Lines Matching full:matrix
83 // If J(x*) is rank deficient, then the covariance matrix C(x*) is
89 // matrix for y was identity. This is an important assumption. If this
94 // Where S is a positive semi-definite matrix denoting the covariance
104 // covariance matrix not equal to identity, then it is the user's
108 // is the inverse square root of the covariance matrix S.
115 // Since the computation of the covariance matrix requires computing
116 // the inverse of a potentially large matrix, this can involve a
119 // covariance matrix. Quite often just the block diagonal. This class
120 // allows the user to specify the parts of the covariance matrix that
122 // and store those parts of the covariance matrix.
138 // Numerical rank deficiency, where the rank of the matrix cannot be
241 // when the Jacobian matrix J is well conditioned. For
245 // detect when the matrix being factorized is not of full
247 // if the matrix is rank deficient (cholmod_rcond), but it is
253 // matrix. Therefore, if you are doing SPARSE_CHOLESKY, we
267 // Jacobian matrix is rank deficient.
274 // If the Jacobian matrix is near singular, then inverting J'J
280 // which is essentially a rank deficient matrix, we have
326 // As mentioned above, when the covariance matrix is near
346 // truncated matrix is still below
372 // Compute a part of the covariance matrix.
374 // The vector covariance_blocks, indexes into the covariance matrix
379 // Since the covariance matrix is symmetric, if the user passes
387 // what parts of the covariance matrix are computed. The full
399 // Return the block of the covariance matrix corresponding to
410 // parameter_block1_size x parameter_block2_size matrix. The
411 matrix.
