
which is not evaluated until (explicitly or implicitely) cast to a @code{bool}.
There are also two methods
@node Pattern Matching and Advanced Substitutions, Polynomial Arithmetic, Substituting Expressions, Methods and Functions
@c node-name, next, previous, up
@section Pattern matching and advanced substitutions
+@cindex @code{wildcard} (class)
+@cindex Pattern matching
GiNaC allows the use of patterns for checking whether an expression is of a
certain form or contains subexpressions of a certain form, and for
represents an arbitrary expression. Every wildcard has a @dfn{label} which is
an unsigned integer number to allow having multiple different wildcards in a
pattern. Wildcards are printed as @samp{$label} (this is also the way they
-are specified in @command{ginsh}. In C++ code, wildcard objects are created
+are specified in @command{ginsh}). In C++ code, wildcard objects are created
with the call
@example
> match(a*b^2,a^$1*b^$2);
FAIL
(The matching is syntactic, not algebraic, and "a" doesn't match "a^$1"
- even if a==a^1.)
+ even though a==a^1.)
> match(x*atan2(x,x^2),$0*atan2($0,$0^2));
@{$0==x@}
> match(atan2(y,x^2),atan2(y,$0));
@example
> has(x*sin(x+y+2*a),y);
1
-> has(x*sin(x+y+2*a+y),x+y);
+> has(x*sin(x+y+2*a),x+y);
0
(This is because in GiNaC, "x+y" is not a subexpression of "x+y+2*a" (which
has the subexpressions "x", "y" and "2*a".)
-> has(x*sin(x+y+2*a+y),x+y+$1);
+> has(x*sin(x+y+2*a),x+y+$1);
1
(But this is possible.)
> has(x*sin(2*(x+y)+2*a),x+y);
@section Symmetrization
@cindex @code{symmetrize()}
@cindex @code{antisymmetrize()}
+@cindex @code{symmetrize_cyclic()}
-The two methods
+The three methods
@example
ex ex::symmetrize(const lst & l);
ex ex::antisymmetrize(const lst & l);
+ex ex::symmetrize_cyclic(const lst & l);
@end example
-symmetrize an expression by returning the symmetric or antisymmetric sum
-over all permutations of the specified list of objects, weighted by the
-number of permutations.
+symmetrize an expression by returning the sum over all symmetric,
+antisymmetric or cyclic permutations of the specified list of objects,
+weighted by the number of permutations.
-The two additional methods
+The three additional methods
@example
ex ex::symmetrize();
ex ex::antisymmetrize();
+ex ex::symmetrize_cyclic();
@end example
symmetrize or antisymmetrize an expression over its free indices.
// -> 1/2*A.j.i+1/2*A.i.j
cout << indexed(A, i, j, k).antisymmetrize(lst(i, j)) << endl;
// -> -1/2*A.j.i.k+1/2*A.i.j.k
- cout << lst(a, b, c).symmetrize(lst(a, b, c)) << endl;
- // -> 1/6*@{a,b,c@}+1/6*@{c,a,b@}+1/6*@{b,a,c@}+1/6*@{c,b,a@}+1/6*@{b,c,a@}+1/6*@{a,c,b@}
+ cout << lst(a, b, c).symmetrize_cyclic(lst(a, b, c)) << endl;
+ // -> 1/3*@{a,b,c@}+1/3*@{b,c,a@}+1/3*@{c,a,b@}
@}
@end example
// -> "GiNaC rulez"+"Hello, world!"
@end example
-(note that GiNaC's automatic term reordering is in effect here), or even
+(GiNaC's automatic term reordering is in effect here), or even
@example
e = pow(mystring("One string"), 2*sin(Pi-mystring("Another string")));
@item
development tools: powerful development tools exist for C++, like fancy
editors (e.g. with automatic indentation and syntax highlighting),
-debuggers, visualization tools, documentation generators...
+debuggers, visualization tools, documentation generators@dots{}
@item
modularization: C++ programs can easily be split into modules by
git://www.ginac.de / ginac.git / blobdiff