Mercurial > octave
annotate libinterp/corefcn/inv.cc @ 30554:117ebe363f56 stable
Fix handling of scalar exceptional values in inv() (bug #61689)
* inv.cc (Finv): Pass "true" rather than '1' to inverse() function to match
'bool' argument. Pass 5th argument of true to inverse() to force calculation
of condition number. Rename variable "rcond_plus_one_eq_one" to "is_singular"
for clarity. Use isnan() to also catch singular matrices with NaN as
reciprocal condition number. Don't emit warning for inverse of a scalar
which thereby matches '/' and '\' operators. Add many BIST tests for various
exceptional value inputs.
* CMatrix.cc, dMatrix.cc, fCMatrix.cc, fMatrix.cc (inverse): Expand if
conditional for MatrixType::Diagonal to check input parameter "calc_cond" and
use an if/else tree to determine the reciprocal condition number (rcond) for
a scalar.
author | Rik <rik@octave.org> |
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date | Sat, 25 Dec 2021 19:16:44 -0800 |
parents | 98400baa509f |
children | 72cea722ca21 796f54d4ddbf |
rev | line source |
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1 //////////////////////////////////////////////////////////////////////// |
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2 // |
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3 // Copyright (C) 1996-2021 The Octave Project Developers |
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4 // |
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5 // See the file COPYRIGHT.md in the top-level directory of this |
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6 // distribution or <https://octave.org/copyright/>. |
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7 // |
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8 // This file is part of Octave. |
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9 // |
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10 // Octave is free software: you can redistribute it and/or modify it |
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11 // under the terms of the GNU General Public License as published by |
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12 // the Free Software Foundation, either version 3 of the License, or |
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13 // (at your option) any later version. |
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14 // |
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15 // Octave is distributed in the hope that it will be useful, but |
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16 // WITHOUT ANY WARRANTY; without even the implied warranty of |
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17 // MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the |
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18 // GNU General Public License for more details. |
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19 // |
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20 // You should have received a copy of the GNU General Public License |
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21 // along with Octave; see the file COPYING. If not, see |
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22 // <https://www.gnu.org/licenses/>. |
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23 // |
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24 //////////////////////////////////////////////////////////////////////// |
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26 #if defined (HAVE_CONFIG_H) |
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27 # include "config.h" |
2928 | 28 #endif |
29 | |
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30 #include "defun.h" |
2928 | 31 #include "error.h" |
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32 #include "errwarn.h" |
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33 #include "ovl.h" |
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34 #include "ops.h" |
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35 #include "ov-re-diag.h" |
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36 #include "ov-cx-diag.h" |
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37 #include "ov-flt-re-diag.h" |
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38 #include "ov-flt-cx-diag.h" |
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39 #include "ov-perm.h" |
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41 OCTAVE_NAMESPACE_BEGIN |
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42 |
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43 DEFUN (inv, args, nargout, |
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44 doc: /* -*- texinfo -*- |
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45 @deftypefn {} {@var{x} =} inv (@var{A}) |
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46 @deftypefnx {} {[@var{x}, @var{rcond}] =} inv (@var{A}) |
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47 @deftypefnx {} {[@dots{}] =} inverse (@dots{}) |
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48 Compute the inverse of the square matrix @var{A}. |
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49 |
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50 Return an estimate of the reciprocal condition number if requested, |
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51 otherwise warn of an ill-conditioned matrix if the reciprocal condition |
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52 number is small. |
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53 |
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54 In general it is best to avoid calculating the inverse of a matrix directly. |
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55 For example, it is both faster and more accurate to solve systems of |
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56 equations (@var{A}*@math{x} = @math{b}) with |
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57 @code{@var{y} = @var{A} \ @math{b}}, rather than |
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58 @code{@var{y} = inv (@var{A}) * @math{b}}. |
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59 |
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60 If called with a sparse matrix, then in general @var{x} will be a full |
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61 matrix requiring significantly more storage. Avoid forming the inverse of a |
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62 sparse matrix if possible. |
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63 |
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64 @code{inverse} is an alias and may be used identically in place of @code{inv}. |
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65 @seealso{ldivide, rdivide, pinv} |
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66 @end deftypefn */) |
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68 if (args.length () != 1) |
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69 print_usage (); |
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71 octave_value arg = args(0); | |
72 | |
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73 if (arg.isempty ()) |
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74 return ovl (Matrix ()); |
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76 if (arg.rows () != arg.columns ()) |
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77 err_square_matrix_required ("inverse", "A"); |
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79 octave_value result; |
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80 octave_idx_type info; |
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81 double rcond = 0.0; |
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82 float frcond = 0.0; |
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83 bool isfloat = arg.is_single_type (); |
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84 |
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85 if (arg.is_diag_matrix ()) |
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86 { |
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87 rcond = 1.0; |
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88 frcond = 1.0f; |
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89 if (arg.iscomplex ()) |
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90 { |
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91 if (isfloat) |
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92 { |
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93 result = arg.float_complex_diag_matrix_value ().inverse (info); |
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94 if (info == -1) |
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95 frcond = 0.0f; |
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96 else if (nargout > 1) |
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97 frcond = arg.float_complex_diag_matrix_value ().rcond (); |
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98 } |
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99 else |
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100 { |
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101 result = arg.complex_diag_matrix_value ().inverse (info); |
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102 if (info == -1) |
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103 rcond = 0.0; |
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104 else if (nargout > 1) |
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105 rcond = arg.complex_diag_matrix_value ().rcond (); |
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106 } |
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107 } |
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108 else |
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109 { |
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110 if (isfloat) |
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111 { |
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112 result = arg.float_diag_matrix_value ().inverse (info); |
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113 if (info == -1) |
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114 frcond = 0.0f; |
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115 else if (nargout > 1) |
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116 frcond = arg.float_diag_matrix_value ().rcond (); |
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117 } |
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118 else |
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119 { |
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120 result = arg.diag_matrix_value ().inverse (info); |
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121 if (info == -1) |
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122 rcond = 0.0; |
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123 else if (nargout > 1) |
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124 rcond = arg.diag_matrix_value ().rcond (); |
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125 } |
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126 } |
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127 } |
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128 else if (arg.is_perm_matrix ()) |
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129 { |
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130 info = 0; |
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131 rcond = 1.0; |
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132 result = arg.perm_matrix_value ().inverse (); |
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133 } |
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134 else if (isfloat) |
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136 if (arg.isreal ()) |
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137 { |
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138 FloatMatrix m = arg.float_matrix_value (); |
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139 |
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140 MatrixType mattyp = args(0).matrix_type (); |
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141 result = m.inverse (mattyp, info, frcond, true, true); |
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142 args(0).matrix_type (mattyp); |
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143 } |
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144 else if (arg.iscomplex ()) |
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145 { |
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146 FloatComplexMatrix m = arg.float_complex_matrix_value (); |
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147 |
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148 MatrixType mattyp = args(0).matrix_type (); |
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149 result = m.inverse (mattyp, info, frcond, true, true); |
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150 args(0).matrix_type (mattyp); |
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151 } |
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153 else |
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155 if (arg.isreal ()) |
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156 { |
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157 if (arg.issparse ()) |
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158 { |
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159 SparseMatrix m = arg.sparse_matrix_value (); |
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160 |
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161 MatrixType mattyp = args(0).matrix_type (); |
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162 result = m.inverse (mattyp, info, rcond, true, true); |
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163 args(0).matrix_type (mattyp); |
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164 } |
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165 else |
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166 { |
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167 Matrix m = arg.matrix_value (); |
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168 |
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169 MatrixType mattyp = args(0).matrix_type (); |
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170 result = m.inverse (mattyp, info, rcond, true, true); |
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171 args(0).matrix_type (mattyp); |
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172 } |
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173 } |
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174 else if (arg.iscomplex ()) |
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175 { |
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176 if (arg.issparse ()) |
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177 { |
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178 SparseComplexMatrix m = arg.sparse_complex_matrix_value (); |
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179 |
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180 MatrixType mattyp = args(0).matrix_type (); |
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181 result = m.inverse (mattyp, info, rcond, true, true); |
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182 args(0).matrix_type (mattyp); |
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183 } |
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184 else |
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185 { |
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186 ComplexMatrix m = arg.complex_matrix_value (); |
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187 |
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188 MatrixType mattyp = args(0).matrix_type (); |
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189 result = m.inverse (mattyp, info, rcond, true, true); |
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190 args(0).matrix_type (mattyp); |
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191 } |
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192 } |
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193 else |
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194 err_wrong_type_arg ("inv", arg); |
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196 |
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197 octave_value_list retval (nargout > 1 ? 2 : 1); |
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198 |
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199 retval(0) = result; |
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200 if (nargout > 1) |
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201 retval(1) = (isfloat ? octave_value (frcond) : octave_value (rcond)); |
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202 |
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203 if (nargout < 2) |
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204 { |
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205 bool is_singular; |
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206 |
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207 if (isfloat) |
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208 is_singular = ((frcond + 1.0f == 1.0f) || octave::math::isnan (frcond)) |
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209 && ! arg.is_scalar_type (); |
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210 else |
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211 is_singular = ((rcond + 1.0 == 1.0) || octave::math::isnan (rcond)) |
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212 && ! arg.is_scalar_type (); |
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213 |
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214 if (info == -1 || is_singular) |
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215 warn_singular_matrix (isfloat ? frcond : rcond); |
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216 } |
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218 return retval; | |
219 } | |
220 | |
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221 /* |
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222 ## Basic test for double/single matrices |
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223 %!assert (inv ([1, 2; 3, 4]), [-2, 1; 1.5, -0.5], 5*eps) |
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224 %!test |
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225 %! [xinv, rcond] = inv ([1,2;3,4]); |
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226 %! assert (xinv, [-2, 1; 1.5, -0.5], 5*eps); |
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227 %! assert (isa (rcond, "double")); |
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228 |
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229 %!assert (inv (single ([1, 2; 3, 4])), single ([-2, 1; 1.5, -0.5]), |
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230 %! 5*eps ("single")) |
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231 %!test |
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232 %! [xinv, rcond] = inv (single ([1,2;3,4])); |
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233 %! assert (xinv, single ([-2, 1; 1.5, -0.5]), 5*eps ("single")); |
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234 %! assert (isa (rcond, "single")); |
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235 |
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236 ## Normal scalar cases |
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237 %!assert (inv (2), 0.5) |
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238 %!test |
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239 %! [xinv, rcond] = inv (2); |
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240 %! assert (xinv, 0.5); |
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241 %! assert (rcond, 1); |
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242 %!assert (inv (single (2)), single (0.5)) |
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243 %!test |
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244 %! [xinv, rcond] = inv (single (2)); |
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245 %! assert (xinv, single (0.5)); |
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246 %! assert (rcond, single (1)); |
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247 %!assert (inv (complex (1, -1)), 0.5+0.5i) |
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248 %!test |
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249 %! [xinv, rcond] = inv (complex (1, -1)); |
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250 %! assert (xinv, 0.5+0.5i); |
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251 %! assert (rcond, 1); |
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252 %!assert (inv (complex (single (1), -1)), single (0.5+0.5i)) |
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253 %!test |
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254 %! [xinv, rcond] = inv (complex (single (1), -1)); |
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255 %! assert (xinv, single (0.5+0.5i)); |
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256 %! assert (rcond, single (1)); |
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257 |
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258 ## Test special inputs |
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259 ## Empty matrix |
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260 %!assert (inv (zeros (2,0)), []) |
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261 |
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262 ## Scalar "0" |
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263 %!assert (inv (0), Inf) |
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264 %!test |
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265 %! [xinv, rcond] = inv (0); |
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266 %! assert (xinv, Inf); |
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267 %! assert (rcond, 0); |
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268 %!assert (inv (single (0)), single (Inf)) |
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269 %!test |
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270 %! [xinv, rcond] = inv (single (0)); |
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271 %! assert (xinv, single (Inf)); |
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272 %! assert (rcond, single (0)); |
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273 %!assert (inv (complex (0, 0)), Inf) |
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274 %!test |
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275 %! [xinv, rcond] = inv (complex (0, 0)); |
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276 %! assert (xinv, Inf); |
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277 %! assert (rcond, 0); |
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278 %!assert (inv (complex (single (0), 0)), single (Inf)) |
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279 %!test |
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280 %! [xinv, rcond] = inv (complex (single (0), 0)); |
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281 %! assert (xinv, single (Inf)); |
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282 %! assert (rcond, single (0)); |
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283 ## NOTE: Matlab returns +Inf for -0 input, but it returns -Inf for 1/-0. |
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284 ## These should be the same, and in Octave they are. |
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285 %!assert (inv (-0), -Inf) |
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286 %!test |
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287 %! [xinv, rcond] = inv (-0); |
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288 %! assert (xinv, -Inf); |
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289 %! assert (rcond, 0); |
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290 |
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291 ## Scalar "Inf" |
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292 %!assert (inv (Inf), 0) |
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293 %!test |
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294 %! [xinv, rcond] = inv (Inf); |
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295 %! assert (xinv, 0); |
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296 %! assert (rcond, 0); |
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297 %!assert (inv (single (Inf)), single (0)) |
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298 %!test |
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299 %! [xinv, rcond] = inv (single (Inf)); |
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300 %! assert (xinv, single (0)); |
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301 %! assert (rcond, single (0)); |
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302 %!assert (inv (complex (1, Inf)), 0) |
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303 %!test |
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304 %! [xinv, rcond] = inv (complex (1, Inf)); |
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305 %! assert (xinv, 0); |
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306 %! assert (rcond, 0); |
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307 %!assert (inv (complex (single (1), Inf)), single (0)) |
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308 %!test |
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309 %! [xinv, rcond] = inv (complex (single (1), Inf)); |
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310 %! assert (xinv, single (0)); |
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311 %! assert (rcond, single (0)); |
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312 |
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313 ## Scalar "NaN" |
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314 %!assert (inv (NaN), NaN) |
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315 %!test |
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316 %! [xinv, rcond] = inv (NaN); |
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317 %! assert (xinv, NaN); |
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318 %! assert (rcond, NaN); |
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319 %!assert (inv (single (NaN)), single (NaN)) |
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320 %!test |
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321 %! [xinv, rcond] = inv (single (NaN)); |
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322 %! assert (xinv, single (NaN)); |
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323 %! assert (rcond, single (NaN)); |
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324 %!assert (inv (complex (1, NaN)), complex (NaN, NaN)) |
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325 %!test |
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326 %! [xinv, rcond] = inv (complex (1, NaN)); |
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327 %! assert (xinv, complex (NaN, NaN)); |
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328 %! assert (rcond, NaN); |
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329 %!assert (inv (complex (single (1), NaN)), complex (single (NaN), NaN)) |
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330 %!test |
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331 %! [xinv, rcond] = inv (complex (single (1), NaN)); |
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332 %! assert (xinv, complex (single (NaN), NaN)); |
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333 %! assert (rcond, single (NaN)); |
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334 |
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335 ## Matrix special values |
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336 ## Matrix of all zeroes |
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337 %!warning <matrix singular> assert (inv (zeros (2,2)), Inf (2,2)) |
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338 %!test |
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339 %! [xinv, rcond] = inv (zeros (2,2)); |
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340 %! assert (xinv, Inf (2,2)); |
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341 %! assert (rcond, 0); |
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342 ## Matrix of all Inf |
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343 %!warning <rcond = > assert (inv (Inf (2,2)), NaN (2,2)) |
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344 %!test |
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345 %! [xinv, rcond] = inv (Inf (2,2)); |
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346 %! assert (xinv, NaN (2,2)); |
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347 %! assert (rcond, NaN); |
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348 ## Matrix of all NaN |
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349 %!warning <rcond = > assert (inv (NaN (2,2)), NaN (2,2)) |
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350 %!test |
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351 %! [xinv, rcond] = inv (NaN (2,2)); |
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352 %! assert (xinv, NaN (2,2)); |
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353 %! assert (rcond, NaN); |
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354 |
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355 ## Special diagonal matrices |
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356 %!test |
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357 %! fail ("A = inv (diag ([1, 0, 1]))", "warning", "matrix singular"); |
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358 %! assert (A, diag ([Inf, Inf, Inf])); |
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359 |
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360 ## Special sparse matrices |
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361 %!testif HAVE_UMFPACK <*56232> |
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362 %! fail ("A = inv (sparse ([1, 2;0 ,0]))", "warning", "matrix singular"); |
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363 %! assert (A, sparse ([Inf, Inf; 0, 0])); |
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364 |
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365 %!testif HAVE_UMFPACK <*56232> |
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366 %! fail ("A = inv (sparse ([1i, 2;0 ,0]))", "warning", "matrix singular"); |
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367 %! assert (A, sparse ([Inf, Inf; 0, 0])); |
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368 |
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369 %!testif HAVE_UMFPACK <*56232> |
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370 %! fail ("A = inv (sparse ([1, 0, 0; 0, 0, 0; 0, 0, 1]))", "warning", "matrix singular"); |
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371 %! assert (A, sparse ([Inf, 0, 0; 0, 0, 0; 0, 0, Inf])); |
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372 |
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373 %!error inv () |
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374 %!error inv ([1, 2; 3, 4], 2) |
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375 %!error <must be a square matrix> inv ([1, 2; 3, 4; 5, 6]) |
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376 %!error <inverse of the null matrix not defined> inv (sparse (2, 2, 0)) |
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377 %!error <inverse of the null matrix not defined> inv (diag ([0, 0])) |
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378 %!error <inverse of the null matrix not defined> inv (diag (complex ([0, 0]))) |
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379 */ |
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380 |
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381 DEFALIAS (inverse, inv); |
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382 |
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383 OCTAVE_NAMESPACE_END |