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Diff for /OpenXM/src/asir-doc/parts/groebner.texi between version 1.10 and 1.13

version 1.10, 2003/04/28 03:09:23 version 1.13, 2004/09/13 09:23:30
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 @comment $OpenXM: OpenXM/src/asir-doc/parts/groebner.texi,v 1.9 2003/04/24 08:13:24 noro Exp $  @comment $OpenXM: OpenXM/src/asir-doc/parts/groebner.texi,v 1.12 2003/12/27 11:52:07 takayama Exp $
 \BJP  \BJP
 @node $B%0%l%V%J4pDl$N7W;;(B,,, Top  @node $B%0%l%V%J4pDl$N7W;;(B,,, Top
 @chapter $B%0%l%V%J4pDl$N7W;;(B  @chapter $B%0%l%V%J4pDl$N7W;;(B
Line 15 
Line 15 
 * $B4pK\E*$JH!?t(B::  * $B4pK\E*$JH!?t(B::
 * $B7W;;$*$h$SI=<($N@)8f(B::  * $B7W;;$*$h$SI=<($N@)8f(B::
 * $B9`=g=x$N@_Dj(B::  * $B9`=g=x$N@_Dj(B::
   * Weight::
 * $BM-M}<0$r78?t$H$9$k%0%l%V%J4pDl7W;;(B::  * $BM-M}<0$r78?t$H$9$k%0%l%V%J4pDl7W;;(B::
 * $B4pDlJQ49(B::  * $B4pDlJQ49(B::
 * Weyl $BBe?t(B::  * Weyl $BBe?t(B::
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Line 27 
 * Fundamental functions::  * Fundamental functions::
 * Controlling Groebner basis computations::  * Controlling Groebner basis computations::
 * Setting term orderings::  * Setting term orderings::
   * Weight::
 * Groebner basis computation with rational function coefficients::  * Groebner basis computation with rational function coefficients::
 * Change of ordering::  * Change of ordering::
 * Weyl algebra::  * Weyl algebra::
Line 1055  beforehand, and some heuristic trial may be inevitable
Line 1057  beforehand, and some heuristic trial may be inevitable
 \E  \E
   
 \BJP  \BJP
   @node Weight ,,, $B%0%l%V%J4pDl$N7W;;(B
   @section Weight
   \E
   \BEG
   @node Weight,,, Groebner basis computation
   @section Weight
   \E
   \BJP
   $BA0@a$G>R2p$7$?9`=g=x$O(B, $B3FJQ?t$K(B weight ($B=E$_(B) $B$r@_Dj$9$k$3$H$G(B
   $B$h$j0lHLE*$J$b$N$H$J$k(B.
   \E
   \BEG
   Term orders introduced in the previous section can be generalized
   by setting a weight for each variable.
   \E
   @example
   [0] dp_td(<<1,1,1>>);
   3
   [1] dp_set_weight([1,2,3])$
   [2] dp_td(<<1,1,1>>);
   6
   @end example
   \BJP
   $BC19`<0$NA4<!?t$r7W;;$9$k:](B, $B%G%U%)%k%H$G$O(B
   $B3FJQ?t$N;X?t$NOB$rA4<!?t$H$9$k(B. $B$3$l$O3FJQ?t$N(B weight $B$r(B 1 $B$H(B
   $B9M$($F$$$k$3$H$KAjEv$9$k(B. $B$3$NNc$G$O(B, $BBh0l(B, $BBhFs(B, $BBh;0JQ?t$N(B
   weight $B$r$=$l$>$l(B 1,2,3 $B$H;XDj$7$F$$$k(B. $B$3$N$?$a(B, @code{<<1,1,1>>}
   $B$NA4<!?t(B ($B0J2<$G$O$3$l$rC19`<0$N(B weight $B$H8F$V(B) $B$,(B @code{1*1+1*2+1*3=6} $B$H$J$k(B.
   weight $B$r@_Dj$9$k$3$H$G(B, $BF1$89`=g=x7?$N$b$H$G0[$J$k9`=g=x$,Dj5A$G$-$k(B.
   $BNc$($P(B, weight $B$r$&$^$/@_Dj$9$k$3$H$G(B, $BB?9`<0$r(B weighted homogeneous
   $B$K$9$k$3$H$,$G$-$k>l9g$,$"$k(B.
   \E
   \BEG
   By default, the total degree of a monomial is equal to
   the sum of all exponents. This means that the weight for each variable
   is set to 1.
   In this example, the weights for the first, the second and the third
   variable are set to 1, 2 and 3 respectively.
   Therefore the total degree of @code{<<1,1,1>>} under this weight,
   which is called the weight of the monomial, is @code{1*1+1*2+1*3=6}.
   By setting weights, different term orders can be set under a term
   order type. For example, a polynomial can be made weighted homogeneous
   by setting an appropriate weight.
   \E
   
   \BJP
   $B3FJQ?t$KBP$9$k(B weight $B$r$^$H$a$?$b$N$r(B weight vector $B$H8F$V(B.
   $B$9$Y$F$N@.J,$,@5$G$"$j(B, $B%0%l%V%J4pDl7W;;$K$*$$$F(B, $BA4<!?t$N(B
   $BBe$o$j$KMQ$$$i$l$k$b$N$rFC$K(B sugar weight $B$H8F$V$3$H$K$9$k(B.
   sugar strategy $B$K$*$$$F(B, $BA4<!?t$NBe$o$j$K;H$o$l$k$+$i$G$"$k(B.
   $B0lJ}$G(B, $B3F@.J,$,I,$:$7$b@5$H$O8B$i$J$$(B weight vector $B$O(B,
   sugar weight $B$H$7$F@_Dj$9$k$3$H$O$G$-$J$$$,(B, $B9`=g=x$N0lHL2=$K$O(B
   $BM-MQ$G$"$k(B. $B$3$l$i$O(B, $B9TNs$K$h$k9`=g=x$N@_Dj$K$9$G$K8=$l$F(B
   $B$$$k(B. $B$9$J$o$A(B, $B9`=g=x$rDj5A$9$k9TNs$N3F9T$,(B, $B0l$D$N(B weight vector
   $B$H8+$J$5$l$k(B. $B$^$?(B, $B%V%m%C%/=g=x$O(B, $B3F%V%m%C%/$N(B
   $BJQ?t$KBP1~$9$k@.J,$N$_(B 1 $B$GB>$O(B 0 $B$N(B weight vector $B$K$h$kHf3S$r(B
   $B:G=i$K9T$C$F$+$i(B, $B3F%V%m%C%/Kh$N(B tie breaking $B$r9T$&$3$H$KAjEv$9$k(B.
   \E
   
   \BEG
   A list of weights for all variables is called a weight vector.
   A weight vector is called a sugar weight vector if
   its elements are all positive and it is used for computing
   a weighted total degree of a monomial, because such a weight
   is used instead of total degree in sugar strategy.
   On the other hand, a weight vector whose elements are not necessarily
   positive cannot be set as a sugar weight, but it is useful for
   generalizing term order. In fact, such a weight vector already
   appeared in a matrix order. That is, each row of a matrix defining
   a term order is regarded as a weight vector. A block order
   is also considered as a refinement of comparison by weight vectors.
   It compares two terms by using a weight vector whose elements
   corresponding to variables in a block is 1 and 0 otherwise,
   then it applies a tie breaker.
   
   \E
   
   \BJP
 @node $BM-M}<0$r78?t$H$9$k%0%l%V%J4pDl7W;;(B,,, $B%0%l%V%J4pDl$N7W;;(B  @node $BM-M}<0$r78?t$H$9$k%0%l%V%J4pDl7W;;(B,,, $B%0%l%V%J4pDl$N7W;;(B
 @section $BM-M}<0$r78?t$H$9$k%0%l%V%J4pDl7W;;(B  @section $BM-M}<0$r78?t$H$9$k%0%l%V%J4pDl7W;;(B
 \E  \E
Line 1412  strategy $B$K$h$k7W;;(B, @code{hgr()} $B$O(B trace
Line 1492  strategy $B$K$h$k7W;;(B, @code{hgr()} $B$O(B trace
 @item  @item
 @code{dgr()} $B$GI=<($5$l$k;~4V$O(B, $B$3$NH!?t$,<B9T$5$l$F$$$k%W%m%;%9$G$N(B  @code{dgr()} $B$GI=<($5$l$k;~4V$O(B, $B$3$NH!?t$,<B9T$5$l$F$$$k%W%m%;%9$G$N(B
 CPU $B;~4V$G$"$j(B, $B$3$NH!?t$N>l9g$O$[$H$s$IDL?.$N$?$a$N;~4V$G$"$k(B.  CPU $B;~4V$G$"$j(B, $B$3$NH!?t$N>l9g$O$[$H$s$IDL?.$N$?$a$N;~4V$G$"$k(B.
   @item
   $BB?9`<0%j%9%H(B @var{plist} $B$NMWAG$,J,;6I=8=B?9`<0$N>l9g$O(B
   $B7k2L$bJ,;6I=8=B?9`<0$N%j%9%H$G$"$k(B.
   $B$3$N>l9g(B, $B0z?t$NJ,;6B?9`<0$OM?$($i$l$?=g=x$K=>$$(B @code{dp_sort} $B$G(B
   $B%=!<%H$5$l$F$+$i7W;;$5$l$k(B.
   $BB?9`<0%j%9%H$NMWAG$,J,;6I=8=B?9`<0$N>l9g$b(B
   $BJQ?t$N?tJ,$NITDj85$N%j%9%H$r(B @var{vlist} $B0z?t$H$7$FM?$($J$$$H$$$1$J$$(B
   ($B%@%_!<(B).
 \E  \E
 \BEG  \BEG
 @item  @item
Line 1440  Therefore this function is useful to reduce the actual
Line 1528  Therefore this function is useful to reduce the actual
 The CPU time shown after an exection of @code{dgr()} indicates  The CPU time shown after an exection of @code{dgr()} indicates
 that of the master process, and most of the time corresponds to the time  that of the master process, and most of the time corresponds to the time
 for communication.  for communication.
   @item
   When the elements of @var{plist} are distributed polynomials,
   the result is also a list of distributed polynomials.
   In this case, firstly  the elements of @var{plist} is sorted by @code{dp_sort}
   and the Grobner basis computation is started.
   Variables must be given in @var{vlist} even in this case
   (these variables are dummy).
 \E  \E
 @end itemize  @end itemize
   
Line 3967  execute @code{dp_gr_print(2)} in advance.
Line 4062  execute @code{dp_gr_print(2)} in advance.
 @var{vlist} $B$O(B @code{x}-$BJQ?t(B, @var{vlist} $B$OBP1~$9$k(B @code{D}-$BJQ?t(B  @var{vlist} $B$O(B @code{x}-$BJQ?t(B, @var{vlist} $B$OBP1~$9$k(B @code{D}-$BJQ?t(B
 $B$r=g$KJB$Y$k(B.  $B$r=g$KJB$Y$k(B.
 @item @code{bfunction} $B$H(B @code{bfct} $B$G$OMQ$$$F$$$k%"%k%4%j%:%`$,(B  @item @code{bfunction} $B$H(B @code{bfct} $B$G$OMQ$$$F$$$k%"%k%4%j%:%`$,(B
 $B0[$J$k(B. $B$I$A$i$,9bB.2=$OF~NO$K$h$k(B.  $B0[$J$k(B. $B$I$A$i$,9bB.$+$OF~NO$K$h$k(B.
 @item @code{ann(@var{f})} $B$O(B, @code{@var{f}^s} $B$N(B annihilator ideal  @item @code{ann(@var{f})} $B$O(B, @code{@var{f}^s} $B$N(B annihilator ideal
 $B$N@8@.7O$rJV$9(B. @code{ann(@var{f})} $B$O(B, @code{[@var{a},@var{list}]}  $B$N@8@.7O$rJV$9(B. @code{ann(@var{f})} $B$O(B, @code{[@var{a},@var{list}]}
 $B$J$k%j%9%H$rJV$9(B. $B$3$3$G(B, @var{a} $B$O(B @var{f} $B$N(B @var{b} $B4X?t$N:G>.@0?t:,(B,  $B$J$k%j%9%H$rJV$9(B. $B$3$3$G(B, @var{a} $B$O(B @var{f} $B$N(B @var{b} $B4X?t$N:G>.@0?t:,(B,
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