[6ba162] | 1 | // ideal.cc |
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| 2 | |
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| 3 | // implementation of some general ideal functions |
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| 4 | |
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| 5 | #ifndef IDEAL_CC |
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| 6 | #define IDEAL_CC |
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| 7 | |
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[10b7a4] | 8 | #include <climits> |
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[6ba162] | 9 | #include "ideal.h" |
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| 10 | |
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| 11 | ///////////////////////////////////////////////////////////////////////////// |
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| 12 | ////////////////// private member functions ///////////////////////////////// |
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| 13 | ///////////////////////////////////////////////////////////////////////////// |
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| 14 | |
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| 15 | ///////////// subset_tree data structure //////////////////////////////////// |
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| 16 | |
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| 17 | #ifdef SUPPORT_DRIVEN_METHODS_EXTENDED |
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| 18 | |
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[8a5474] | 19 | void ideal::create_subset_tree() |
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[6ba162] | 20 | { |
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| 21 | for(short i=0;i<Number_of_Lists;i++) |
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| 22 | { |
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| 23 | |
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| 24 | // First determine the number of binary vectors whose support is a subset |
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| 25 | // of the support of i (i read as binary vector). |
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| 26 | // The support of i is a set of cardinality s, where s is the number of |
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| 27 | // bits in i that are 1. Hence the desired number is 2^s. |
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| 28 | |
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| 29 | short s=0; |
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| 30 | |
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| 31 | for(short k=0;k<List_Support_Variables;k++) |
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| 32 | if( (i&(1<<k)) == (1<<k) ) |
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| 33 | // bit k of i is 1 |
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| 34 | s++; |
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| 35 | |
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| 36 | S.number_of_subsets[i]=(1<<s); |
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| 37 | // (1<<s) == 2^s |
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| 38 | |
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| 39 | // Now determine the concrete binary vectors whose support is a subset |
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| 40 | // of that of i. This is done in a very simple manner by comparing |
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| 41 | // the support of each number between 0 and i (read as binary vector) |
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| 42 | // with that of i. (Efficiency considerations are absolutely unimportant |
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| 43 | // in this function.) |
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| 44 | |
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| 45 | S.subsets_of_support[i]=new short[S.number_of_subsets[i]]; |
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| 46 | // memory allocation for subsets_of_support[i] |
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| 47 | |
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| 48 | short index=0; |
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| 49 | for(short j=0;j<Number_of_Lists;j++) |
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| 50 | if((i&j)==j) |
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| 51 | // If the support of j as a bit vector is contained in the support of |
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| 52 | // i as a bit vector, j is saved in the list subsets_of_support[i]. |
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| 53 | { |
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| 54 | S.subsets_of_support[i][index]=j; |
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| 55 | index++; |
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| 56 | } |
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| 57 | } |
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| 58 | } |
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| 59 | |
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[8a5474] | 60 | void ideal::destroy_subset_tree() |
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[6ba162] | 61 | { |
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| 62 | for(short i=0;i<Number_of_Lists;i++) |
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| 63 | delete[] S.subsets_of_support[i]; |
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| 64 | // The arrays number_of_subsets and subsets_of_support (the (short*)-array) |
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| 65 | // are not dynamically allocated and do not have to be deleted. |
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| 66 | } |
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| 67 | #endif // SUPPORT_DRIVEN_METHODS_EXTENDED |
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| 68 | |
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| 69 | /////////// subroutines for BuchbergerŽs algorithm ////////////////////////// |
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| 70 | |
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| 71 | ideal& ideal::add_new_generator(binomial& bin) |
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| 72 | { |
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| 73 | #ifdef SUPPORT_DRIVEN_METHODS_EXTENDED |
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| 74 | |
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| 75 | new_generators[(bin.head_support)%Number_of_Lists].insert(bin); |
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| 76 | // insert the bin according to its support, |
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| 77 | // considering only the first List_Support_Variables variables. |
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| 78 | |
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| 79 | #endif // SUPPORT_DRIVEN_METHODS_EXTENDED |
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| 80 | |
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| 81 | #ifdef NO_SUPPORT_DRIVEN_METHODS_EXTENDED |
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| 82 | |
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| 83 | new_generators.insert(bin); |
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| 84 | |
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| 85 | #endif // NO_SUPPORT_DRIVEN_METHODS_EXTENDED |
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| 86 | |
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| 87 | return(*this); |
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| 88 | } |
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| 89 | |
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| 90 | ideal& ideal::add_generator(binomial& bin) |
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| 91 | { |
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| 92 | // Beside its function as a auxiliary routine for a shorter code, this routine |
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| 93 | // offers a good way to hide if SUPPORT_DRIVEN_METHODS_EXTENDED are used or |
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| 94 | // not. So the constructors do not have to care about this. |
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| 95 | |
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| 96 | #ifdef SUPPORT_DRIVEN_METHODS_EXTENDED |
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| 97 | |
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| 98 | generators[(bin.head_support)%Number_of_Lists].insert(bin); |
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| 99 | // insert the bin according to its support, |
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| 100 | // considering only the first List_Support_Variables variables. |
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| 101 | |
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| 102 | #endif // SUPPORT_DRIVEN_METHODS_EXTENDED |
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| 103 | |
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| 104 | #ifdef NO_SUPPORT_DRIVEN_METHODS_EXTENDED |
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| 105 | |
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| 106 | generators.insert(bin); |
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| 107 | |
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| 108 | #endif // NO_SUPPORT_DRIVEN_METHODS_EXTENDED |
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| 109 | |
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| 110 | size++; |
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| 111 | number_of_new_binomials++; |
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| 112 | |
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| 113 | return(*this); |
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| 114 | } |
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| 115 | |
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| 116 | //////////////////// constructor subroutines //////////////////////////////// |
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| 117 | |
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| 118 | ideal& ideal::Conti_Traverso_ideal(matrix& A,const term_ordering& _w) |
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| 119 | { |
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| 120 | |
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| 121 | // A may have negative entries; to model this with binomials, we need an |
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| 122 | // inversion variable. |
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| 123 | |
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| 124 | w=_w; |
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| 125 | // The argument term ordering should be given by the objective function. |
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| 126 | |
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| 127 | w.convert_to_elimination_ordering(A.rows+1,LEX); |
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| 128 | // extend term ordering into an elimination ordering of the appropriate |
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| 129 | // size |
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| 130 | |
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[2a94d3] | 131 | Integer *generator=new Integer[A.columns+A.rows+1]; |
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[6ba162] | 132 | // A.columns + A.rows +1 is the number of variables for the Conti-Traverso |
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| 133 | // algorithm with "inversion variable". |
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| 134 | |
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| 135 | // build initial ideal generators |
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| 136 | for(short j=0;j<A.columns;j++) |
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| 137 | { |
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| 138 | for(short k=0;k<A.columns;k++) |
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| 139 | // original variables |
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| 140 | if(j==k) |
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| 141 | generator[k]=-1; |
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| 142 | else |
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| 143 | generator[k]=0; |
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| 144 | |
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| 145 | for(short i=0;i<A.rows;i++) |
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| 146 | // elimination variables |
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| 147 | generator[A.columns+i]=A.coefficients[i][j]; |
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| 148 | |
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| 149 | generator[A.columns+A.rows]=0; |
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| 150 | // inversion variable |
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| 151 | |
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| 152 | // Note that the relative order of the variables is important: |
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| 153 | // If the elimination variables do not follow the other variables, |
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| 154 | // the conversion of the term ordering has not the desired effect. |
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| 155 | |
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| 156 | binomial* bin=new binomial(A.rows+1+A.columns,generator,w); |
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| 157 | add_generator(*bin); |
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| 158 | } |
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| 159 | |
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| 160 | // now add the "inversion generator" |
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| 161 | for(short j=0;j<A.columns;j++) |
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| 162 | generator[j]=0; |
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| 163 | for(short i=0;i<A.rows+1;i++) |
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| 164 | generator[A.columns+i]=1; |
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| 165 | binomial* bin=new binomial(A.rows+1+A.columns,generator,w); |
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| 166 | add_generator(*bin); |
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| 167 | |
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[2a94d3] | 168 | delete[] generator; |
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[6ba162] | 169 | return *this; |
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| 170 | } |
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| 171 | |
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| 172 | ideal& ideal::Positive_Conti_Traverso_ideal(matrix& A,const term_ordering& _w) |
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| 173 | { |
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| 174 | |
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| 175 | // A is assumed to have only nonnegative entries;then we need no |
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| 176 | // "inversion variable". |
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| 177 | |
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| 178 | w=_w; |
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| 179 | // The argument term ordering should be given by the objective function. |
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| 180 | |
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| 181 | w.convert_to_elimination_ordering(A.rows, LEX); |
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| 182 | // extend term ordering into an elimination ordering of the appropriate |
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| 183 | // size |
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| 184 | |
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[2a94d3] | 185 | Integer *generator=new Integer[A.columns+A.rows]; |
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[6ba162] | 186 | // A.columns + A.rows is the number of variables for the Conti-Traverso |
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| 187 | // algorithm without "inversion variable". |
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| 188 | |
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| 189 | // build the initial ideal generators |
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| 190 | for(short j=0;j<A.columns;j++) |
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| 191 | { |
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| 192 | for(short k=0;k<A.columns;k++) |
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| 193 | // original variables |
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| 194 | if(j==k) |
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| 195 | generator[k]=-1; |
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| 196 | else |
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| 197 | generator[k]=0; |
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| 198 | |
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| 199 | for(short i=0;i<A.rows;i++) |
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| 200 | // elimination variables |
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| 201 | generator[A.columns+i]=A.coefficients[i][j]; |
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| 202 | |
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| 203 | // Note that the relative order of the variables is important: |
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| 204 | // If the elimination variables do not follow the other variables, |
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| 205 | // the conversion of the term ordering has not the desired effect. |
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| 206 | |
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| 207 | binomial* bin=new binomial(A.rows+A.columns,generator,w); |
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| 208 | add_generator(*bin); |
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| 209 | } |
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[2a94d3] | 210 | delete[] generator; |
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[6ba162] | 211 | return *this; |
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| 212 | } |
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| 213 | |
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| 214 | ideal& ideal::Pottier_ideal(matrix& A, const term_ordering& _w) |
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| 215 | { |
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| 216 | |
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| 217 | w=_w; |
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| 218 | // The argument term_ordering should be given by the objective function. |
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| 219 | |
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| 220 | w.convert_to_elimination_ordering(1,LEX); |
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| 221 | // add one elimination variable used to saturate the ideal |
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| 222 | |
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| 223 | if(A._kernel_dimension==-2) |
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| 224 | // kernel of A not yet computed, do this now |
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| 225 | A.LLL_kernel_basis(); |
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| 226 | |
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| 227 | if((A._kernel_dimension==-1) && (A.columns<0)) |
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| 228 | // error occurred in kernel computation or matrix corrupt |
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| 229 | { |
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| 230 | cout<<"\nWARNING: ideal& ideal::Pottier_ideal(matrix&, const " |
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| 231 | "term_ordering&):\ncannot build ideal from a corrupt input matrix"<<endl; |
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| 232 | size=-1; |
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| 233 | return *this; |
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| 234 | } |
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| 235 | |
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[2a94d3] | 236 | Integer *generator=new Integer[A.columns+1]; |
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[6ba162] | 237 | // This is the number of variables needed for Pottier's algorithm. |
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| 238 | |
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| 239 | |
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| 240 | // compute initial generating system from the kernel of A |
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| 241 | for(short j=0;j<A._kernel_dimension;j++) |
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| 242 | { |
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| 243 | |
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| 244 | for(short k=0;k<A.columns;k++) |
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| 245 | { |
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| 246 | |
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| 247 | // We should first verifie if the components of the LLL-reduced lattice |
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| 248 | // basis fit into the basic data type (Integer as defined in globals.h). |
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| 249 | // This overflow control does of course not detect overflows in the |
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| 250 | // course of the LLL-algorithm! |
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| 251 | |
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| 252 | #ifdef _SHORT_ |
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| 253 | |
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[7de4d7] | 254 | if(((A.H)[j][k]>(const BigInt &)SHRT_MAX) || ((A.H)[j][k]<(const BigInt &)SHRT_MIN)) |
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[6ba162] | 255 | { |
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| 256 | cerr<<"\nWARNING: ideal& ideal::Pottier_ideal(matrix&, const " |
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| 257 | "term_ordering&):\n" |
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| 258 | "LLL-reduced kernel basis does not fit into the used " |
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| 259 | "basic data type short."<<endl; |
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| 260 | size=-3; |
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[2a94d3] | 261 | delete[] generator; |
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[6ba162] | 262 | return *this; |
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| 263 | } |
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| 264 | |
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| 265 | #endif // _SHORT_ |
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| 266 | |
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| 267 | #ifdef _INT_ |
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| 268 | |
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[1bc154] | 269 | if(((A.H)[j][k]>(const BigInt&)INT_MAX) || ((A.H)[j][k]<(const BigInt&)INT_MIN)) |
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[6ba162] | 270 | { |
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| 271 | cerr<<"\nWARNING: ideal& ideal::Pottier_ideal(matrix&, const " |
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| 272 | "term_ordering&):\n" |
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| 273 | "LLL-reduced kernel basis does not fit into the used " |
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| 274 | "basic data type int."<<endl; |
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| 275 | size=-3; |
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[2a94d3] | 276 | delete[] generator; |
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[6ba162] | 277 | return *this; |
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| 278 | } |
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| 279 | |
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| 280 | #endif // _INT_ |
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| 281 | |
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| 282 | #ifdef _LONG_ |
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| 283 | |
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[1bc154] | 284 | if(((A.H)[j][k]>(const BigInt&)LONG_MAX) || ((A.H)[j][k]<(const BigInt&)LONG_MIN)) |
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[6ba162] | 285 | { |
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| 286 | cerr<<"\nWARNING: ideal& ideal::Pottier_ideal(matrix&, const " |
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| 287 | "term_ordering&):\n" |
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| 288 | "LLL-reduced kernel basis does not fit into the used " |
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| 289 | "basic data type long."<<endl; |
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| 290 | size=-3; |
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[2a94d3] | 291 | delete[] generator; |
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[6ba162] | 292 | return *this; |
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| 293 | } |
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| 294 | |
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| 295 | #endif // _LONG_ |
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| 296 | |
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| 297 | generator[k]=(A.H)[j][k]; |
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| 298 | } |
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| 299 | |
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| 300 | generator[A.columns]=0; |
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| 301 | // elimination variable |
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| 302 | |
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| 303 | // Note that the relative order of the variables is important: |
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| 304 | // If the elimination variable does not follow the other variables, |
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| 305 | // the conversion of the term ordering has not the desired effect. |
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| 306 | |
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| 307 | binomial* bin=new binomial(A.columns+1,generator,w); |
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| 308 | add_generator(*bin); |
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| 309 | } |
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| 310 | |
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| 311 | // build "saturation generator" |
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| 312 | |
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[bd19b6] | 313 | |
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[6ba162] | 314 | // The use of the hosten_shapiro procedure is useful here because the head |
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| 315 | // of the computed saturation generator is smaller if less variables are |
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| 316 | // involved. |
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[bd19b6] | 317 | short* sat_var = NULL; |
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| 318 | short number_of_sat_var = A.hosten_shapiro(sat_var); |
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| 319 | if( (number_of_sat_var == 0) || (sat_var == NULL) ) |
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| 320 | { |
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| 321 | delete[] generator; |
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| 322 | return *this; |
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| 323 | } |
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[6ba162] | 324 | |
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| 325 | for(short j=0;j<A.columns;j++) |
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| 326 | generator[j]=0; |
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| 327 | |
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| 328 | for(short k=0;k<number_of_sat_var;k++) |
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| 329 | generator[sat_var[k]]=1; |
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| 330 | |
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| 331 | generator[A.columns]=1; |
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| 332 | |
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| 333 | binomial* bin=new binomial(A.columns+1,generator,w); |
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| 334 | add_generator(*bin); |
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| 335 | // The "saturation generator" seems to be a monomial, but is interpreted |
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| 336 | // as a binomial with tail 1 by the designed data structures. |
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| 337 | |
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[bd19b6] | 338 | delete[] sat_var; |
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[2a94d3] | 339 | delete[] generator; |
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[6ba162] | 340 | |
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| 341 | return *this; |
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| 342 | } |
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| 343 | |
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| 344 | ideal& ideal::Hosten_Sturmfels_ideal(matrix& A, const term_ordering& _w) |
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| 345 | { |
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| 346 | |
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| 347 | // check term ordering |
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| 348 | if((_w.weight_refinement()!=W_REV_LEX) && (_w.is_positive()==FALSE)) |
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| 349 | cerr<<"\nWARNING: ideal& ideal::Hosten_Sturmfels_ideal(matrix&, const " |
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| 350 | "term_ordering&):\nargument term ordering should be a weighted reverse" |
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| 351 | "lexicographical \nwith positive weights"<<endl; |
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| 352 | |
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| 353 | w=_w; |
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| 354 | // The argument term_ordering should be given by a homogenous grading. |
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| 355 | |
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| 356 | if(A._kernel_dimension==-2) |
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| 357 | // kernel of A not yet computed, do this now |
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| 358 | A.LLL_kernel_basis(); |
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| 359 | |
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| 360 | if((A._kernel_dimension==-1) && (A.columns<0)) |
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| 361 | // error occurred in kernel computation or matrix corrupt |
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| 362 | { |
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| 363 | cout<<"\nWARNING: ideal& ideal::Hosten_Sturmfels_ideal(matrix&, const " |
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| 364 | "term_ordering&):\ncannot build ideal from a corrupt input matrix"<<endl; |
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| 365 | size=-1; |
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| 366 | return *this; |
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| 367 | } |
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| 368 | |
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[2a94d3] | 369 | Integer * generator=new Integer[A.columns]; |
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[6ba162] | 370 | // The algorithm of Hosten and Sturmfels does not need supplementary |
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| 371 | // variables. |
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| 372 | |
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| 373 | |
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| 374 | // compute initial generating system from the kernel of A |
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| 375 | for(short j=0;j<A._kernel_dimension;j++) |
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| 376 | { |
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| 377 | |
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| 378 | for(short k=0;k<A.columns;k++) |
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| 379 | { |
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| 380 | |
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| 381 | // We should first verifie if the components of the LLL-reduced lattice |
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| 382 | // basis fit into the basic data type (Integer as defined in globals.h). |
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| 383 | // This overflow control does of course not detect overflows in the |
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| 384 | // course of the LLL-algorithm! |
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| 385 | |
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| 386 | #ifdef _SHORT_ |
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| 387 | |
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[7de4d7] | 388 | if(((A.H)[j][k]>(const BigInt &)SHRT_MAX) || ((A.H)[j][k]<(const BigInt &)SHRT_MIN)) |
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[6ba162] | 389 | { |
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| 390 | cerr<<"\nWARNING: ideal& ideal::Hosten_Sturmfels_ideal(matrix&, const " |
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| 391 | "term_ordering&):\nLLL-reduced kernel basis does not fit " |
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| 392 | "into the used basic data type short."<<endl; |
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| 393 | size=-3; |
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[2a94d3] | 394 | delete[] generator; |
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[6ba162] | 395 | return *this; |
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| 396 | } |
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| 397 | |
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| 398 | #endif // _SHORT_ |
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| 399 | |
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| 400 | #ifdef _INT_ |
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| 401 | |
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[1bc154] | 402 | if(((A.H)[j][k]>(const BigInt&)INT_MAX) || ((A.H)[j][k]<(const BigInt&)INT_MIN)) |
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[6ba162] | 403 | { |
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| 404 | cerr<<"\nWARNING: ideal& ideal::Hosten_Sturmfels_ideal(matrix&, const " |
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| 405 | "term_ordering&):\nLLL-reduced kernel basis does not fit " |
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| 406 | "into the used basic data type int."<<endl; |
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| 407 | size=-3; |
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[2a94d3] | 408 | delete[] generator; |
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[6ba162] | 409 | return *this; |
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| 410 | } |
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| 411 | |
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| 412 | #endif // _INT_ |
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| 413 | |
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| 414 | #ifdef _LONG_ |
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| 415 | |
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[1bc154] | 416 | if(((A.H)[j][k]>(const BigInt&)LONG_MAX) || ((A.H)[j][k]<(const BigInt&)LONG_MIN)) |
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[6ba162] | 417 | { |
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| 418 | cerr<<"\nWARNING: ideal& ideal::Hosten_Sturmfels_ideal(matrix&, const " |
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| 419 | "term_ordering&):\nLLL-reduced kernel basis does not fit " |
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| 420 | "into the used basic data type long."<<endl; |
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| 421 | size=-3; |
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[2a94d3] | 422 | delete[] generator; |
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[6ba162] | 423 | return *this; |
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| 424 | } |
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| 425 | |
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| 426 | #endif // _LONG_ |
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| 427 | |
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| 428 | generator[k]=(A.H)[j][k]; |
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| 429 | } |
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| 430 | |
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| 431 | // verifie term ordering |
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| 432 | if(w.weight(generator)!=0) |
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| 433 | cerr<<"\nWARNING: ideal& ideal::Hosten_Sturmfels_ideal(matrix&, " |
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| 434 | "const term_ordering&):\nInvalid row space vector does not induce " |
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| 435 | "homogenous grading."<<endl; |
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| 436 | |
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| 437 | binomial* bin=new binomial(A.columns,generator,w); |
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| 438 | add_generator(*bin); |
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| 439 | } |
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| 440 | |
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[2a94d3] | 441 | delete[] generator; |
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[6ba162] | 442 | return *this; |
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| 443 | } |
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| 444 | |
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| 445 | ideal& ideal::DiBiase_Urbanke_ideal(matrix& A, const term_ordering& _w) |
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| 446 | { |
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| 447 | w=_w; |
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| 448 | |
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| 449 | if(A._kernel_dimension==-2) |
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| 450 | // kernel of A not yet computed, do this now |
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| 451 | A.LLL_kernel_basis(); |
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| 452 | |
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| 453 | if((A._kernel_dimension==-1) && (A.columns<0)) |
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| 454 | // error occurred in kernel computation or matrix corrupt |
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| 455 | { |
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| 456 | cout<<"\nWARNING: ideal& ideal::DiBiase_Urbanke_ideal(matrix&, const " |
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| 457 | "term_ordering&):\ncannot build ideal from a corrupt input matrix"<<endl; |
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| 458 | size=-1; |
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| 459 | return *this; |
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| 460 | } |
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| 461 | |
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| 462 | // now compute flip variables |
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| 463 | |
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| 464 | short* F; |
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| 465 | // set of flip variables |
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| 466 | // If F[i]==j, x_j will be flipped. |
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| 467 | |
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| 468 | short r=A.compute_flip_variables(F); |
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| 469 | // number of flip variables |
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| 470 | |
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| 471 | if(r<0) |
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| 472 | { |
---|
| 473 | cout<<"Kernel of the input matrix contains no vector with nonzero " |
---|
| 474 | "components.\nPlease use another algorithm."<<endl; |
---|
| 475 | size=-1; |
---|
| 476 | return *this; |
---|
| 477 | } |
---|
| 478 | |
---|
| 479 | // check term ordering (as far as possible) |
---|
| 480 | BOOLEAN ordering_okay=TRUE; |
---|
| 481 | |
---|
| 482 | if(_w.weight_refinement()!=W_LEX) |
---|
| 483 | ordering_okay=FALSE; |
---|
| 484 | |
---|
| 485 | if(r>0) |
---|
| 486 | { |
---|
| 487 | for(short i=0;i<_w.number_of_weighted_variables();i++) |
---|
| 488 | if((_w[i]!=0) && (i!=F[0])) |
---|
| 489 | ordering_okay=FALSE; |
---|
| 490 | } |
---|
| 491 | |
---|
| 492 | if(ordering_okay==FALSE) |
---|
| 493 | cerr<<"\nWARNING: ideal& ideal::DiBiase_Urbanke_ideal(matrix&, const " |
---|
| 494 | "term_ordering&):\nargument term ordering might be inappropriate"<<endl; |
---|
| 495 | |
---|
[2a94d3] | 496 | Integer *generator=new Integer[A.columns]; |
---|
[6ba162] | 497 | // The algorithm of DiBiase and Urbanke does not need supplementary |
---|
| 498 | // variables. |
---|
| 499 | |
---|
| 500 | // compute initial generating system from the kernel of A |
---|
| 501 | for(short j=0;j<A._kernel_dimension;j++) |
---|
| 502 | { |
---|
| 503 | |
---|
| 504 | for(short k=0;k<A.columns;k++) |
---|
| 505 | { |
---|
| 506 | |
---|
| 507 | // We should first verifie if the components of the LLL-reduced lattice |
---|
| 508 | // basis fit into the basic data type (Integer as defined in globals.h). |
---|
| 509 | // This overflow control does of course not detect overflows in the |
---|
| 510 | // course of the LLL-algorithm! |
---|
| 511 | |
---|
| 512 | #ifdef _SHORT_ |
---|
| 513 | |
---|
[7de4d7] | 514 | if(((A.H)[j][k]>(const BigInt &)SHRT_MAX) || ((A.H)[j][k]<(const BigInt &)SHRT_MIN)) |
---|
[6ba162] | 515 | { |
---|
| 516 | cerr<<"\nWARNING: ideal& ideal::DiBiase_Urbanke_ideal(matrix&, const " |
---|
| 517 | "term_ordering&):\nLLL-reduced kernel basis does not fit " |
---|
| 518 | "into the used basic data type short."<<endl; |
---|
| 519 | size=-3; |
---|
[2a94d3] | 520 | delete[] generator; |
---|
[6ba162] | 521 | return *this; |
---|
| 522 | } |
---|
| 523 | |
---|
| 524 | #endif // _SHORT_ |
---|
| 525 | |
---|
| 526 | #ifdef _INT_ |
---|
| 527 | |
---|
[1bc154] | 528 | if(((A.H)[j][k]>(const BigInt&)INT_MAX) || ((A.H)[j][k]<(const BigInt&)INT_MIN)) |
---|
[6ba162] | 529 | { |
---|
| 530 | cerr<<"\nWARNING: ideal& ideal::DiBiase_Urbanke_ideal(matrix&, const " |
---|
| 531 | "term_ordering&):\nLLL-reduced kernel basis does not fit " |
---|
| 532 | "into the used basic data type int."<<endl; |
---|
| 533 | size=-3; |
---|
[2a94d3] | 534 | delete[] generator; |
---|
[6ba162] | 535 | return *this; |
---|
| 536 | } |
---|
| 537 | |
---|
| 538 | #endif // _INT_ |
---|
| 539 | |
---|
| 540 | #ifdef _LONG_ |
---|
| 541 | |
---|
[1bc154] | 542 | if(((A.H)[j][k]>(const BigInt&)LONG_MAX) || ((A.H)[j][k]<(const BigInt&)LONG_MIN)) |
---|
[6ba162] | 543 | { |
---|
| 544 | cerr<<"\nWARNING: ideal& ideal::DiBiase_Urbanke_ideal(matrix&, const " |
---|
| 545 | "term_ordering&):\nLLL-reduced kernel basis does not fit " |
---|
| 546 | "into the used basic data type long."<<endl; |
---|
| 547 | size=-3; |
---|
[2a94d3] | 548 | delete[] generator; |
---|
[6ba162] | 549 | return *this; |
---|
| 550 | } |
---|
| 551 | |
---|
| 552 | #endif // _LONG_ |
---|
| 553 | generator[k]=(A.H)[j][k]; |
---|
| 554 | } |
---|
| 555 | |
---|
| 556 | // flip variables |
---|
| 557 | for(short l=0;l<r;l++) |
---|
| 558 | generator[F[l]]*=-1; |
---|
| 559 | |
---|
| 560 | binomial* bin=new binomial(A.columns,generator,w); |
---|
| 561 | add_generator(*bin); |
---|
| 562 | } |
---|
| 563 | delete[] F; |
---|
[2a94d3] | 564 | delete[] generator; |
---|
[6ba162] | 565 | return *this; |
---|
| 566 | } |
---|
| 567 | |
---|
| 568 | ideal& ideal::Bigatti_LaScala_Robbiano_ideal(matrix& A,const term_ordering& _w) |
---|
| 569 | { |
---|
| 570 | |
---|
| 571 | // check term ordering |
---|
| 572 | if((_w.weight_refinement()!=W_REV_LEX) && (_w.is_positive()==FALSE)) |
---|
| 573 | cerr<<"\nWARNING: ideal& ideal::Bigatti_LaScala_Robbiano_ideal(matrix&, " |
---|
| 574 | "const term_ordering&):\nargument term ordering should be a weighted " |
---|
| 575 | "reverse lexicographical \nwith positive weights"<<endl; |
---|
| 576 | |
---|
| 577 | w=_w; |
---|
| 578 | // The argument term_ordering should be given by a homogenous grading. |
---|
| 579 | |
---|
| 580 | if(A._kernel_dimension==-2) |
---|
| 581 | // kernel of A not yet computed, do this now |
---|
| 582 | A.LLL_kernel_basis(); |
---|
| 583 | |
---|
| 584 | if((A._kernel_dimension==-1) && (A.columns<0)) |
---|
| 585 | // error occurred in kernel computation or matrix corrupt |
---|
| 586 | { |
---|
| 587 | cout<<"\nWARNING: ideal& ideal::Bigatti_LaScala_Robbiano_ideal(matrix&, " |
---|
| 588 | "const term_ordering&):\n" |
---|
| 589 | "cannot build ideal from a corrupt input matrix"<<endl; |
---|
| 590 | size=-1; |
---|
| 591 | return *this; |
---|
| 592 | } |
---|
| 593 | |
---|
| 594 | // now compute saturation variables |
---|
| 595 | |
---|
| 596 | // The techniques for computing a small set of saturation variables are |
---|
| 597 | // useful here for the following two reasons: |
---|
| 598 | // - The head of the saturation generator involves less variables, is |
---|
| 599 | // smaller in term ordering. |
---|
| 600 | // - The weight of the pseudo-elimination variable is smaller. |
---|
| 601 | short* sat_var; |
---|
| 602 | short number_of_sat_var=A.hosten_shapiro(sat_var); |
---|
| 603 | |
---|
| 604 | float weight=0; |
---|
| 605 | for(short i=0;i<number_of_sat_var;i++) |
---|
| 606 | weight+=w[sat_var[i]]; |
---|
| 607 | |
---|
| 608 | w.append_weighted_variable(weight); |
---|
| 609 | // one supplementary variable used to saturate the ideal |
---|
| 610 | |
---|
[2a94d3] | 611 | Integer *generator=new Integer[A.columns+1]; |
---|
[6ba162] | 612 | // The algorithm of Bigatti, LaScala and Robbiano needs one supplementary |
---|
| 613 | // weighted variable. |
---|
| 614 | |
---|
| 615 | // first build "saturation generator" |
---|
| 616 | for(short k=0;k<A.columns;k++) |
---|
| 617 | generator[k]=0; |
---|
| 618 | for(short i=0;i<number_of_sat_var;i++) |
---|
| 619 | generator[sat_var[i]]=1; |
---|
| 620 | generator[A.columns]=-1; |
---|
| 621 | |
---|
| 622 | delete[] sat_var; |
---|
| 623 | |
---|
| 624 | binomial* bin=new binomial(A.columns+1,generator,w); |
---|
| 625 | add_generator(*bin); |
---|
| 626 | |
---|
| 627 | // compute initial generating system from the kernel of A |
---|
| 628 | for(short j=0;j<A._kernel_dimension;j++) |
---|
| 629 | { |
---|
| 630 | for(short k=0;k<A.columns;k++) |
---|
| 631 | { |
---|
| 632 | // We should first verifie if the components of the LLL-reduced lattice |
---|
| 633 | // basis fit into the basic data type (Integer as defined in globals.h). |
---|
| 634 | // This overflow control does of course not detect overflows in the |
---|
| 635 | // course of the LLL-algorithm! |
---|
| 636 | |
---|
| 637 | #ifdef _SHORT_ |
---|
| 638 | |
---|
[7de4d7] | 639 | if(((A.H)[j][k]>(const BigInt &)SHRT_MAX) || ((A.H)[j][k]<(const BigInt &)SHRT_MIN)) |
---|
[6ba162] | 640 | { |
---|
| 641 | cerr<<"\nWARNING: ideal& ideal::Bigatti_LaScala_Robbiano_ideal" |
---|
| 642 | "(matrix&, const term_ordering&):\nLLL-reduced kernel basis does " |
---|
| 643 | "not fit into the used basic data type short."<<endl; |
---|
| 644 | size=-3; |
---|
[2a94d3] | 645 | delete[] generator; |
---|
[6ba162] | 646 | return *this; |
---|
| 647 | } |
---|
| 648 | |
---|
| 649 | #endif // _SHORT_ |
---|
| 650 | |
---|
| 651 | #ifdef _INT_ |
---|
| 652 | |
---|
[1bc154] | 653 | if(((A.H)[j][k]>(const BigInt&)INT_MAX) || ((A.H)[j][k]<(const BigInt&)INT_MIN)) |
---|
[6ba162] | 654 | { |
---|
| 655 | cerr<<"\nWARNING: ideal& ideal::Bigatti_LaScala_Robbiano_ideal" |
---|
| 656 | "(matrix&, const term_ordering&):\nLLL-reduced kernel basis does " |
---|
| 657 | "not fit into the used basic data type int."<<endl; |
---|
| 658 | size=-3; |
---|
[2a94d3] | 659 | delete[] generator; |
---|
[6ba162] | 660 | return *this; |
---|
| 661 | } |
---|
| 662 | |
---|
| 663 | #endif // _INT_ |
---|
| 664 | |
---|
| 665 | #ifdef _LONG_ |
---|
| 666 | |
---|
[1bc154] | 667 | if(((A.H)[j][k]>(const BigInt&)LONG_MAX) || ((A.H)[j][k]<(const BigInt&)LONG_MIN)) |
---|
[6ba162] | 668 | { |
---|
| 669 | cerr<<"\nWARNING: ideal& ideal::Bigatti_LaScala_Robbiano_ideal" |
---|
| 670 | "(matrix&, const term_ordering&):\nLLL-reduced kernel basis does " |
---|
| 671 | "not fit into the used basic data type long."<<endl; |
---|
| 672 | size=-3; |
---|
| 673 | return *this; |
---|
| 674 | } |
---|
| 675 | |
---|
| 676 | #endif // _LONG_ |
---|
| 677 | generator[k]=(A.H)[j][k]; |
---|
| 678 | } |
---|
| 679 | generator[A.columns]=0; |
---|
| 680 | // saturation variable |
---|
| 681 | // Note that the relative order of the variables is important (because |
---|
| 682 | // of the reverse lexicographical refinement of the weight). |
---|
| 683 | |
---|
| 684 | if(w.weight(generator)!=0) |
---|
| 685 | cerr<<"\nWARNING: ideal& ideal::Bigatti_LaScala_Robbiano_ideal(matrix&, " |
---|
| 686 | "const term_ordering&):\nInvalid row space vector does not induce " |
---|
| 687 | "homogenous grading."<<endl; |
---|
| 688 | |
---|
| 689 | binomial* bin=new binomial(A.columns+1,generator,w); |
---|
| 690 | add_generator(*bin); |
---|
| 691 | // insert generator |
---|
| 692 | } |
---|
[2a94d3] | 693 | delete[] generator; |
---|
[6ba162] | 694 | return *this; |
---|
| 695 | } |
---|
| 696 | |
---|
| 697 | ///////////////////////////////////////////////////////////////////////////// |
---|
| 698 | //////////////// public member functions //////////////////////////////////// |
---|
| 699 | ///////////////////////////////////////////////////////////////////////////// |
---|
| 700 | |
---|
| 701 | /////////////////// constructors and destructor ///////////////////////////// |
---|
| 702 | |
---|
| 703 | ideal::ideal(matrix& A, const term_ordering& _w, const short& algorithm) |
---|
| 704 | { |
---|
| 705 | |
---|
| 706 | // check arguments as far as possible |
---|
| 707 | |
---|
| 708 | if(A.error_status()<0) |
---|
| 709 | { |
---|
| 710 | cerr<<"\nWARNING: ideal::ideal(matrix&, const term_ordering&, const " |
---|
| 711 | "short&):\ncannot create ideal from a corrupt input matrix"<<endl; |
---|
| 712 | size=-1; |
---|
| 713 | return; |
---|
| 714 | } |
---|
| 715 | |
---|
| 716 | if(_w.error_status()<0) |
---|
| 717 | { |
---|
| 718 | cerr<<"\nWARNING: ideal::ideal(matrix&, const term_ordering&, const " |
---|
| 719 | "short&):\ncannot create ideal with a corrupt input ordering"<<endl; |
---|
| 720 | size=-1; |
---|
| 721 | return; |
---|
| 722 | } |
---|
| 723 | |
---|
| 724 | if((_w.number_of_elimination_variables()!=0) && |
---|
| 725 | (_w.number_of_weighted_variables()!=A.columns)) |
---|
| 726 | cerr<<"\nWARNING: ideal& ideal::ideal(matrix&, const term_ordering&):\n" |
---|
| 727 | "argument term ordering might be inappropriate"<<endl; |
---|
| 728 | |
---|
| 729 | #ifdef SUPPORT_DRIVEN_METHODS_EXTENDED |
---|
| 730 | |
---|
| 731 | create_subset_tree(); |
---|
| 732 | |
---|
| 733 | #endif // SUPPORT_DRIVEN_METHODS_EXTENDED |
---|
| 734 | |
---|
| 735 | size=0; |
---|
| 736 | |
---|
| 737 | // initialize the S-pair flags with the default value |
---|
| 738 | // (this is not really necessray, but looks nicer when outputting the |
---|
| 739 | // ideal without having computed a Groebner basis) |
---|
| 740 | rel_primeness=1; |
---|
| 741 | M_criterion=2; |
---|
| 742 | F_criterion=0; |
---|
| 743 | B_criterion=8; |
---|
| 744 | second_criterion=0; |
---|
| 745 | |
---|
| 746 | interreduction_percentage=12.0; |
---|
| 747 | |
---|
| 748 | // construct the ideal according to the algorithm |
---|
| 749 | switch(algorithm) |
---|
| 750 | { |
---|
| 751 | case CONTI_TRAVERSO: |
---|
| 752 | Conti_Traverso_ideal(A,_w); |
---|
| 753 | break; |
---|
| 754 | case POSITIVE_CONTI_TRAVERSO: |
---|
| 755 | Positive_Conti_Traverso_ideal(A,_w); |
---|
| 756 | break; |
---|
| 757 | case POTTIER: |
---|
| 758 | Pottier_ideal(A,_w); |
---|
| 759 | break; |
---|
| 760 | case HOSTEN_STURMFELS: |
---|
| 761 | Hosten_Sturmfels_ideal(A,_w); |
---|
| 762 | break; |
---|
| 763 | case DIBIASE_URBANKE: |
---|
| 764 | DiBiase_Urbanke_ideal(A,_w); |
---|
| 765 | break; |
---|
| 766 | case BIGATTI_LASCALA_ROBBIANO: |
---|
| 767 | Bigatti_LaScala_Robbiano_ideal(A,_w); |
---|
| 768 | break; |
---|
| 769 | default: |
---|
| 770 | cerr<<"\nWARNING: ideal::ideal(matrix&, const term_ordering&, const " |
---|
| 771 | "short&):\nunknown algorithm for ideal construction"<<endl; |
---|
| 772 | size=-1; |
---|
| 773 | return; |
---|
| 774 | } |
---|
| 775 | number_of_new_binomials=size; |
---|
| 776 | } |
---|
| 777 | |
---|
| 778 | ideal::ideal(const ideal& I) |
---|
| 779 | { |
---|
| 780 | |
---|
| 781 | if(I.error_status()<0) |
---|
| 782 | cerr<<"\nWARNING: ideal::ideal(const ideal&):\n" |
---|
| 783 | "trying to create ideal from a corrupt one"<<endl; |
---|
| 784 | |
---|
| 785 | size=0; |
---|
| 786 | // the size is automatically incremented when copying the generators |
---|
| 787 | |
---|
| 788 | w=I.w; |
---|
| 789 | |
---|
| 790 | rel_primeness=I.rel_primeness; |
---|
| 791 | M_criterion=I.M_criterion; |
---|
| 792 | F_criterion=I.F_criterion; |
---|
| 793 | B_criterion=I.B_criterion; |
---|
| 794 | second_criterion=I.second_criterion; |
---|
| 795 | |
---|
| 796 | interreduction_percentage=I.interreduction_percentage; |
---|
| 797 | |
---|
| 798 | // copy generators |
---|
| 799 | // To be sure to get a real copy of the argument ideal, the lists |
---|
| 800 | // aux_list and new_generators are also copied. |
---|
| 801 | list_iterator iter; |
---|
| 802 | |
---|
| 803 | |
---|
| 804 | #ifdef NO_SUPPORT_DRIVEN_METHODS_EXTENDED |
---|
| 805 | |
---|
| 806 | iter.set_to_list(I.generators); |
---|
| 807 | |
---|
| 808 | while(iter.is_at_end()==FALSE) |
---|
| 809 | { |
---|
| 810 | binomial* bin=new binomial(iter.get_element()); |
---|
| 811 | add_generator(*bin); |
---|
| 812 | iter.next(); |
---|
| 813 | } |
---|
| 814 | |
---|
| 815 | iter.set_to_list(I.new_generators); |
---|
| 816 | |
---|
| 817 | while(iter.is_at_end()==FALSE) |
---|
| 818 | { |
---|
| 819 | binomial* bin=new binomial(iter.get_element()); |
---|
| 820 | add_new_generator(*bin); |
---|
| 821 | iter.next(); |
---|
| 822 | } |
---|
| 823 | |
---|
| 824 | #endif // NO_SUPPORT_DRIVEN_METHODS_EXTENDED |
---|
| 825 | |
---|
| 826 | #ifdef SUPPORT_DRIVEN_METHODS_EXTENDED |
---|
| 827 | |
---|
| 828 | create_subset_tree(); |
---|
| 829 | |
---|
| 830 | for(short i=0;i<Number_of_Lists;i++) |
---|
| 831 | { |
---|
| 832 | iter.set_to_list(I.generators[i]); |
---|
| 833 | |
---|
| 834 | while(iter.is_at_end()==FALSE) |
---|
| 835 | { |
---|
| 836 | binomial* bin=new binomial(iter.get_element()); |
---|
| 837 | add_generator(*bin); |
---|
| 838 | iter.next(); |
---|
| 839 | } |
---|
| 840 | } |
---|
| 841 | |
---|
| 842 | for(short i=0;i<Number_of_Lists;i++) |
---|
| 843 | { |
---|
| 844 | iter.set_to_list(I.new_generators[i]); |
---|
| 845 | |
---|
| 846 | while(iter.is_at_end()==FALSE) |
---|
| 847 | { |
---|
| 848 | binomial* bin=new binomial(iter.get_element()); |
---|
| 849 | add_new_generator(*bin); |
---|
| 850 | iter.next(); |
---|
| 851 | } |
---|
| 852 | } |
---|
| 853 | |
---|
| 854 | #endif // SUPPORT_DRIVEN_METHODS_EXTENDED |
---|
| 855 | |
---|
| 856 | iter.set_to_list(I.aux_list); |
---|
| 857 | |
---|
| 858 | while(iter.is_at_end()==FALSE) |
---|
| 859 | { |
---|
| 860 | binomial* bin=new binomial(iter.get_element()); |
---|
| 861 | aux_list._insert(*bin); |
---|
| 862 | iter.next(); |
---|
| 863 | } |
---|
| 864 | number_of_new_binomials=size; |
---|
| 865 | } |
---|
| 866 | |
---|
| 867 | ideal::ideal(ifstream& input, const term_ordering& _w, const short& |
---|
| 868 | number_of_generators) |
---|
| 869 | { |
---|
| 870 | if(_w.error_status()<0) |
---|
| 871 | { |
---|
| 872 | cerr<<"\nWARNING: ideal::ideal(ifstream&, const term_ordering&, const " |
---|
| 873 | "short&):\ncannot create ideal with a corrupt input ordering"<<endl; |
---|
| 874 | size=-1; |
---|
| 875 | return; |
---|
| 876 | } |
---|
| 877 | |
---|
| 878 | w=_w; |
---|
| 879 | |
---|
| 880 | // initialize the S-pair flags with the default value |
---|
| 881 | // (this is not really necessray, but looks nicer when outputting the |
---|
| 882 | // ideal without having computed a Groebner basis) |
---|
| 883 | rel_primeness=1; |
---|
| 884 | M_criterion=2; |
---|
| 885 | F_criterion=0; |
---|
| 886 | B_criterion=8; |
---|
| 887 | second_criterion=0; |
---|
| 888 | |
---|
| 889 | interreduction_percentage=12.0; |
---|
| 890 | |
---|
| 891 | #ifdef SUPPORT_DRIVEN_METHODS_EXTENDED |
---|
| 892 | |
---|
| 893 | create_subset_tree(); |
---|
| 894 | |
---|
| 895 | #endif // SUPPORT_DRIVEN_METHODS_EXTENDED |
---|
| 896 | |
---|
| 897 | short number_of_variables= |
---|
| 898 | w.number_of_elimination_variables()+w.number_of_weighted_variables(); |
---|
[2a94d3] | 899 | Integer* generator=new Integer[number_of_variables]; |
---|
[6ba162] | 900 | |
---|
| 901 | for(long i=0;i<number_of_generators;i++) |
---|
| 902 | { |
---|
| 903 | for(short j=0;j<number_of_variables;j++) |
---|
| 904 | { |
---|
| 905 | input>>generator[j]; |
---|
| 906 | |
---|
| 907 | if(!input) |
---|
| 908 | // input failure, set "error flag" |
---|
| 909 | { |
---|
| 910 | cerr<<"\nWARNING: ideal::ideal(ifstream&, const term_ordering&, " |
---|
| 911 | "const short&): \ninput failure when reading generator "<<i<<endl; |
---|
| 912 | size=-2; |
---|
[2a94d3] | 913 | delete[] generator; |
---|
[6ba162] | 914 | return; |
---|
| 915 | } |
---|
| 916 | } |
---|
| 917 | binomial* bin=new binomial(number_of_variables,generator,w); |
---|
| 918 | add_generator(*bin); |
---|
| 919 | } |
---|
| 920 | size=number_of_generators; |
---|
| 921 | number_of_new_binomials=size; |
---|
[2a94d3] | 922 | delete[] generator; |
---|
[6ba162] | 923 | } |
---|
| 924 | |
---|
| 925 | ideal::~ideal() |
---|
| 926 | { |
---|
| 927 | |
---|
| 928 | #ifdef SUPPORT_DRIVEN_METHODS_EXTENDED |
---|
| 929 | |
---|
| 930 | destroy_subset_tree(); |
---|
| 931 | |
---|
| 932 | #endif // SUPPORT_DRIVEN_METHODS_EXTENDED |
---|
| 933 | |
---|
| 934 | // The destructor of the lists is automatically called. |
---|
| 935 | } |
---|
| 936 | |
---|
| 937 | ///////////////////// object information //////////////////////////////////// |
---|
| 938 | |
---|
| 939 | long ideal::number_of_generators() const |
---|
| 940 | { |
---|
| 941 | return size; |
---|
| 942 | } |
---|
| 943 | |
---|
| 944 | short ideal::error_status() const |
---|
| 945 | { |
---|
| 946 | if(size<0) |
---|
| 947 | return -1; |
---|
| 948 | else |
---|
| 949 | return 0; |
---|
| 950 | } |
---|
| 951 | |
---|
| 952 | //////////////////////////// output ///////////////////////////////////////// |
---|
| 953 | |
---|
| 954 | void ideal::print() const |
---|
| 955 | { |
---|
| 956 | printf("\nterm ordering:\n"); |
---|
| 957 | w.print(); |
---|
| 958 | |
---|
| 959 | printf("\ngenerators:\n"); |
---|
| 960 | |
---|
| 961 | #ifdef SUPPORT_DRIVEN_METHODS_EXTENDED |
---|
| 962 | |
---|
| 963 | for(short i=0;i<Number_of_Lists;i++) |
---|
| 964 | generators[i].ordered_print(w); |
---|
| 965 | |
---|
| 966 | #endif // SUPPORT_DRIVEN_METHODS_EXTENDED |
---|
| 967 | |
---|
| 968 | #ifdef NO_SUPPORT_DRIVEN_METHODS_EXTENDED |
---|
| 969 | |
---|
| 970 | generators.ordered_print(w); |
---|
| 971 | |
---|
| 972 | #endif // NO_SUPPORT_DRIVEN_METHODS_EXTENDED |
---|
| 973 | |
---|
| 974 | printf("\nnumber of generators: %ld\n",size); |
---|
| 975 | } |
---|
| 976 | |
---|
| 977 | void ideal::print_all() const |
---|
| 978 | { |
---|
| 979 | print(); |
---|
| 980 | cout<<"\nCurrently used S-pair criteria:"<<endl; |
---|
| 981 | if(rel_primeness) |
---|
| 982 | cout<<"relatively prime leading terms"<<endl; |
---|
| 983 | if(M_criterion) |
---|
| 984 | cout<<"criterion M"<<endl; |
---|
| 985 | if(F_criterion) |
---|
| 986 | cout<<"criterion F"<<endl; |
---|
| 987 | if(B_criterion) |
---|
| 988 | cout<<"criterion B"<<endl; |
---|
| 989 | if(second_criterion) |
---|
| 990 | cout<<"second criterion"<<endl; |
---|
| 991 | cout<<"\nInterreduction frequency: "<<setprecision(1) |
---|
| 992 | <<interreduction_percentage<<" %"<<endl; |
---|
| 993 | } |
---|
| 994 | |
---|
| 995 | void ideal::print(FILE *output) const |
---|
| 996 | { |
---|
| 997 | fprintf(output,"\nterm ordering:\n"); |
---|
| 998 | w.print(output); |
---|
| 999 | |
---|
| 1000 | fprintf(output,"\ngenerators:\n"); |
---|
| 1001 | |
---|
| 1002 | #ifdef SUPPORT_DRIVEN_METHODS_EXTENDED |
---|
| 1003 | |
---|
| 1004 | for(short i=0;i<Number_of_Lists;i++) |
---|
| 1005 | generators[i].ordered_print(output,w); |
---|
| 1006 | |
---|
| 1007 | #endif // SUPPORT_DRIVEN_METHODS_EXTENDED |
---|
| 1008 | |
---|
| 1009 | #ifdef NO_SUPPORT_DRIVEN_METHODS_EXTENDED |
---|
| 1010 | |
---|
| 1011 | generators.ordered_print(output,w); |
---|
| 1012 | |
---|
| 1013 | #endif // NO_SUPPORT_DRIVEN_METHODS_EXTENDED |
---|
| 1014 | |
---|
| 1015 | fprintf(output,"\nnumber of generators: %ld\n",size); |
---|
| 1016 | |
---|
[bd19b6] | 1017 | fprintf(output,"\nInterreduction frequency: %.1f %% \n", interreduction_percentage); |
---|
[6ba162] | 1018 | } |
---|
| 1019 | |
---|
| 1020 | void ideal::print_all(FILE* output) const |
---|
| 1021 | { |
---|
| 1022 | print(output); |
---|
| 1023 | fprintf(output,"\nCurrently used S-pair criteria:\n"); |
---|
| 1024 | if(rel_primeness) |
---|
| 1025 | fprintf(output,"relatively prime leading terms\n"); |
---|
| 1026 | if(M_criterion) |
---|
| 1027 | fprintf(output,"criterion M\n"); |
---|
| 1028 | if(F_criterion) |
---|
| 1029 | fprintf(output,"criterion F\n"); |
---|
| 1030 | if(B_criterion) |
---|
| 1031 | fprintf(output,"criterion B\n"); |
---|
| 1032 | if(second_criterion) |
---|
| 1033 | fprintf(output,"second criterion\n"); |
---|
| 1034 | } |
---|
| 1035 | |
---|
| 1036 | void ideal::print(ofstream& output) const |
---|
| 1037 | { |
---|
| 1038 | output<<"\nterm ordering:\n"<<endl; |
---|
| 1039 | w.print(output); |
---|
| 1040 | |
---|
| 1041 | output<<"\ngenerators:\n"<<endl; |
---|
| 1042 | |
---|
| 1043 | #ifdef SUPPORT_DRIVEN_METHODS_EXTENDED |
---|
| 1044 | |
---|
| 1045 | for(short i=0;i<Number_of_Lists;i++) |
---|
| 1046 | generators[i].ordered_print(output,w); |
---|
| 1047 | |
---|
| 1048 | #endif // SUPPORT_DRIVEN_METHODS_EXTENDED |
---|
| 1049 | |
---|
| 1050 | #ifdef NO_SUPPORT_DRIVEN_METHODS_EXTENDED |
---|
| 1051 | |
---|
| 1052 | generators.ordered_print(output,w); |
---|
| 1053 | |
---|
| 1054 | #endif // NO_SUPPORT_DRIVEN_METHODS_EXTENDED |
---|
| 1055 | |
---|
| 1056 | output<<"\nnumber of generators: "<<size<<endl; |
---|
| 1057 | } |
---|
| 1058 | |
---|
| 1059 | void ideal::print_all(ofstream& output) const |
---|
| 1060 | { |
---|
| 1061 | print(output); |
---|
| 1062 | output<<"\nCurrently used S-pair criteria:"<<endl; |
---|
| 1063 | if(rel_primeness) |
---|
| 1064 | output<<"relatively prime leading terms"<<endl; |
---|
| 1065 | if(M_criterion) |
---|
| 1066 | output<<"criterion M"<<endl; |
---|
| 1067 | if(F_criterion) |
---|
| 1068 | output<<"criterion F"<<endl; |
---|
| 1069 | if(B_criterion) |
---|
| 1070 | output<<"criterion B"<<endl; |
---|
| 1071 | if(second_criterion) |
---|
| 1072 | output<<"second_criterion"<<endl; |
---|
| 1073 | output<<"\nInterreduction frequency: "<<setprecision(1) |
---|
| 1074 | <<interreduction_percentage<<" %"<<endl; |
---|
| 1075 | } |
---|
| 1076 | |
---|
| 1077 | void ideal::format_print(ofstream& output) const |
---|
| 1078 | { |
---|
| 1079 | #ifdef SUPPORT_DRIVEN_METHODS_EXTENDED |
---|
| 1080 | |
---|
| 1081 | for(short i=0;i<Number_of_Lists;i++) |
---|
| 1082 | generators[i].ordered_format_print(output,w); |
---|
| 1083 | |
---|
| 1084 | #endif // SUPPORT_DRIVEN_METHODS_EXTENDED |
---|
| 1085 | |
---|
| 1086 | #ifdef NO_SUPPORT_DRIVEN_METHODS_EXTENDED |
---|
| 1087 | |
---|
| 1088 | generators.ordered_format_print(output,w); |
---|
| 1089 | |
---|
| 1090 | #endif // NO_SUPPORT_DRIVEN_METHODS_EXTENDED |
---|
| 1091 | } |
---|
| 1092 | #endif // IDEAL_CC |
---|