Lines Matching full:matrix
25 \b The \b problem: You have a system of equations, that you have written as a single matrix equation
29 \b The \b solution: You can choose between various decompositions, depending on what your matrix \a A looks like,
41 matrix is of type Matrix3f, this line could have been replaced by:
49 depending on your matrix and the trade-off you want to make:
55 <th>Requirements on the matrix</th>
112 For example, if your matrix is positive definite, the above table says that a very good
114 matrix (not a vector) as right hand side is possible.
144 Make sure to check if your matrix is self-adjoint, as is often the case in these problems. Here's an example using
162 advantageously replaced by solve() operations, and the determinant is often \em not a good way of checking if a matrix
168 call inverse() and determinant() directly on a matrix. If your matrix is of a very small fixed size (at most 4x4) this
195 of the normal matrix. In any case, just read any reference text on least squares, and it will be very easy for you
202 at the time of the construction, the matrix that you will want to decompose; or if you want to reuse an existing
207 \li all decompositions have a compute(matrix) method that does the computation, and that may be called again
232 Certain decompositions are rank-revealing, i.e. are able to compute the rank of a matrix. These are typically
233 also the decompositions that behave best in the face of a non-full-rank matrix (which in the square case means a
234 singular matrix). On \ref TopicLinearAlgebraDecompositions "this table" you can see for all our decompositions
238 and some are also providing methods to compute the kernel (null-space) and image (column-space) of the matrix, as is the
250 floating-point matrix is \em exactly rank-deficient. Eigen picks a sensible default threshold, which depends
