All Packages Class Hierarchy
The bakslv_f77 method solves Ax = b where A is an upper triangular matrix.
The chlhsn_f77 method finds "THE L(L-TRANSPOSE) [WRITTEN LL+] DECOMPOSITION OF THE PERTURBED MODEL HESSIAN MATRIX A+MU*I(WHERE MU\0 AND I IS THE IDENTITY MATRIX) WHICH IS SAFELY POSITIVE DEFINITE.
The choldc_f77 method finds "THE PERTURBED L(L-TRANSPOSE) [WRITTEN LL+] DECOMPOSITION OF A+D, WHERE D IS A NON-NEGATIVE DIAGONAL MATRIX ADDED TO A IF NECESSARY TO ALLOW THE CHOLESKY DECOMPOSITION TO CONTINUE." Translated by Steve Verrill, April 15, 1998.
This method multiplies a constant times a portion of a column of a matrix and adds the product to the corresponding portion of another column of the matrix --- a portion of col2 is replaced by the corresponding portion of a*col1 + col2.
This method multiplies a constant times a portion of a column of a matrix and adds the product to the corresponding portion of another column of the matrix --- a portion of col2 is replaced by the corresponding portion of a*col1 + col2.
This method calculates the dot product of portions of two columns of a matrix.
This method calculates the dot product of portions of two columns of a matrix.
This method finds the index of the element of a portion of a column of a matrix that has the maximum absolute value.
This method finds the index of the element of a portion of a column of a matrix that has the maximum absolute value.
This method calculates the Euclidean norm of a portion of a column of a matrix.
This method calculates the Euclidean norm of a portion of a column of a matrix.
This method "applies a plane rotation." It is a modification of the LINPACK function DROT.
This method "applies a plane rotation." It is a modification of the LINPACK function DROT.
This method scales a portion of a column of a matrix by a constant.
This method scales a portion of a column of a matrix by a constant.
This method interchanges two columns of a matrix.
This method interchanges two columns of a matrix.
This method multiplies a constant times a portion of a column of a matrix x[ ][ ] and adds the product to the corresponding portion of a vector y[ ] --- a portion of y[ ] is replaced by the corresponding portion of ax[ ][j] + y[ ].
This method multiplies a constant times a portion of a column of a matrix x[ ][ ] and adds the product to the corresponding portion of a vector y[ ] --- a portion of y[ ] is replaced by the corresponding portion of ax[ ][j] + y[ ].
This method calculates the dot product of a portion of a column of a matrix and the corresponding portion of a vector.
This method calculates the dot product of a portion of a column of a matrix and the corresponding portion of a vector.
This method multiplies a constant times a portion of a vector y[ ] and adds the product to the corresponding portion of a column of a matrix x[ ][ ] --- a portion of column j of x[ ][ ] is replaced by the corresponding portion of ay[ ] + x[ ][j].
This method multiplies a constant times a portion of a vector y[ ] and adds the product to the corresponding portion of a column of a matrix x[ ][ ] --- a portion of column j of x[ ][ ] is replaced by the corresponding portion of ay[ ] + x[ ][j].
This method multiplies a constant times a vector and adds the product to another vector --- dy[ ] = da*dx[ ] + dy[ ].
This method multiplies a constant times a vector and adds the product to another vector --- dy[ ] = da*dx[ ] + dy[ ].
This method copies the vector dx[ ] to the vector dy[ ].
This method copies the vector dx[ ] to the vector dy[ ].
This method copies a portion of vector x[ ] to the corresponding portion of vector y[ ].
This method copies a portion of vector x[ ] to the corresponding portion of vector y[ ].
This method calculates the dot product of two vectors.
This method calculates the dot product of two vectors.
The dfault_f77 method sets default values for each input variable to the minimization algorithm.
This method uses the LU decomposition provided by DGEFA to obtain the determinant and/or inverse of a full rank n by n matrix.
This method uses the LU decomposition provided by DGEFA to obtain the determinant and/or inverse of a full rank n by n matrix.
This method decomposes an n by n matrix A into a product, LU, where L is a lower triangular matrix and U is an upper triangular matrix.
This method decomposes an n by n matrix A into a product, LU, where L is a lower triangular matrix and U is an upper triangular matrix.
This method uses the LU decomposition provided by DGEFA to solve the equation Ax = b where A is a full rank n by n matrix.
This method uses the LU decomposition provided by DGEFA to solve the equation Ax = b where A is a full rank n by n matrix.
This method calculates the Euclidean norm of the vector stored in dx[ ] with storage increment incx.
This method calculates the Euclidean norm of the vector stored in dx[ ] with storage increment incx.
This method calculates the Euclidean norm of a portion of a vector x[ ].
This method calculates the Euclidean norm of a portion of a vector x[ ].
The dogdrv_f77 method finds the next Newton iterate (xpls) by the double dogleg method.
The dogstp_f77 method finds the new step by the double dogleg appproach.
This method uses the Cholesky decomposition provided by DPOFA to obtain the determinant and/or inverse of a symmetric, positive definite matrix.
This method uses the Cholesky decomposition provided by DPOFA to obtain the determinant and/or inverse of a symmetric, positive definite matrix.
This method decomposes an p by p symmetric, positive definite matrix X into a product, R´R, where R is an upper triangular matrix and R´ is the transpose of R.
This method decomposes an p by p symmetric, positive definite matrix X into a product, R´R, where R is an upper triangular matrix and R´ is the transpose of R.
This method uses the Cholesky decomposition provided by DPOFA to solve the equation Ax = b where A is symmetric, positive definite.
This method uses the Cholesky decomposition provided by DPOFA to solve the equation Ax = b where A is symmetric, positive definite.
This method decomposes an n by p matrix X into a product, QR, of an orthogonal n by n matrix Q and an upper triangular n by p matrix R.
This method decomposes an n by p matrix X into a product, QR, of an orthogonal n by n matrix Q and an upper triangular n by p matrix R.
This method "applies the output of DQRDC to compute coordinate transformations, projections, and least squares solutions." For details, see the comments in the code.
This method "applies the output of DQRDC to compute coordinate transformations, projections, and least squares solutions." For details, see the comments in the code.
This method constructs a Givens plane rotation.
This method constructs a Givens plane rotation.
This method scales a vector by a constant.
This method scales a vector by a constant.
This method scales a portion of a vector by a constant.
This method scales a portion of a vector by a constant.
This method decomposes a n by p matrix X into a product UDV´ where ...
This method decomposes a n by p matrix X into a product UDV´ where ...
This method interchanges two vectors.
This method interchanges two vectors.
The enorm_f77 method calculates the Euclidean norm of a vector.
This method factors the n by n symmetric positive definite matrix A as RR´ where R is a lower triangular matrix.
The fdjac2 method computes a forward-difference approximation to the m by n Jacobian matrix associated with a specified problem of m functions in n variables.
This method performs a simple linear regression (y = a + b*x).
This method performs a 1-dimensional minimization.
The forslv_f77 method solves Ax = b where A is a lower triangular matrix.
The fstocd_f77 method finds a central difference approximation to the gradient of the function to be minimized.
This version of the fstofd_f77 method finds first order finite difference approximations for gradients.
This version of the fstofd_f77 method finds a finite difference approximation to the Hessian.
This method takes a set of sorted data and returns the associated normal scores (Weisberg-Bingham versions) and the value of the Weisberg-Bingham version of the Shapiro-Wilk statistic for this data.
The grdchk_f77 method checks the analytic gradient supplied by the user.
The heschk_f77 method checks the analytic Hessian supplied by the user.
The hookdr_f77 method finds a next Newton iterate (xpls) by the More-Hebdon technique.
The hookst_f77 method finds a new step by the More-Hebdon algorithm.
The hsnint_f77 method provides the initial Hessian when secant updates are being used.
This method obtains the inverse of an n by n
symmetric positive definite matrix A.
This method finds the index of the element of a vector that has the maximum absolute value.
This method finds the index of the element of a vector that has the maximum absolute value.
The lltslv_f77 method solves Ax = b where A has the form L(L transpose) but only the lower triangular part, L, is stored.
The lmder1_f77 method minimizes the sum of the squares of m nonlinear functions in n variables by a modification of the Levenberg-Marquardt algorithm.
The lmder_f77 method minimizes the sum of the squares of m nonlinear functions in n variables by a modification of the Levenberg-Marquardt algorithm.
The lmdif1_f77 method minimizes the sum of the squares of m nonlinear functions in n variables by a modification of the Levenberg-Marquardt algorithm.
The lmdif_f77 method minimizes the sum of the squares of m nonlinear functions in n variables by a modification of the Levenberg-Marquardt algorithm.
Given an m by n matrix A, an n by n nonsingular diagonal matrix D, an m-vector b, and a positive number delta, the problem is to determine a value for the parameter par such that if x solves the system
A*x = b , sqrt(par)*D*x = 0
in the least squares sense, and dxnorm is the Euclidean
norm of D*x, then either par is zero and
(dxnorm-delta) <= 0.1*delta ,
or par is positive and
abs(dxnorm-delta) <= 0.1*delta .
The lnsrch_f77 method finds a next Newton iterate by line search.
This method multiplies an n x p matrix by a p x r matrix.
This method multiplies an n x p matrix by a p x r matrix.
This method obtains the transpose of an n x p matrix.
This method obtains the transpose of an n x p matrix.
This method multiplies an n x p matrix by a p x 1 vector.
This method multiplies an n x p matrix by a p x 1 vector.
The mvmltl_f77 method computes y = Lx where L is a lower triangular matrix stored in A.
The mvmlts_f77 method computes y = Ax where A is a symmetric matrix stored in its lower triangular part.
The mvmltu_f77 method computes Y = (L transpose)X where L is a lower triangular matrix stored in A (L transpose is taken implicitly).
This is a normal cdf inverse routine.
This is a normal cdf inverse routine.
This is a normal cdf inverse routine.
This is a normal cdf inverse routine.
The optchk_f77 method checks the input for reasonableness.
The optdrv_f77 method is the driver for the nonlinear optimization problem.
The optif0_f77 method minimizes a smooth nonlinear function of n variables.
The optif9_f77 method minimizes a smooth nonlinear function of n variables.
The optstp_f77 method determines whether the algorithm should terminate due to any of the following: 1) problem solved within user tolerance 2) convergence within user tolerance 3) iteration limit reached 4) divergence or too restrictive maximum step (stepmx) suspected Translated by Steve Verrill, May 12, 1998.
The qraux1_f77 method interchanges rows i,i+1 of the upper Hessenberg matrix r, columns i to n.
The qraux2_f77 method pre-multiplies r by the Jacobi rotation j(i,i+1,a,b).
The qrfac_f77 method uses Householder transformations with column pivoting (optional) to compute a QR factorization of the m by n matrix A.
Given an m by n matrix A, an n by n diagonal matrix D, and an m-vector b, the problem is to determine an x which solves the system
Ax = b , Dx = 0 ,
in the least squares sense.
The qrupdt_f77 method finds an orthogonal n by n matrix, Q*, and an upper triangular n by n matrix, R*, such that (Q*)(R*) = R+U(V+).
The result_f77 method prints information.
This method attempts to perform an n*log(n) sort rather than an n*n sort.
The sclmul_f77 method multiplies a vector by a scalar.
The secfac_f77 method updates the Hessian by the BFGS factored technique.
The secunf_f77 method updates the Hessian by the BFGS unfactored approach.
This method implements the FORTRAN sign (not sin) function.
This method implements the FORTRAN sign (not sin) function.
The sndofd_f77 method finds second order forward finite difference approximations to the Hessian.
This method obtains the solution, y, of the equation Ly = b where L is a known full rank lower triangular n by n matrix, and b is a known vector of length n.
This method solves the equation
Ax = b
where A is a known n by n symmetric positive definite matrix,
and b is a known vector of length n.The tregup_f77 method decides whether to accept xpls = x + sc as the next iterate and update the trust region dlt.