1 namespace Eigen { 2 3 /** \page Eigen2ToEigen3 Porting from Eigen2 to Eigen3 4 5 <div class="bigwarning">Eigen2 support is deprecated in Eigen 3.2.x and it will be removed in Eigen 3.3.</div> 6 7 This page lists the most important API changes between Eigen2 and Eigen3, 8 and gives tips to help porting your application from Eigen2 to Eigen3. 9 10 \eigenAutoToc 11 12 \section CompatibilitySupport Eigen2 compatibility support 13 14 In order to ease the switch from Eigen2 to Eigen3, Eigen3 features \subpage Eigen2SupportModes "Eigen2 support modes". 15 16 The quick way to enable this is to define the \c EIGEN2_SUPPORT preprocessor token \b before including any Eigen header (typically it should be set in your project options). 17 18 A more powerful, \em staged migration path is also provided, which may be useful to migrate larger projects from Eigen2 to Eigen3. This is explained in the \ref Eigen2SupportModes "Eigen 2 support modes" page. 19 20 \section Using The USING_PART_OF_NAMESPACE_EIGEN macro 21 22 The USING_PART_OF_NAMESPACE_EIGEN macro has been removed. In Eigen 3, just do: 23 \code 24 using namespace Eigen; 25 \endcode 26 27 \section ComplexDot Dot products over complex numbers 28 29 This is the single trickiest change between Eigen 2 and Eigen 3. It only affects code using \c std::complex numbers as scalar type. 30 31 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. 32 33 \section VectorBlocks Vector blocks 34 35 <table class="manual"> 36 <tr><th>Eigen 2</th><th>Eigen 3</th></th> 37 <tr><td>\code 38 vector.start(length) 39 vector.start<length>() 40 vector.end(length) 41 vector.end<length>() 42 \endcode</td><td>\code 43 vector.head(length) 44 vector.head<length>() 45 vector.tail(length) 46 vector.tail<length>() 47 \endcode</td></tr> 48 </table> 49 50 51 \section Corners Matrix Corners 52 53 <table class="manual"> 54 <tr><th>Eigen 2</th><th>Eigen 3</th></th> 55 <tr><td>\code 56 matrix.corner(TopLeft,r,c) 57 matrix.corner(TopRight,r,c) 58 matrix.corner(BottomLeft,r,c) 59 matrix.corner(BottomRight,r,c) 60 matrix.corner<r,c>(TopLeft) 61 matrix.corner<r,c>(TopRight) 62 matrix.corner<r,c>(BottomLeft) 63 matrix.corner<r,c>(BottomRight) 64 \endcode</td><td>\code 65 matrix.topLeftCorner(r,c) 66 matrix.topRightCorner(r,c) 67 matrix.bottomLeftCorner(r,c) 68 matrix.bottomRightCorner(r,c) 69 matrix.topLeftCorner<r,c>() 70 matrix.topRightCorner<r,c>() 71 matrix.bottomLeftCorner<r,c>() 72 matrix.bottomRightCorner<r,c>() 73 \endcode</td> 74 </tr> 75 </table> 76 77 Notice that Eigen3 also provides these new convenience methods: topRows(), bottomRows(), leftCols(), rightCols(). See in class DenseBase. 78 79 \section CoefficientWiseOperations Coefficient wise operations 80 81 In Eigen2, coefficient wise operations which have no proper mathematical definition (as a coefficient wise product) 82 were achieved using the .cwise() prefix, e.g.: 83 \code a.cwise() * b \endcode 84 In Eigen3 this .cwise() prefix has been superseded by a new kind of matrix type called 85 Array for which all operations are performed coefficient wise. You can easily view a matrix as an array and vice versa using 86 the MatrixBase::array() and ArrayBase::matrix() functions respectively. Here is an example: 87 \code 88 Vector4f a, b, c; 89 c = a.array() * b.array(); 90 \endcode 91 Note that the .array() function is not at all a synonym of the deprecated .cwise() prefix. 92 While the .cwise() prefix changed the behavior of the following operator, the array() function performs 93 a permanent conversion to the array world. Therefore, for binary operations such as the coefficient wise product, 94 both sides must be converted to an \em array as in the above example. On the other hand, when you 95 concatenate multiple coefficient wise operations you only have to do the conversion once, e.g.: 96 \code 97 Vector4f a, b, c; 98 c = a.array().abs().pow(3) * b.array().abs().sin(); 99 \endcode 100 With Eigen2 you would have written: 101 \code 102 c = (a.cwise().abs().cwise().pow(3)).cwise() * (b.cwise().abs().cwise().sin()); 103 \endcode 104 105 \section PartAndExtract Triangular and self-adjoint matrices 106 107 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: 108 109 <table class="manual"> 110 <tr><th>Eigen 2</th><th>Eigen 3</th></tr> 111 <tr><td>\code 112 A.part<UpperTriangular>(); 113 A.part<StrictlyLowerTriangular>(); \endcode</td> 114 <td>\code 115 A.triangularView<Upper>() 116 A.triangularView<StrictlyLower>()\endcode</td></tr> 117 <tr><td>\code 118 A.extract<UpperTriangular>(); 119 A.extract<StrictlyLowerTriangular>();\endcode</td> 120 <td>\code 121 A.triangularView<Upper>() 122 A.triangularView<StrictlyLower>()\endcode</td></tr> 123 <tr><td>\code 124 A.marked<UpperTriangular>(); 125 A.marked<StrictlyLowerTriangular>();\endcode</td> 126 <td>\code 127 A.triangularView<Upper>() 128 A.triangularView<StrictlyLower>()\endcode</td></tr> 129 <tr><td colspan="2"></td></tr> 130 <tr><td>\code 131 A.part<SelfAdfjoint|UpperTriangular>(); 132 A.extract<SelfAdfjoint|LowerTriangular>();\endcode</td> 133 <td>\code 134 A.selfadjointView<Upper>() 135 A.selfadjointView<Lower>()\endcode</td></tr> 136 <tr><td colspan="2"></td></tr> 137 <tr><td>\code 138 UpperTriangular 139 LowerTriangular 140 UnitUpperTriangular 141 UnitLowerTriangular 142 StrictlyUpperTriangular 143 StrictlyLowerTriangular 144 \endcode</td><td>\code 145 Upper 146 Lower 147 UnitUpper 148 UnitLower 149 StrictlyUpper 150 StrictlyLower 151 \endcode</td> 152 </tr> 153 </table> 154 155 \sa class TriangularView, class SelfAdjointView 156 157 \section TriangularSolveInPlace Triangular in-place solving 158 159 <table class="manual"> 160 <tr><th>Eigen 2</th><th>Eigen 3</th></tr> 161 <tr><td>\code A.triangularSolveInPlace<XxxTriangular>(Y);\endcode</td><td>\code A.triangularView<Xxx>().solveInPlace(Y);\endcode</td></tr> 162 </table> 163 164 165 \section Decompositions Matrix decompositions 166 167 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. 168 169 <table class="manual"> 170 <tr> 171 <th>Eigen 2</th> 172 <th>Eigen 3</th> 173 <th>Notes</th> 174 </tr> 175 <tr> 176 <td>LU</td> 177 <td>FullPivLU</td> 178 <td class="alt">See also the new PartialPivLU, it's much faster</td> 179 </tr> 180 <tr> 181 <td>QR</td> 182 <td>HouseholderQR</td> 183 <td class="alt">See also the new ColPivHouseholderQR, it's more reliable</td> 184 </tr> 185 <tr> 186 <td>SVD</td> 187 <td>JacobiSVD</td> 188 <td class="alt">We currently don't have a bidiagonalizing SVD; of course this is planned.</td> 189 </tr> 190 <tr> 191 <td>EigenSolver and friends</td> 192 <td>\code #include<Eigen/Eigenvalues> \endcode </td> 193 <td class="alt">Moved to separate module</td> 194 </tr> 195 </table> 196 197 \section LinearSolvers Linear solvers 198 199 <table class="manual"> 200 <tr><th>Eigen 2</th><th>Eigen 3</th><th>Notes</th></tr> 201 <tr><td>\code A.lu();\endcode</td> 202 <td>\code A.fullPivLu();\endcode</td> 203 <td class="alt">Now A.lu() returns a PartialPivLU</td></tr> 204 <tr><td>\code A.lu().solve(B,&X);\endcode</td> 205 <td>\code X = A.lu().solve(B); 206 X = A.fullPivLu().solve(B);\endcode</td> 207 <td class="alt">The returned by value is fully optimized</td></tr> 208 <tr><td>\code A.llt().solve(B,&X);\endcode</td> 209 <td>\code X = A.llt().solve(B); 210 X = A.selfadjointView<Lower>.llt().solve(B); 211 X = A.selfadjointView<Upper>.llt().solve(B);\endcode</td> 212 <td class="alt">The returned by value is fully optimized and \n 213 the selfadjointView API allows you to select the \n 214 triangular part to work on (default is lower part)</td></tr> 215 <tr><td>\code A.llt().solveInPlace(B);\endcode</td> 216 <td>\code B = A.llt().solve(B); 217 B = A.selfadjointView<Lower>.llt().solve(B); 218 B = A.selfadjointView<Upper>.llt().solve(B);\endcode</td> 219 <td class="alt">In place solving</td></tr> 220 <tr><td>\code A.ldlt().solve(B,&X);\endcode</td> 221 <td>\code X = A.ldlt().solve(B); 222 X = A.selfadjointView<Lower>.ldlt().solve(B); 223 X = A.selfadjointView<Upper>.ldlt().solve(B);\endcode</td> 224 <td class="alt">The returned by value is fully optimized and \n 225 the selfadjointView API allows you to select the \n 226 triangular part to work on</td></tr> 227 </table> 228 229 \section GeometryModule Changes in the Geometry module 230 231 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 \ref Eigen2SupportModes "Eigen 2 support modes" to perform your migration. 232 233 \section Transform The Transform class 234 235 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. 236 237 The Transform3f (etc) typedefs are no more. In Eigen 3, the Transform typedefs explicitly refer to the \a Projective and \a Affine modes: 238 239 <table class="manual"> 240 <tr><th>Eigen 2</th><th>Eigen 3</th><th>Notes</th></tr> 241 <tr> 242 <td> Transform3f </td> 243 <td> Affine3f or Projective3f </td> 244 <td> Of course 3f is just an example here </td> 245 </tr> 246 </table> 247 248 249 \section LazyVsNoalias Lazy evaluation and noalias 250 251 In Eigen all operations are performed in a lazy fashion except the matrix products which are always evaluated into a temporary by default. 252 In Eigen2, lazy evaluation could be enforced by tagging a product using the .lazy() function. However, in complex expressions it was not 253 easy to determine where to put the lazy() function. In Eigen3, the lazy() feature has been superseded by the MatrixBase::noalias() function 254 which can be used on the left hand side of an assignment when no aliasing can occur. Here is an example: 255 \code 256 MatrixXf a, b, c; 257 ... 258 c.noalias() += 2 * a.transpose() * b; 259 \endcode 260 However, the noalias mechanism does not cover all the features of the old .lazy(). Indeed, in some extremely rare cases, 261 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, 262 just like any other expressions. To this end you can use the MatrixBase::lazyProduct() function, however we strongly discourage you to 263 use it unless you are sure of what you are doing, i.e., you have rigourosly measured a speed improvement. 264 265 \section AlignMacros Alignment-related macros 266 267 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 ;-) 268 269 The EIGEN_DONT_ALIGN option still exists in Eigen 3, but it has a new cousin: EIGEN_DONT_ALIGN_STATICALLY. It allows to get rid of all static alignment issues while keeping alignment of dynamic-size heap-allocated arrays, thus keeping vectorization for dynamic-size objects. 270 271 \section AlignedMap Aligned Map objects 272 273 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. 274 275 New in Eigen3 is the #Aligned option. See the documentation of class Map. Use it like this: 276 \code 277 Map<Vector4f, Aligned> myMappedVector(some_aligned_array); 278 \endcode 279 There also are related convenience static methods, which actually are the preferred way as they take care of such things as constness: 280 \code 281 result = Vector4f::MapAligned(some_aligned_array); 282 \endcode 283 284 \section StdContainers STL Containers 285 286 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): 287 288 <table class="manual"> 289 <tr><th>Eigen 2</th><th>Eigen 3</th></tr> 290 <tr> 291 <td> \code std::vector<Matrix4f> \endcode </td> 292 <td> \code std::vector<Matrix4f, aligned_allocator<Matrix4f> > \endcode </td> 293 </tr> 294 </table> 295 296 \section eiPrefix Internal ei_ prefix 297 298 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. 299 300 301 302 */ 303 304 } 305
