1 namespace Eigen { 2 3 /** \page Eigen2ToEigen3 Porting from Eigen2 to Eigen3 4 5 This page lists the most important API changes between Eigen2 and Eigen3, 6 and gives tips to help porting your application from Eigen2 to Eigen3. 7 8 \eigenAutoToc 9 10 \section CompatibilitySupport Eigen2 compatibility support 11 12 Up to version 3.2 %Eigen provides <a href="http://eigen.tuxfamily.org/dox-3.2/Eigen2SupportModes.html">Eigen2 support modes</a>. These are removed now, because they were barely used anymore and became hard to maintain after internal re-designs. 13 You can still use them by first <a href="http://eigen.tuxfamily.org/dox-3.2/Eigen2ToEigen3.html">porting your code to Eigen 3.2</a>. 14 15 \section Using The USING_PART_OF_NAMESPACE_EIGEN macro 16 17 The USING_PART_OF_NAMESPACE_EIGEN macro has been removed. In Eigen 3, just do: 18 \code 19 using namespace Eigen; 20 \endcode 21 22 \section ComplexDot Dot products over complex numbers 23 24 This is the single trickiest change between Eigen 2 and Eigen 3. It only affects code using \c std::complex numbers as scalar type. 25 26 Eigen 2's dot product was linear in the first variable. Eigen 3's dot product is linear in the second variable. In other words, the Eigen 2 code \code x.dot(y) \endcode is equivalent to the Eigen 3 code \code y.dot(x) \endcode In yet other words, dot products are complex-conjugated in Eigen 3 compared to Eigen 2. The switch to the new convention was commanded by common usage, especially with the notation \f$ x^Ty \f$ for dot products of column-vectors. 27 28 \section VectorBlocks Vector blocks 29 30 <table class="manual"> 31 <tr><th>Eigen 2</th><th>Eigen 3</th></th> 32 <tr><td>\code 33 vector.start(length) 34 vector.start<length>() 35 vector.end(length) 36 vector.end<length>() 37 \endcode</td><td>\code 38 vector.head(length) 39 vector.head<length>() 40 vector.tail(length) 41 vector.tail<length>() 42 \endcode</td></tr> 43 </table> 44 45 46 \section Corners Matrix Corners 47 48 <table class="manual"> 49 <tr><th>Eigen 2</th><th>Eigen 3</th></th> 50 <tr><td>\code 51 matrix.corner(TopLeft,r,c) 52 matrix.corner(TopRight,r,c) 53 matrix.corner(BottomLeft,r,c) 54 matrix.corner(BottomRight,r,c) 55 matrix.corner<r,c>(TopLeft) 56 matrix.corner<r,c>(TopRight) 57 matrix.corner<r,c>(BottomLeft) 58 matrix.corner<r,c>(BottomRight) 59 \endcode</td><td>\code 60 matrix.topLeftCorner(r,c) 61 matrix.topRightCorner(r,c) 62 matrix.bottomLeftCorner(r,c) 63 matrix.bottomRightCorner(r,c) 64 matrix.topLeftCorner<r,c>() 65 matrix.topRightCorner<r,c>() 66 matrix.bottomLeftCorner<r,c>() 67 matrix.bottomRightCorner<r,c>() 68 \endcode</td> 69 </tr> 70 </table> 71 72 Notice that Eigen3 also provides these new convenience methods: topRows(), bottomRows(), leftCols(), rightCols(). See in class DenseBase. 73 74 \section CoefficientWiseOperations Coefficient wise operations 75 76 In Eigen2, coefficient wise operations which have no proper mathematical definition (as a coefficient wise product) 77 were achieved using the .cwise() prefix, e.g.: 78 \code a.cwise() * b \endcode 79 In Eigen3 this .cwise() prefix has been superseded by a new kind of matrix type called 80 Array for which all operations are performed coefficient wise. You can easily view a matrix as an array and vice versa using 81 the MatrixBase::array() and ArrayBase::matrix() functions respectively. Here is an example: 82 \code 83 Vector4f a, b, c; 84 c = a.array() * b.array(); 85 \endcode 86 Note that the .array() function is not at all a synonym of the deprecated .cwise() prefix. 87 While the .cwise() prefix changed the behavior of the following operator, the array() function performs 88 a permanent conversion to the array world. Therefore, for binary operations such as the coefficient wise product, 89 both sides must be converted to an \em array as in the above example. On the other hand, when you 90 concatenate multiple coefficient wise operations you only have to do the conversion once, e.g.: 91 \code 92 Vector4f a, b, c; 93 c = a.array().abs().pow(3) * b.array().abs().sin(); 94 \endcode 95 With Eigen2 you would have written: 96 \code 97 c = (a.cwise().abs().cwise().pow(3)).cwise() * (b.cwise().abs().cwise().sin()); 98 \endcode 99 100 \section PartAndExtract Triangular and self-adjoint matrices 101 102 In Eigen 2 you had to play with the part, extract, and marked functions to deal with triangular and selfadjoint matrices. In Eigen 3, all these functions have been removed in favor of the concept of \em views: 103 104 <table class="manual"> 105 <tr><th>Eigen 2</th><th>Eigen 3</th></tr> 106 <tr><td>\code 107 A.part<UpperTriangular>(); 108 A.part<StrictlyLowerTriangular>(); \endcode</td> 109 <td>\code 110 A.triangularView<Upper>() 111 A.triangularView<StrictlyLower>()\endcode</td></tr> 112 <tr><td>\code 113 A.extract<UpperTriangular>(); 114 A.extract<StrictlyLowerTriangular>();\endcode</td> 115 <td>\code 116 A.triangularView<Upper>() 117 A.triangularView<StrictlyLower>()\endcode</td></tr> 118 <tr><td>\code 119 A.marked<UpperTriangular>(); 120 A.marked<StrictlyLowerTriangular>();\endcode</td> 121 <td>\code 122 A.triangularView<Upper>() 123 A.triangularView<StrictlyLower>()\endcode</td></tr> 124 <tr><td colspan="2"></td></tr> 125 <tr><td>\code 126 A.part<SelfAdfjoint|UpperTriangular>(); 127 A.extract<SelfAdfjoint|LowerTriangular>();\endcode</td> 128 <td>\code 129 A.selfadjointView<Upper>() 130 A.selfadjointView<Lower>()\endcode</td></tr> 131 <tr><td colspan="2"></td></tr> 132 <tr><td>\code 133 UpperTriangular 134 LowerTriangular 135 UnitUpperTriangular 136 UnitLowerTriangular 137 StrictlyUpperTriangular 138 StrictlyLowerTriangular 139 \endcode</td><td>\code 140 Upper 141 Lower 142 UnitUpper 143 UnitLower 144 StrictlyUpper 145 StrictlyLower 146 \endcode</td> 147 </tr> 148 </table> 149 150 \sa class TriangularView, class SelfAdjointView 151 152 \section TriangularSolveInPlace Triangular in-place solving 153 154 <table class="manual"> 155 <tr><th>Eigen 2</th><th>Eigen 3</th></tr> 156 <tr><td>\code A.triangularSolveInPlace<XxxTriangular>(Y);\endcode</td><td>\code A.triangularView<Xxx>().solveInPlace(Y);\endcode</td></tr> 157 </table> 158 159 160 \section Decompositions Matrix decompositions 161 162 Some of Eigen 2's matrix decompositions have been renamed in Eigen 3, while some others have been removed and are replaced by other decompositions in Eigen 3. 163 164 <table class="manual"> 165 <tr> 166 <th>Eigen 2</th> 167 <th>Eigen 3</th> 168 <th>Notes</th> 169 </tr> 170 <tr> 171 <td>LU</td> 172 <td>FullPivLU</td> 173 <td class="alt">See also the new PartialPivLU, it's much faster</td> 174 </tr> 175 <tr> 176 <td>QR</td> 177 <td>HouseholderQR</td> 178 <td class="alt">See also the new ColPivHouseholderQR, it's more reliable</td> 179 </tr> 180 <tr> 181 <td>SVD</td> 182 <td>JacobiSVD</td> 183 <td class="alt">We currently don't have a bidiagonalizing SVD; of course this is planned.</td> 184 </tr> 185 <tr> 186 <td>EigenSolver and friends</td> 187 <td>\code #include<Eigen/Eigenvalues> \endcode </td> 188 <td class="alt">Moved to separate module</td> 189 </tr> 190 </table> 191 192 \section LinearSolvers Linear solvers 193 194 <table class="manual"> 195 <tr><th>Eigen 2</th><th>Eigen 3</th><th>Notes</th></tr> 196 <tr><td>\code A.lu();\endcode</td> 197 <td>\code A.fullPivLu();\endcode</td> 198 <td class="alt">Now A.lu() returns a PartialPivLU</td></tr> 199 <tr><td>\code A.lu().solve(B,&X);\endcode</td> 200 <td>\code X = A.lu().solve(B); 201 X = A.fullPivLu().solve(B);\endcode</td> 202 <td class="alt">The returned by value is fully optimized</td></tr> 203 <tr><td>\code A.llt().solve(B,&X);\endcode</td> 204 <td>\code X = A.llt().solve(B); 205 X = A.selfadjointView<Lower>.llt().solve(B); 206 X = A.selfadjointView<Upper>.llt().solve(B);\endcode</td> 207 <td class="alt">The returned by value is fully optimized and \n 208 the selfadjointView API allows you to select the \n 209 triangular part to work on (default is lower part)</td></tr> 210 <tr><td>\code A.llt().solveInPlace(B);\endcode</td> 211 <td>\code B = A.llt().solve(B); 212 B = A.selfadjointView<Lower>.llt().solve(B); 213 B = A.selfadjointView<Upper>.llt().solve(B);\endcode</td> 214 <td class="alt">In place solving</td></tr> 215 <tr><td>\code A.ldlt().solve(B,&X);\endcode</td> 216 <td>\code X = A.ldlt().solve(B); 217 X = A.selfadjointView<Lower>.ldlt().solve(B); 218 X = A.selfadjointView<Upper>.ldlt().solve(B);\endcode</td> 219 <td class="alt">The returned by value is fully optimized and \n 220 the selfadjointView API allows you to select the \n 221 triangular part to work on</td></tr> 222 </table> 223 224 \section GeometryModule Changes in the Geometry module 225 226 The Geometry module is the one that changed the most. If you rely heavily on it, it's probably a good idea to use the <a href="http://eigen.tuxfamily.org/dox-3.2/Eigen2SupportModes.html">"Eigen 2 support modes"</a> to perform your migration. 227 228 \section Transform The Transform class 229 230 In Eigen 2, the Transform class didn't really know whether it was a projective or affine transformation. In Eigen 3, it takes a new \a Mode template parameter, which indicates whether it's \a Projective or \a Affine transform. There is no default value. 231 232 The Transform3f (etc) typedefs are no more. In Eigen 3, the Transform typedefs explicitly refer to the \a Projective and \a Affine modes: 233 234 <table class="manual"> 235 <tr><th>Eigen 2</th><th>Eigen 3</th><th>Notes</th></tr> 236 <tr> 237 <td> Transform3f </td> 238 <td> Affine3f or Projective3f </td> 239 <td> Of course 3f is just an example here </td> 240 </tr> 241 </table> 242 243 244 \section LazyVsNoalias Lazy evaluation and noalias 245 246 In Eigen all operations are performed in a lazy fashion except the matrix products which are always evaluated into a temporary by default. 247 In Eigen2, lazy evaluation could be enforced by tagging a product using the .lazy() function. However, in complex expressions it was not 248 easy to determine where to put the lazy() function. In Eigen3, the lazy() feature has been superseded by the MatrixBase::noalias() function 249 which can be used on the left hand side of an assignment when no aliasing can occur. Here is an example: 250 \code 251 MatrixXf a, b, c; 252 ... 253 c.noalias() += 2 * a.transpose() * b; 254 \endcode 255 However, the noalias mechanism does not cover all the features of the old .lazy(). Indeed, in some extremely rare cases, 256 it might be useful to explicit request for a lay product, i.e., for a product which will be evaluated one coefficient at once, on request, 257 just like any other expressions. To this end you can use the MatrixBase::lazyProduct() function, however we strongly discourage you to 258 use it unless you are sure of what you are doing, i.e., you have rigourosly measured a speed improvement. 259 260 \section AlignMacros Alignment-related macros 261 262 The EIGEN_ALIGN_128 macro has been renamed to EIGEN_ALIGN16. Don't be surprised, it's just that we switched to counting in bytes ;-) 263 264 The \link TopicPreprocessorDirectivesPerformance EIGEN_DONT_ALIGN \endlink option still exists in Eigen 3, but it has a new cousin: \link TopicPreprocessorDirectivesPerformance EIGEN_DONT_ALIGN_STATICALLY.\endlink It allows to get rid of all static alignment issues while keeping alignment of dynamic-size heap-allocated arrays. Vectorization of statically allocated arrays is still preserved (unless you define \link TopicPreprocessorDirectivesPerformance EIGEN_UNALIGNED_VECTORIZE \endlink =0), at the cost of unaligned memory stores. 265 266 \section AlignedMap Aligned Map objects 267 268 A common issue with Eigen 2 was that when mapping an array with Map, there was no way to tell Eigen that your array was aligned. There was a ForceAligned option but it didn't mean that; it was just confusing and has been removed. 269 270 New in Eigen3 is the #Aligned option. See the documentation of class Map. Use it like this: 271 \code 272 Map<Vector4f, Aligned> myMappedVector(some_aligned_array); 273 \endcode 274 There also are related convenience static methods, which actually are the preferred way as they take care of such things as constness: 275 \code 276 result = Vector4f::MapAligned(some_aligned_array); 277 \endcode 278 279 \section StdContainers STL Containers 280 281 In Eigen2, <tt>\#include\<Eigen/StdVector\></tt> tweaked std::vector to automatically align elements. The problem was that that was quite invasive. In Eigen3, we only override standard behavior if you use Eigen::aligned_allocator<T> as your allocator type. So for example, if you use std::vector<Matrix4f>, you need to do the following change (note that aligned_allocator is under namespace Eigen): 282 283 <table class="manual"> 284 <tr><th>Eigen 2</th><th>Eigen 3</th></tr> 285 <tr> 286 <td> \code std::vector<Matrix4f> \endcode </td> 287 <td> \code std::vector<Matrix4f, aligned_allocator<Matrix4f> > \endcode </td> 288 </tr> 289 </table> 290 291 \section eiPrefix Internal ei_ prefix 292 293 In Eigen2, global internal functions and structures were prefixed by \c ei_. In Eigen3, they all have been moved into the more explicit \c internal namespace. So, e.g., \c ei_sqrt(x) now becomes \c internal::sqrt(x). Of course it is not recommended to rely on Eigen's internal features. 294 295 296 297 */ 298 299 } 300
