Mercurial > hg > octave-jordi
annotate doc/interpreter/diagperm.txi @ 8917:d707aa3bbc36
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author | Jaroslav Hajek <highegg@gmail.com> |
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date | Fri, 06 Mar 2009 13:42:24 +0100 |
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rev | line source |
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1 @c Copyright (C) 2009 Jaroslav Hajek |
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2 @c |
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3 @c This file is part of Octave. |
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4 @c |
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5 @c Octave is free software; you can redistribute it and/or modify it |
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6 @c under the terms of the GNU General Public License as published by the |
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7 @c Free Software Foundation; either version 3 of the License, or (at |
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8 @c your option) any later version. |
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9 @c |
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10 @c Octave is distributed in the hope that it will be useful, but WITHOUT |
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11 @c ANY WARRANTY; without even the implied warranty of MERCHANTABILITY or |
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12 @c FITNESS FOR A PARTICULAR PURPOSE. See the GNU General Public License |
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13 @c for more details. |
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14 @c |
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15 @c You should have received a copy of the GNU General Public License |
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16 @c along with Octave; see the file COPYING. If not, see |
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17 @c <http://www.gnu.org/licenses/>. |
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18 |
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19 @node Diagonal and Permutation Matrices |
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20 @chapter Diagonal and Permutation Matrices |
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21 |
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22 @menu |
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23 * Basic Usage:: Creation and Manipulation of Diagonal and Permutation Matrices |
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24 * Matrix Algebra:: Linear Algebra with Diagonal and Permutation Matrices |
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25 * Function Support:: Functions That Are Aware of These Matrices |
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26 * Example Codes:: Some Examples of Usage |
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27 * Zeros Treatment:: The Differences in Treatment of Zero Elements |
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28 @end menu |
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29 |
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30 @node Basic Usage |
8917 | 31 @section Creating and Manipulating Diagonal and Permutation Matrices |
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32 |
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33 A diagonal matrix is defined as a matrix that has zero entries outside the main diagonal; |
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34 that is, |
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35 @iftex |
8917 | 36 @tex |
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37 $D_{ij} = 0$ if $i \neq j$ |
8917 | 38 @end tex |
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39 @end iftex |
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40 @ifnottex |
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41 @code{D(i,j) == 0} if @code{i != j}. |
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42 @end ifnottex |
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43 Most often, square diagonal matrices are considered; however, the definition can equally |
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44 be applied to nonsquare matrices, in which case we usually speak of a rectangular diagonal |
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45 matrix. |
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46 |
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47 A permutation matrix is defined as a square matrix that has a single element equal to unity |
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48 in each row and each column; all other elements are zero. That is, there exists a |
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49 permutation (vector) |
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50 @iftex |
8917 | 51 @tex |
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52 $p$ such that $P_{ij}=1$ if $j = p_i$ and |
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53 $P_{ij}=0$ otherwise. |
8917 | 54 @end tex |
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55 @end iftex |
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56 @ifnottex |
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57 @code{p} such that @code{P(i,j) == 1} if @code{j == p(i)} and |
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58 @code{P(i,j) == 0} otherwise. |
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59 @end ifnottex |
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60 |
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61 Octave provides special treatment of real and complex rectangular diagonal matrices, |
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62 as well as permutation matrices. They are stored as special objects, using efficient |
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63 storage and algorithms, facilitating writing both readable and efficient matrix algebra |
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64 expressions in the Octave language. |
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65 |
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66 @menu |
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67 * Creating Diagonal Matrices:: |
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68 * Creating Permutation Matrices:: |
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69 * Explicit and Implicit Conversions:: |
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70 @end menu |
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71 |
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72 @node Creating Diagonal Matrices |
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73 @subsection Creating Diagonal Matrices |
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74 |
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75 The most common and easiest way to create a diagonal matrix is using the built-in |
8917 | 76 function @dfn{diag}. The expression @code{diag (v)}, with @var{v} a vector, |
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77 will create a square diagonal matrix with elements on the main diagonal given |
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78 by the elements of @var{v}, and size equal to the length of @var{v}. |
8917 | 79 @code{diag (v, m, n)} can be used to construct a rectangular diagonal matrix. |
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80 The result of these expressions will be a special diagonal matrix object, rather |
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81 than a general matrix object. |
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82 |
8917 | 83 Diagonal matrix with unit elements can be created using @dfn{eye}. |
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84 Some other built-in functions can also return diagonal matrices. Examples include |
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85 @dfn{balance} or @dfn{inv}. |
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86 |
8917 | 87 Example: |
88 @example | |
89 diag (1:4) | |
90 @result{} | |
91 Diagonal Matrix | |
92 | |
93 1 0 0 0 | |
94 0 2 0 0 | |
95 0 0 3 0 | |
96 0 0 0 4 | |
97 | |
98 diag(1:3,5,3) | |
99 | |
100 @result{} | |
101 Diagonal Matrix | |
102 | |
103 1 0 0 | |
104 0 2 0 | |
105 0 0 3 | |
106 0 0 0 | |
107 0 0 0 | |
108 @end example | |
109 | |
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110 @node Creating Permutation Matrices |
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111 @subsection Creating Permutation Matrices |
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112 |
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113 For creating permutation matrices, Octave does not introduce a new function, but |
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114 rather overrides an existing syntax: permutation matrices can be conveniently |
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115 created by indexing an identity matrix by permutation vectors. |
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116 That is, if @var{q} is a permutation vector of length @var{n}, the expression |
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117 @example |
8917 | 118 P = eye (n) (:, q); |
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119 @end example |
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120 will create a permutation matrix - a special matrix object. |
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121 @example |
8917 | 122 eye (n) (q, :) |
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123 @end example |
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124 will also work (and create a row permutation matrix), as well as |
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125 @example |
8917 | 126 eye (n) (q1, q2). |
127 @end example | |
128 | |
129 For example: | |
130 @example | |
131 eye (4) ([1,3,2,4],:) | |
132 @result{} | |
133 Permutation Matrix | |
134 | |
135 1 0 0 0 | |
136 0 0 1 0 | |
137 0 1 0 0 | |
138 0 0 0 1 | |
139 | |
140 eye (4) (:,[1,3,2,4]) | |
141 @result{} | |
142 Permutation Matrix | |
143 | |
144 1 0 0 0 | |
145 0 0 1 0 | |
146 0 1 0 0 | |
147 0 0 0 1 | |
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148 @end example |
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149 |
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150 Mathematically, an identity matrix is both diagonal and permutation matrix. |
8917 | 151 In Octave, @code{eye (n)} returns a diagonal matrix, because a matrix |
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152 can only have one class. You can convert this diagonal matrix to a permutation |
8917 | 153 matrix by indexing it by an identity permutation, as shown below. |
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154 This is a special property of the identity matrix; indexing other diagonal |
8917 | 155 matrices generally produces a full matrix. |
156 | |
157 @example | |
158 eye (3) | |
159 @result{} | |
160 Diagonal Matrix | |
161 | |
162 1 0 0 | |
163 0 1 0 | |
164 0 0 1 | |
165 | |
166 eye(3)(1:3,:) | |
167 @result{} | |
168 Permutation Matrix | |
169 | |
170 1 0 0 | |
171 0 1 0 | |
172 0 0 1 | |
173 @end example | |
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174 |
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175 Some other built-in functions can also return permutation matrices. Examples include |
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176 @dfn{inv} or @dfn{lu}. |
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177 |
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178 @node Explicit and Implicit Conversions |
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179 @subsection Explicit and Implicit Conversions |
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180 |
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181 The diagonal and permutation matrices are special objects in their own right. A number |
8917 | 182 of operations and built-in functions are defined for these matrices to use special, |
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183 more efficient code than would be used for a full matrix in the same place. Examples |
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184 are given in further sections. |
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185 |
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186 To facilitate smooth mixing with full matrices, backward compatibility, and |
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187 compatibility with Matlab, the diagonal and permutation matrices should allow |
8917 | 188 any operation that works on full matrices, and will either treat it specially, |
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189 or implicitly convert themselves to full matrices. |
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190 |
8917 | 191 Instances include matrix indexing, except for extracting a single element or |
192 a leading submatrix, indexed assignment, or applying most mapper functions, | |
193 such as @dfn{exp}. | |
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194 |
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195 An explicit conversion to a full matrix can be requested using the built-in |
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196 function @dfn{full}. It should also be noted that the diagonal and permutation |
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197 matrix objects will cache the result of the conversion after it is first |
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198 requested (explicitly or implicitly), so that subsequent conversions will |
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199 be very cheap. |
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200 |
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201 @node Matrix Algebra |
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202 @section Linear Algebra with Diagonal and Permutation Matrices |
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203 |
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204 As has been already said, diagonal and permutation matrices make it |
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205 possible to use efficient algorithms while preserving natural linear |
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206 algebra syntax. This section describes in detail the operations that |
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207 are treated specially when performed on these special matrix objects. |
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208 |
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209 @menu |
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210 * Expressions Involving Diagonal Matrices:: |
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211 * Expressions Involving Permutation Matrices:: |
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212 @end menu |
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213 |
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214 @node Expressions Involving Diagonal Matrices |
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215 @subsection Expressions Involving Diagonal Matrices |
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216 |
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217 Assume @var{D} is a diagonal matrix. If @var{M} is a full matrix, |
8917 | 218 then @code{D*M} will scale the rows of @var{M}. That means, |
219 if @code{S = D*M}, then for each pair of indices | |
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220 i,j it holds |
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221 @iftex |
8917 | 222 @tex |
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223 $$S_{ij} = D_{ii} M_{ij}$$ |
8917 | 224 @end tex |
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225 @end iftex |
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226 @ifnottex |
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227 @example |
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228 S(i,j) = D(i,i) * M(i,j). |
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229 @end example |
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230 @end ifnottex |
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231 Similarly, @code{M*D} will do a column scaling. |
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232 |
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233 The matrix @var{D} may also be rectangular, m-by-n where @code{m != n}. |
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234 If @code{m < n}, then the expression @code{D*M} is equivalent to |
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235 @example |
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236 D(:,1:m) * M(1:m,:), |
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237 @end example |
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238 i.e. trailing @code{n-m} rows of @var{M} are ignored. If @code{m > n}, |
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239 then @code{D*M} is equivalent to |
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240 @example |
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241 [D(1:n,n) * M; zeros(m-n, columns (M))], |
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242 @end example |
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243 i.e. null rows are appended to the result. |
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244 The situation for right-multiplication @code{M*D} is analogous. |
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245 |
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246 The expressions @code{D \ M} and @code{M / D} perform inverse scaling. |
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247 They are equivalent to solving a diagonal (or rectangular diagonal) |
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248 in a least-squares minimum-norm sense. In exact arithmetics, this is |
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249 equivalent to multiplying by a pseudoinverse. The pseudoinverse of |
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250 a rectangular diagonal matrix is again a rectangular diagonal matrix |
8917 | 251 with swapped dimensions, where each nonzero diagonal element is replaced |
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252 by its reciprocal. |
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253 The matrix division algorithms do, in fact, use division rather than |
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254 multiplication by reciprocals for better numerical accuracy; otherwise, they |
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255 honor the above definition. Note that a diagonal matrix is never truncated due |
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256 to ill-conditioning; otherwise, it would not be much useful for scaling. This |
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257 is typically consistent with linear algebra needs. A full matrix that only |
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258 happens to be diagonal (an is thus not a special object) is of course treated |
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259 normally. |
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260 |
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261 If @var{D1} and @var{D2} are both diagonal matrices, then the expressions |
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262 @example |
8917 | 263 D1 + D2 |
264 D1 - D2 | |
265 D1 * D2 | |
266 D1 / D2 | |
267 D1 \ D2 | |
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268 @end example |
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269 again produce diagonal matrices, provided that normal |
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270 dimension matching rules are obeyed. The relations used are same as described above. |
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271 |
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272 Also, a diagonal matrix @var{D} can be multiplied or divided by a scalar, or raised |
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273 to a scalar power if it is square, producing diagonal matrix result in all cases. |
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274 |
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275 A diagonal matrix can also be transposed or conjugate-transposed, giving the expected |
8917 | 276 result. Extracting a leading submatrix of a diagonal matrix, i.e. @code{D(1:m,1:n)}, |
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277 will produce a diagonal matrix, other indexing expressions will implicitly convert to |
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278 full matrix. |
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279 |
8917 | 280 Adding a diagonal matrix to a full matrix only operates on the diagonal elements. Thus, |
281 @example | |
282 A = A + eps * eye (n) | |
283 @end example | |
284 is an efficient method of augmenting the diagonal of a matrix. Subtraction works | |
285 analogically. | |
286 | |
287 When involved in expressions with other element-by-element operators, @code{.*}, | |
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288 @code{./}, @code{.\} or @code{.^}, an implicit conversion to full matrix will |
8917 | 289 take place. This is not always strictly necessary but chosen to facilitate |
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290 better consistency with Matlab. |
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291 |
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292 @node Expressions Involving Permutation Matrices |
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293 @subsection Expressions Involving Permutation Matrices |
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294 |
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295 If @var{P} is a permutation matrix and @var{M} a matrix, the expression |
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296 @code{P*M} will permute the rows of @var{M}. Similarly, @code{M*P} will |
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297 yield a column permutation. |
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298 Matrix division @code{P\M} and @code{M/P} can be used to do inverse permutation. |
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299 |
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300 The previously described syntax for creating permutation matrices can actually |
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301 help an user to understand the connection between a permutation matrix and |
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302 a permuting vector. Namely, the following holds, where @code{I = eye (n)} |
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303 is an identity matrix: |
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304 @example |
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305 I(p,:) * M = (I*M) (p,:) = M(p,:) |
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306 @end example |
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307 Similarly, |
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308 @example |
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309 M * I(:,p) = (M*I) (:,p) = M(:,p) |
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310 @end example |
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311 |
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312 The expressions @code{I(p,:)} and @code{I(:,p)} are permutation matrices. |
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313 |
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314 A permutation matrix can be transposed (or conjugate-transposed, which is the |
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315 same, because a permutation matrix is never complex), inverting the |
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316 permutation, or equivalently, turning a row-permutation matrix into a |
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317 column-permutation one. For permutation matrices, transpose is equivalent to |
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318 inversion, thus @code{P\M} is equivalent to @code{P'*M}. Transpose of a |
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319 permutation matrix (or inverse) is a constant-time operation, flipping only a |
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320 flag internally, and thus the choice between the two above equivalent |
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321 expressions for inverse permuting is completely up to the user's taste. |
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322 |
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323 Two permutation matrices can be multiplied or divided (if their sizes match), performing |
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324 a composition of permutations. Also a permutation matrix can be indexed by a permutation |
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325 vector (or two vectors), giving again a permutation matrix. |
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326 Any other operations do not generally yield a permutation matrix and will thus |
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327 trigger the implicit conversion. |
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328 |
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329 @node Function Support |
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330 @section Functions That Are Aware of These Matrices |
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331 |
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332 This section lists the built-in functions that are aware of diagonal and |
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333 permutation matrices on input, or can return them as output. Passed to other |
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334 functions, these matrices will in general trigger an implicit conversion. |
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335 (Of course, user-defined dynamically linked functions may also work with |
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336 diagonal or permutation matrices). |
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337 |
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338 @menu |
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339 * Diagonal Matrix Functions:: |
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340 * Permutation Matrix Functions:: |
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341 @end menu |
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342 |
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343 @node Diagonal Matrix Functions |
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344 @subsection Diagonal Matrix Functions |
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345 |
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346 @dfn{inv} and @dfn{pinv} can be applied to a diagonal matrix, yielding again |
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347 a diagonal matrix. @dfn{det} will use an efficient straightforward calculation |
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348 when given a diagonal matrix, as well as @dfn{cond}. |
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349 The following mapper functions can be applied to a diagonal matrix |
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350 without converting it to a full one: |
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351 @dfn{abs}, @dfn{real}, @dfn{imag}, @dfn{conj}, @dfn{sqrt}. |
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352 A diagonal matrix can also be returned from the @dfn{balance} |
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353 and @dfn{svd} functions. |
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354 |
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355 @node Permutation Matrix Functions |
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356 @subsection Permutation Matrix Functions |
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357 |
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358 @dfn{inv} and @dfn{pinv} will invert a permutation matrix, preserving its |
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359 specialness. @dfn{det} can be applied to a permutation matrix, efficiently |
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360 calculating the sign of the permutation (which is equal to the determinant). |
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361 |
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362 A permutation matrix can also be returned from the built-in functions |
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363 @dfn{lu} and @dfn{qr}, if a pivoted factorization is requested. |
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364 |
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365 @node Example Codes |
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366 @section Some Examples of Usage |
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367 |
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368 The following can be used to solve a linear system @code{A*x = b} |
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369 using the pivoted LU factorization: |
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370 @example |
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371 [L, U, P] = lu (A); ## now L*U = P*A |
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372 x = U \ L \ P*b; |
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373 @end example |
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374 |
8917 | 375 @noindent |
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376 This is how you normalize columns of a matrix @var{X} to unit norm: |
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377 @example |
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378 s = norm (X, "columns"); |
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379 X = diag (s) \ X; |
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380 @end example |
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381 |
8917 | 382 @noindent |
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383 The following expression is a way to efficiently calculate the sign of a |
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384 permutation, given by a permutation vector @var{p}. It will also work |
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385 in earlier versions of Octave, but slowly. |
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386 @example |
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387 det (eye (length (p))(p, :)) |
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388 @end example |
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389 |
8917 | 390 @noindent |
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391 Finally, here's how you solve a linear system @code{A*x = b} |
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392 with Tikhonov regularization (ridge regression) using SVD (a skeleton only): |
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393 @example |
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394 m = rows (A); n = columns (A); |
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395 [U, S, V] = svd (A); |
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396 ## determine the regularization factor alpha |
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397 ## alpha = ... |
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398 ## transform to orthogonal basis |
8917 | 399 b = U'*b; |
400 ## Use the standard formula, replacing A with S. | |
401 ## S is diagonal, so the following will be very fast and accurate. | |
402 x = (S'*S + alpha^2 * eye (n)) \ (S' * b); | |
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403 ## transform to solution basis |
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404 x = V*x; |
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405 @end example |
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406 |
8917 | 407 |
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408 @node Zeros Treatment |
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409 @section The Differences in Treatment of Zero Elements |
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410 |
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411 Making diagonal and permutation matrices special matrix objects in their own |
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412 right and the consequent usage of smarter algorithms for certain operations |
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413 implies, as a side effect, small differences in treating zeros. |
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414 The contents of this section applies also to sparse matrices, discussed in |
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415 the following chapter. |
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416 |
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417 The IEEE standard defines the result of the expressions @code{0*Inf} and |
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418 @code{0*NaN} as @code{NaN}, as it has been generally agreed that this is the |
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419 best compromise. |
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420 Numerical software dealing with structured and sparse matrices (including |
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421 Octave) however, almost always makes a distinction between a "numerical zero" |
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422 and an "assumed zero". |
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423 A "numerical zero" is a zero value occuring in a place where any floating-point |
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424 value could occur. It is normally stored somewhere in memory as an explicit |
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425 value. |
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426 An "assumed zero", on the contrary, is a zero matrix element implied by the |
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427 matrix structure (diagonal, triangular) or a sparsity pattern; its value is |
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428 usually not stored explicitly anywhere, but is implied by the underlying |
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429 data structure. |
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430 |
8917 | 431 The primary distinction is that an assumed zero, when multiplied |
432 by any number, or divided by any nonzero number, | |
433 yields *always* a zero, even when, e.g., multiplied by @code{Inf} | |
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434 or divided by @code{NaN}. |
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435 The reason for this behavior is that the numerical multiplication is not |
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436 actually performed anywhere by the underlying algorithm; the result is |
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437 just assumed to be zero. Equivalently, one can say that the part of the |
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438 computation involving assumed zeros is performed symbolically, not numerically. |
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439 |
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440 This behavior not only facilitates the most straightforward and efficient |
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441 implementation of algorithms, but also preserves certain useful invariants, |
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442 like: |
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443 @itemize |
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444 @item scalar * diagonal matrix is a diagonal matrix |
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445 @item sparse matrix / scalar preserves the sparsity pattern |
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446 @item permutation matrix * matrix is equivalent to permuting rows |
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447 @end itemize |
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448 all of these natural mathematical truths would be invalidated by treating |
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449 assumed zeros as numerical ones. |
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450 |
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451 Note that certain competing software does not strictly follow this principle |
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452 and converts assumed zeros to numerical zeros in certain cases, while not doing |
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453 so in other cases. As of today, there are no intentions to mimick such behavior |
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454 in Octave. |
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455 |
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456 Examples of effects of assumed zeros vs. numerical zeros: |
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457 @example |
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458 Inf * eye (3) |
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459 @result{} |
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460 Inf 0 0 |
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461 0 Inf 0 |
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462 0 0 Inf |
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463 |
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464 Inf * speye (3) |
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465 @result{} |
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466 Compressed Column Sparse (rows = 3, cols = 3, nnz = 3 [33%]) |
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467 |
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468 (1, 1) -> Inf |
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469 (2, 2) -> Inf |
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470 (3, 3) -> Inf |
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471 |
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472 Inf * full (eye (3)) |
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473 @result{} |
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474 Inf NaN NaN |
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475 NaN Inf NaN |
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476 NaN NaN Inf |
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477 |
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478 @end example |
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479 |
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480 @example |
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481 diag(1:3) * [NaN; 1; 1] |
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482 @result{} |
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483 NaN |
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484 2 |
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485 3 |
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486 |
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487 sparse(1:3,1:3,1:3) * [NaN; 1; 1] |
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488 @result{} |
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489 NaN |
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490 2 |
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491 3 |
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492 [1,0,0;0,2,0;0,0,3] * [NaN; 1; 1] |
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493 @result{} |
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494 NaN |
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495 NaN |
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496 NaN |
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497 @end example |
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498 |