Linear LeastSquares Fitting¶
This chapter describes routines for performing least squares fits to experimental data using linear combinations of functions. The data may be weighted or unweighted, i.e. with known or unknown errors. For weighted data the functions compute the best fit parameters and their associated covariance matrix. For unweighted data the covariance matrix is estimated from the scatter of the points, giving a variancecovariance matrix.
The functions are divided into separate versions for simple one or twoparameter regression and multipleparameter fits.
Overview¶
Leastsquares fits are found by minimizing (chisquared), the weighted sum of squared residuals over experimental datapoints for the model ,
The parameters of the model are . The weight factors are given by where is the experimental error on the datapoint . The errors are assumed to be Gaussian and uncorrelated. For unweighted data the chisquared sum is computed without any weight factors.
The fitting routines return the bestfit parameters and their covariance matrix. The covariance matrix measures the statistical errors on the bestfit parameters resulting from the errors on the data, , and is defined as
where denotes an average over the Gaussian error distributions of the underlying datapoints.
The covariance matrix is calculated by error propagation from the data errors . The change in a fitted parameter caused by a small change in the data is given by
allowing the covariance matrix to be written in terms of the errors on the data,
For uncorrelated data the fluctuations of the underlying datapoints satisfy
giving a corresponding parameter covariance matrix of
When computing the covariance matrix for unweighted data, i.e. data with unknown errors, the weight factors in this sum are replaced by the single estimate , where is the computed variance of the residuals about the bestfit model, . This is referred to as the variancecovariance matrix.
The standard deviations of the bestfit parameters are given by the square root of the corresponding diagonal elements of the covariance matrix, . The correlation coefficient of the fit parameters and is given by .
Linear regression¶
The functions in this section are used to fit simple one or two
parameter linear regression models. The functions are declared in
the header file gsl_fit.h
.
Linear regression with a constant term¶
The functions described in this section can be used to perform leastsquares fits to a straight line model, .

int gsl_fit_linear(const double *x, const size_t xstride, const double *y, const size_t ystride, size_t n, double *c0, double *c1, double *cov00, double *cov01, double *cov11, double *sumsq)¶
This function computes the bestfit linear regression coefficients (
c0
,c1
) of the model for the dataset (x
,y
), two vectors of lengthn
with stridesxstride
andystride
. The errors ony
are assumed unknown so the variancecovariance matrix for the parameters (c0
,c1
) is estimated from the scatter of the points around the bestfit line and returned via the parameters (cov00
,cov01
,cov11
). The sum of squares of the residuals from the bestfit line is returned insumsq
. Note: the correlation coefficient of the data can be computed usinggsl_stats_correlation()
, it does not depend on the fit.

int gsl_fit_wlinear(const double *x, const size_t xstride, const double *w, const size_t wstride, const double *y, const size_t ystride, size_t n, double *c0, double *c1, double *cov00, double *cov01, double *cov11, double *chisq)¶
This function computes the bestfit linear regression coefficients (
c0
,c1
) of the model for the weighted dataset (x
,y
), two vectors of lengthn
with stridesxstride
andystride
. The vectorw
, of lengthn
and stridewstride
, specifies the weight of each datapoint. The weight is the reciprocal of the variance for each datapoint iny
.The covariance matrix for the parameters (
c0
,c1
) is computed using the weights and returned via the parameters (cov00
,cov01
,cov11
). The weighted sum of squares of the residuals from the bestfit line, , is returned inchisq
.

int gsl_fit_linear_est(double x, double c0, double c1, double cov00, double cov01, double cov11, double *y, double *y_err)¶
This function uses the bestfit linear regression coefficients
c0
,c1
and their covariancecov00
,cov01
,cov11
to compute the fitted functiony
and its standard deviationy_err
for the model at the pointx
.
Linear regression without a constant term¶
The functions described in this section can be used to perform leastsquares fits to a straight line model without a constant term, .

int gsl_fit_mul(const double *x, const size_t xstride, const double *y, const size_t ystride, size_t n, double *c1, double *cov11, double *sumsq)¶
This function computes the bestfit linear regression coefficient
c1
of the model for the datasets (x
,y
), two vectors of lengthn
with stridesxstride
andystride
. The errors ony
are assumed unknown so the variance of the parameterc1
is estimated from the scatter of the points around the bestfit line and returned via the parametercov11
. The sum of squares of the residuals from the bestfit line is returned insumsq
.

int gsl_fit_wmul(const double *x, const size_t xstride, const double *w, const size_t wstride, const double *y, const size_t ystride, size_t n, double *c1, double *cov11, double *sumsq)¶
This function computes the bestfit linear regression coefficient
c1
of the model for the weighted datasets (x
,y
), two vectors of lengthn
with stridesxstride
andystride
. The vectorw
, of lengthn
and stridewstride
, specifies the weight of each datapoint. The weight is the reciprocal of the variance for each datapoint iny
.The variance of the parameter
c1
is computed using the weights and returned via the parametercov11
. The weighted sum of squares of the residuals from the bestfit line, , is returned inchisq
.
Multiparameter regression¶
This section describes routines which perform least squares fits to a linear model by minimizing the cost function
where is a vector of observations, is an by matrix of predictor variables, is a vector of the unknown bestfit parameters to be estimated, and . The matrix defines the weights or uncertainties of the observation vector.
This formulation can be used for fits to any number of functions and/or
variables by preparing the by matrix
appropriately. For example, to fit to a th order polynomial in
x
, use the following matrix,
where the index runs over the observations and the index runs from 0 to .
To fit to a set of sinusoidal functions with fixed frequencies , , , , use,
To fit to independent variables , , , , use,
where is the th value of the predictor variable .
The solution of the general linear leastsquares system requires an additional working space for intermediate results, such as the singular value decomposition of the matrix .
These functions are declared in the header file gsl_multifit.h
.

type gsl_multifit_linear_workspace¶
This workspace contains internal variables for fitting multiparameter models.

gsl_multifit_linear_workspace *gsl_multifit_linear_alloc(const size_t n, const size_t p)¶
This function allocates a workspace for fitting a model to a maximum of
n
observations using a maximum ofp
parameters. The user may later supply a smaller least squares system if desired. The size of the workspace is .

void gsl_multifit_linear_free(gsl_multifit_linear_workspace *work)¶
This function frees the memory associated with the workspace
w
.

int gsl_multifit_linear_svd(const gsl_matrix *X, gsl_multifit_linear_workspace *work)¶
This function performs a singular value decomposition of the matrix
X
and stores the SVD factors internally inwork
.

int gsl_multifit_linear_bsvd(const gsl_matrix *X, gsl_multifit_linear_workspace *work)¶
This function performs a singular value decomposition of the matrix
X
and stores the SVD factors internally inwork
. The matrixX
is first balanced by applying column scaling factors to improve the accuracy of the singular values.

int gsl_multifit_linear(const gsl_matrix *X, const gsl_vector *y, gsl_vector *c, gsl_matrix *cov, double *chisq, gsl_multifit_linear_workspace *work)¶
This function computes the bestfit parameters
c
of the model for the observationsy
and the matrix of predictor variablesX
, using the preallocated workspace provided inwork
. The by variancecovariance matrix of the model parameterscov
is set to , where is the standard deviation of the fit residuals. The sum of squares of the residuals from the bestfit, , is returned inchisq
. If the coefficient of determination is desired, it can be computed from the expression , where the total sum of squares (TSS) of the observationsy
may be computed fromgsl_stats_tss()
.The bestfit is found by singular value decomposition of the matrix
X
using the modified GolubReinsch SVD algorithm, with column scaling to improve the accuracy of the singular values. Any components which have zero singular value (to machine precision) are discarded from the fit.

int gsl_multifit_linear_tsvd(const gsl_matrix *X, const gsl_vector *y, const double tol, gsl_vector *c, gsl_matrix *cov, double *chisq, size_t *rank, gsl_multifit_linear_workspace *work)¶
This function computes the bestfit parameters
c
of the model for the observationsy
and the matrix of predictor variablesX
, using a truncated SVD expansion. Singular values which satisfy are discarded from the fit, where is the largest singular value. The by variancecovariance matrix of the model parameterscov
is set to , where is the standard deviation of the fit residuals. The sum of squares of the residuals from the bestfit, , is returned inchisq
. The effective rank (number of singular values used in solution) is returned inrank
. If the coefficient of determination is desired, it can be computed from the expression , where the total sum of squares (TSS) of the observationsy
may be computed fromgsl_stats_tss()
.

int gsl_multifit_wlinear(const gsl_matrix *X, const gsl_vector *w, const gsl_vector *y, gsl_vector *c, gsl_matrix *cov, double *chisq, gsl_multifit_linear_workspace *work)¶
This function computes the bestfit parameters
c
of the weighted model for the observationsy
with weightsw
and the matrix of predictor variablesX
, using the preallocated workspace provided inwork
. The by covariance matrix of the model parameterscov
is computed as . The weighted sum of squares of the residuals from the bestfit, , is returned inchisq
. If the coefficient of determination is desired, it can be computed from the expression , where the weighted total sum of squares (WTSS) of the observationsy
may be computed fromgsl_stats_wtss()
.

int gsl_multifit_wlinear_tsvd(const gsl_matrix *X, const gsl_vector *w, const gsl_vector *y, const double tol, gsl_vector *c, gsl_matrix *cov, double *chisq, size_t *rank, gsl_multifit_linear_workspace *work)¶
This function computes the bestfit parameters
c
of the weighted model for the observationsy
with weightsw
and the matrix of predictor variablesX
, using a truncated SVD expansion. Singular values which satisfy are discarded from the fit, where is the largest singular value. The by covariance matrix of the model parameterscov
is computed as . The weighted sum of squares of the residuals from the bestfit, , is returned inchisq
. The effective rank of the system (number of singular values used in the solution) is returned inrank
. If the coefficient of determination is desired, it can be computed from the expression , where the weighted total sum of squares (WTSS) of the observationsy
may be computed fromgsl_stats_wtss()
.

int gsl_multifit_linear_est(const gsl_vector *x, const gsl_vector *c, const gsl_matrix *cov, double *y, double *y_err)¶
This function uses the bestfit multilinear regression coefficients
c
and their covariance matrixcov
to compute the fitted function valuey
and its standard deviationy_err
for the model at the pointx
.

int gsl_multifit_linear_residuals(const gsl_matrix *X, const gsl_vector *y, const gsl_vector *c, gsl_vector *r)¶
This function computes the vector of residuals for the observations
y
, coefficientsc
and matrix of predictor variablesX
.

size_t gsl_multifit_linear_rank(const double tol, const gsl_multifit_linear_workspace *work)¶
This function returns the rank of the matrix which must first have its singular value decomposition computed. The rank is computed by counting the number of singular values which satisfy , where is the largest singular value.
Regularized regression¶
Ordinary weighted least squares models seek a solution vector which minimizes the residual
where is the by observation vector,
is the by design matrix, is
the by solution vector,
is the data weighting matrix,
and .
In cases where the least squares matrix is illconditioned,
small perturbations (ie: noise) in the observation vector could lead to
widely different solution vectors .
One way of dealing with illconditioned matrices is to use a “truncated SVD”
in which small singular values, below some given tolerance, are discarded
from the solution. The truncated SVD method is available using the functions
gsl_multifit_linear_tsvd()
and gsl_multifit_wlinear_tsvd()
. Another way
to help solve illposed problems is to include a regularization term in the least squares
minimization
for a suitably chosen regularization parameter and matrix . This type of regularization is known as Tikhonov, or ridge, regression. In some applications, is chosen as the identity matrix, giving preference to solution vectors with smaller norms. Including this regularization term leads to the explicit “normal equations” solution
which reduces to the ordinary least squares solution when . In practice, it is often advantageous to transform a regularized least squares system into the form
This is known as the Tikhonov “standard form” and has the normal equations solution
For an by matrix which is full rank and has (ie: is square or has more rows than columns), we can calculate the “thin” QR decomposition of , and note that since the factor will not change the norm. Since is by, we can then use the transformation
to achieve the standard form. For a rectangular matrix with ,
a more sophisticated approach is needed (see Hansen 1998, chapter 2.3).
In practice, the normal equations solution above is not desirable due to
numerical instabilities, and so the system is solved using the
singular value decomposition of the matrix .
The matrix is often chosen as the identity matrix, or as a first
or second finite difference operator, to ensure a smoothly varying
coefficient vector , or as a diagonal matrix to selectively damp
each model parameter differently. If , the user must first
convert the least squares problem to standard form using
gsl_multifit_linear_stdform1()
or gsl_multifit_linear_stdform2()
,
solve the system, and then backtransform the solution vector to recover
the solution of the original problem (see
gsl_multifit_linear_genform1()
and gsl_multifit_linear_genform2()
).
In many regularization problems, care must be taken when choosing the regularization parameter . Since both the residual norm and solution norm are being minimized, the parameter represents a tradeoff between minimizing either the residuals or the solution vector. A common tool for visualizing the comprimise between the minimization of these two quantities is known as the Lcurve. The Lcurve is a loglog plot of the residual norm on the horizontal axis and the solution norm on the vertical axis. This curve nearly always as an shaped appearance, with a distinct corner separating the horizontal and vertical sections of the curve. The regularization parameter corresponding to this corner is often chosen as the optimal value. GSL provides routines to calculate the Lcurve for all relevant regularization parameters as well as locating the corner.
Another method of choosing the regularization parameter is known as Generalized Cross Validation (GCV). This method is based on the idea that if an arbitrary element is left out of the right hand side, the resulting regularized solution should predict this element accurately. This leads to choosing the parameter which minimizes the GCV function
where is the matrix which relates the solution to the right hand side , ie: . GSL provides routines to compute the GCV curve and its minimum.
For most applications, the steps required to solve a regularized least squares problem are as follows:
Construct the least squares system (, , , )
Transform the system to standard form (, ). This step can be skipped if and .
Calculate the SVD of .
Determine an appropriate regularization parameter (using for example Lcurve or GCV analysis).
Solve the standard form system using the chosen and the SVD of .
Backtransform the standard form solution to recover the original solution vector .

int gsl_multifit_linear_stdform1(const gsl_vector *L, const gsl_matrix *X, const gsl_vector *y, gsl_matrix *Xs, gsl_vector *ys, gsl_multifit_linear_workspace *work)¶

int gsl_multifit_linear_wstdform1(const gsl_vector *L, const gsl_matrix *X, const gsl_vector *w, const gsl_vector *y, gsl_matrix *Xs, gsl_vector *ys, gsl_multifit_linear_workspace *work)¶
These functions define a regularization matrix . The diagonal matrix element is provided by the th element of the input vector
L
. The by least squares matrixX
and vectory
of length are then converted to standard form as described above and the parameters (, ) are stored inXs
andys
on output.Xs
andys
have the same dimensions asX
andy
. Optional data weights may be supplied in the vectorw
of length . In order to apply this transformation, must exist and so none of the may be zero. After the standard form system has been solved, usegsl_multifit_linear_genform1()
to recover the original solution vector. It is allowed to haveX
=Xs
andy
=ys
for an inplace transform. In order to perform a weighted regularized fit with , the user may callgsl_multifit_linear_applyW()
to convert to standard form.

int gsl_multifit_linear_L_decomp(gsl_matrix *L, gsl_vector *tau)¶
This function factors the by regularization matrix
L
into a form needed for the later transformation to standard form.L
may have any number of rows . If the QR decomposition ofL
is computed and stored inL
on output. If , the QR decomposition of is computed and stored inL
on output. On output, the Householder scalars are stored in the vectortau
of size . These outputs will be used bygsl_multifit_linear_wstdform2()
to complete the transformation to standard form.

int gsl_multifit_linear_stdform2(const gsl_matrix *LQR, const gsl_vector *Ltau, const gsl_matrix *X, const gsl_vector *y, gsl_matrix *Xs, gsl_vector *ys, gsl_matrix *M, gsl_multifit_linear_workspace *work)¶

int gsl_multifit_linear_wstdform2(const gsl_matrix *LQR, const gsl_vector *Ltau, const gsl_matrix *X, const gsl_vector *w, const gsl_vector *y, gsl_matrix *Xs, gsl_vector *ys, gsl_matrix *M, gsl_multifit_linear_workspace *work)¶
These functions convert the least squares system (
X
,y
,W
, ) to standard form (, ) which are stored inXs
andys
respectively. The by regularization matrixL
is specified by the inputsLQR
andLtau
, which are outputs fromgsl_multifit_linear_L_decomp()
. The dimensions of the standard form parameters (, ) depend on whether is larger or less than . For ,Xs
is by,ys
is by1, andM
is not used. For ,Xs
is by,ys
is by1, andM
is additional by workspace, which is required to recover the original solution vector after the system has been solved (seegsl_multifit_linear_genform2()
). Optional data weights may be supplied in the vectorw
of length , where .

int gsl_multifit_linear_solve(const double lambda, const gsl_matrix *Xs, const gsl_vector *ys, gsl_vector *cs, double *rnorm, double *snorm, gsl_multifit_linear_workspace *work)¶
This function computes the regularized bestfit parameters which minimize the cost function which is in standard form. The least squares system must therefore be converted to standard form prior to calling this function. The observation vector is provided in
ys
and the matrix of predictor variables inXs
. The solution vector is returned incs
, which has length min(). The SVD ofXs
must be computed prior to calling this function, usinggsl_multifit_linear_svd()
. The regularization parameter is provided inlambda
. The residual norm is returned inrnorm
. The solution norm is returned insnorm
.

int gsl_multifit_linear_genform1(const gsl_vector *L, const gsl_vector *cs, gsl_vector *c, gsl_multifit_linear_workspace *work)¶
After a regularized system has been solved with , this function backtransforms the standard form solution vector
cs
to recover the solution vector of the original problemc
. The diagonal matrix elements are provided in the vectorL
. It is allowed to havec
=cs
for an inplace transform.

int gsl_multifit_linear_genform2(const gsl_matrix *LQR, const gsl_vector *Ltau, const gsl_matrix *X, const gsl_vector *y, const gsl_vector *cs, const gsl_matrix *M, gsl_vector *c, gsl_multifit_linear_workspace *work)¶

int gsl_multifit_linear_wgenform2(const gsl_matrix *LQR, const gsl_vector *Ltau, const gsl_matrix *X, const gsl_vector *w, const gsl_vector *y, const gsl_vector *cs, const gsl_matrix *M, gsl_vector *c, gsl_multifit_linear_workspace *work)¶
After a regularized system has been solved with a general rectangular matrix , specified by (
LQR
,Ltau
), this function backtransforms the standard form solutioncs
to recover the solution vector of the original problem, which is stored inc
, of length . The original least squares matrix and observation vector are provided inX
andy
respectively.M
is the matrix computed bygsl_multifit_linear_stdform2()
. For weighted fits, the weight vectorw
must also be supplied.

int gsl_multifit_linear_applyW(const gsl_matrix *X, const gsl_vector *w, const gsl_vector *y, gsl_matrix *WX, gsl_vector *Wy)¶
For weighted least squares systems with , this function may be used to convert the system to standard form by applying the weight matrix to the least squares matrix
X
and observation vectory
. On output,WX
is equal to andWy
is equal to . It is allowed forWX
=X
andWy
=y
for an inplace transform.

int gsl_multifit_linear_lcurve(const gsl_vector *y, gsl_vector *reg_param, gsl_vector *rho, gsl_vector *eta, gsl_multifit_linear_workspace *work)¶
This function computes the Lcurve for a least squares system using the right hand side vector
y
and the SVD decomposition of the least squares matrixX
, which must be provided togsl_multifit_linear_svd()
prior to calling this function. The output vectorsreg_param
,rho
, andeta
must all be the same size, and will contain the regularization parameters , residual norms , and solution norms which compose the Lcurve, where is the regularized solution vector corresponding to . The user may determine the number of points on the Lcurve by adjusting the size of these input arrays. The regularization parameters are estimated from the singular values ofX
, and chosen to represent the most relevant portion of the Lcurve.

int gsl_multifit_linear_lcurvature(const gsl_vector *y, const gsl_vector *reg_param, const gsl_vector *rho, const gsl_vector *eta, gsl_vector *kappa, gsl_multifit_linear_workspace *work)¶
This function computes the curvature of the Lcurve as a function of the regularization parameter , using the right hand side vector
y
, the vector of regularization parameters,reg_param
, vector of residual norms,rho
, and vector of solution norms,eta
. The arraysreg_param
,rho
, andeta
can be computed bygsl_multifit_linear_lcurve()
. The curvature is defined aswhere and . The curvature values are stored in
kappa
on output.

int gsl_multifit_linear_lcorner(const gsl_vector *rho, const gsl_vector *eta, size_t *idx)¶
This function attempts to locate the corner of the Lcurve defined by the
rho
andeta
input arrays respectively. The corner is defined as the point of maximum curvature of the Lcurve in loglog scale. Therho
andeta
arrays can be outputs ofgsl_multifit_linear_lcurve()
. The algorithm used simply fits a circle to 3 consecutive points on the Lcurve and uses the circle’s radius to determine the curvature at the middle point. Therefore, the input array sizes must be . With more points provided for the Lcurve, a better estimate of the curvature can be obtained. The array index corresponding to maximum curvature (ie: the corner) is returned inidx
. If the input arrays contain colinear points, this function could fail and returnGSL_EINVAL
.

int gsl_multifit_linear_lcorner2(const gsl_vector *reg_param, const gsl_vector *eta, size_t *idx)¶
This function attempts to locate the corner of an alternate Lcurve studied by Rezghi and Hosseini, 2009. This alternate Lcurve can provide better estimates of the regularization parameter for smooth solution vectors. The regularization parameters and solution norms are provided in the
reg_param
andeta
input arrays respectively. The corner is defined as the point of maximum curvature of this alternate Lcurve in linear scale. Thereg_param
andeta
arrays can be outputs ofgsl_multifit_linear_lcurve()
. The algorithm used simply fits a circle to 3 consecutive points on the Lcurve and uses the circle’s radius to determine the curvature at the middle point. Therefore, the input array sizes must be . With more points provided for the Lcurve, a better estimate of the curvature can be obtained. The array index corresponding to maximum curvature (ie: the corner) is returned inidx
. If the input arrays contain colinear points, this function could fail and returnGSL_EINVAL
.

int gsl_multifit_linear_gcv_init(const gsl_vector *y, gsl_vector *reg_param, gsl_vector *UTy, double *delta0, gsl_multifit_linear_workspace *work)¶
This function performs some initialization in preparation for computing the GCV curve and its minimum. The right hand side vector is provided in
y
. On output,reg_param
is set to a vector of regularization parameters in decreasing order and may be of any size. The vectorUTy
of size is set to . The parameterdelta0
is needed for subsequent steps of the GCV calculation.

int gsl_multifit_linear_gcv_curve(const gsl_vector *reg_param, const gsl_vector *UTy, const double delta0, gsl_vector *G, gsl_multifit_linear_workspace *work)¶
This funtion calculates the GCV curve and stores it in
G
on output, which must be the same size asreg_param
. The inputsreg_param
,UTy
anddelta0
are computed ingsl_multifit_linear_gcv_init()
.

int gsl_multifit_linear_gcv_min(const gsl_vector *reg_param, const gsl_vector *UTy, const gsl_vector *G, const double delta0, double *lambda, gsl_multifit_linear_workspace *work)¶
This function computes the value of the regularization parameter which minimizes the GCV curve and stores it in
lambda
. The inputG
is calculated bygsl_multifit_linear_gcv_curve()
and the inputsreg_param
,UTy
anddelta0
are computed bygsl_multifit_linear_gcv_init()
.

double gsl_multifit_linear_gcv_calc(const double lambda, const gsl_vector *UTy, const double delta0, gsl_multifit_linear_workspace *work)¶
This function returns the value of the GCV curve corresponding to the input
lambda
.

int gsl_multifit_linear_gcv(const gsl_vector *y, gsl_vector *reg_param, gsl_vector *G, double *lambda, double *G_lambda, gsl_multifit_linear_workspace *work)¶
This function combines the steps
gcv_init
,gcv_curve
, andgcv_min
defined above into a single function. The inputy
is the right hand side vector. On output,reg_param
andG
, which must be the same size, are set to vectors of and values respectively. The outputlambda
is set to the optimal value of which minimizes the GCV curve. The minimum value of the GCV curve is returned inG_lambda
.

int gsl_multifit_linear_Lk(const size_t p, const size_t k, gsl_matrix *L)¶
This function computes the discrete approximation to the derivative operator of order
k
on a regular grid ofp
points and stores it inL
. The dimensions ofL
are by.

int gsl_multifit_linear_Lsobolev(const size_t p, const size_t kmax, const gsl_vector *alpha, gsl_matrix *L, gsl_multifit_linear_workspace *work)¶
This function computes the regularization matrix
L
corresponding to the weighted Sobolov norm where approximates the derivative operator of order . This regularization norm can be useful in applications where it is necessary to smooth several derivatives of the solution.p
is the number of model parameters,kmax
is the highest derivative to include in the summation above, andalpha
is the vector of weights of sizekmax
+ 1, wherealpha[k]
= is the weight assigned to the derivative of order . The output matrixL
is sizep
byp
and upper triangular.

double gsl_multifit_linear_rcond(const gsl_multifit_linear_workspace *work)¶
This function returns the reciprocal condition number of the least squares matrix , defined as the ratio of the smallest and largest singular values, rcond = . The routine
gsl_multifit_linear_svd()
must first be called to compute the SVD of .
Robust linear regression¶
Ordinary least squares (OLS) models are often heavily influenced by the presence of outliers. Outliers are data points which do not follow the general trend of the other observations, although there is strictly no precise definition of an outlier. Robust linear regression refers to regression algorithms which are robust to outliers. The most common type of robust regression is Mestimation. The general Mestimator minimizes the objective function
where is the residual of the ith data point, and is a function which should have the following properties:
for
The special case of ordinary least squares is given by . Letting be the derivative of , differentiating the objective function with respect to the coefficients and setting the partial derivatives to zero produces the system of equations
where is a vector containing row of the design matrix . Next, we define a weight function , and let :
This system of equations is equivalent to solving a weighted ordinary least squares problem, minimizing . The weights however, depend on the residuals , which depend on the coefficients , which depend on the weights. Therefore, an iterative solution is used, called Iteratively Reweighted Least Squares (IRLS).
Compute initial estimates of the coefficients using ordinary least squares
For iteration , form the residuals , where is a tuning constant depending on the choice of , and are the statistical leverages (diagonal elements of the matrix ). Including and in the residual calculation has been shown to improve the convergence of the method. The residual standard deviation is approximated as , where MAD is the MedianAbsoluteDeviation of the largest residuals from the previous iteration.
Compute new weights .
Compute new coefficients by solving the weighted least squares problem with weights .
Steps 2 through 4 are iterated until the coefficients converge or until some maximum iteration limit is reached. Coefficients are tested for convergence using the critera:
for all where is a small tolerance factor.
The key to this method lies in selecting the function to assign smaller weights to large residuals, and larger weights to smaller residuals. As the iteration proceeds, outliers are assigned smaller and smaller weights, eventually having very little or no effect on the fitted model.

type gsl_multifit_robust_workspace¶
This workspace is used for robust least squares fitting.

gsl_multifit_robust_workspace *gsl_multifit_robust_alloc(const gsl_multifit_robust_type *T, const size_t n, const size_t p)¶
This function allocates a workspace for fitting a model to
n
observations usingp
parameters. The size of the workspace is . The typeT
specifies the function and can be selected from the following choices.
type gsl_multifit_robust_type¶

gsl_multifit_robust_type *gsl_multifit_robust_default¶
This specifies the
gsl_multifit_robust_bisquare
type (see below) and is a good general purpose choice for robust regression.

gsl_multifit_robust_type *gsl_multifit_robust_bisquare¶
This is Tukey’s biweight (bisquare) function and is a good general purpose choice for robust regression. The weight function is given by
and the default tuning constant is .

gsl_multifit_robust_type *gsl_multifit_robust_cauchy¶
This is Cauchy’s function, also known as the Lorentzian function. This function does not guarantee a unique solution, meaning different choices of the coefficient vector
c
could minimize the objective function. Therefore this option should be used with care. The weight function is given byand the default tuning constant is .

gsl_multifit_robust_type *gsl_multifit_robust_fair¶
This is the fair function, which guarantees a unique solution and has continuous derivatives to three orders. The weight function is given by
and the default tuning constant is .

gsl_multifit_robust_type *gsl_multifit_robust_huber¶
This specifies Huber’s function, which is a parabola in the vicinity of zero and increases linearly for a given threshold . This function is also considered an excellent general purpose robust estimator, however, occasional difficulties can be encountered due to the discontinuous first derivative of the function. The weight function is given by
and the default tuning constant is .

gsl_multifit_robust_type *gsl_multifit_robust_ols¶
This specifies the ordinary least squares solution, which can be useful for quickly checking the difference between the various robust and OLS solutions. The weight function is given by
and the default tuning constant is .

gsl_multifit_robust_type *gsl_multifit_robust_welsch¶
This specifies the Welsch function which can perform well in cases where the residuals have an exponential distribution. The weight function is given by
and the default tuning constant is .

gsl_multifit_robust_type *gsl_multifit_robust_default¶

type gsl_multifit_robust_type¶

void gsl_multifit_robust_free(gsl_multifit_robust_workspace *w)¶
This function frees the memory associated with the workspace
w
.

const char *gsl_multifit_robust_name(const gsl_multifit_robust_workspace *w)¶
This function returns the name of the robust type
T
specified togsl_multifit_robust_alloc()
.

int gsl_multifit_robust_tune(const double tune, gsl_multifit_robust_workspace *w)¶
This function sets the tuning constant used to adjust the residuals at each iteration to
tune
. Decreasing the tuning constant increases the downweight assigned to large residuals, while increasing the tuning constant decreases the downweight assigned to large residuals.

int gsl_multifit_robust_maxiter(const size_t maxiter, gsl_multifit_robust_workspace *w)¶
This function sets the maximum number of iterations in the iteratively reweighted least squares algorithm to
maxiter
. By default, this value is set to 100 bygsl_multifit_robust_alloc()
.

int gsl_multifit_robust_weights(const gsl_vector *r, gsl_vector *wts, gsl_multifit_robust_workspace *w)¶
This function assigns weights to the vector
wts
using the residual vectorr
and previously specified weighting function. The output weights are given by , where the weighting functions are detailed ingsl_multifit_robust_alloc()
. is an estimate of the residual standard deviation based on the MedianAbsoluteDeviation and is the tuning constant. This function is useful if the user wishes to implement their own robust regression rather than using the suppliedgsl_multifit_robust()
routine below.

int gsl_multifit_robust(const gsl_matrix *X, const gsl_vector *y, gsl_vector *c, gsl_matrix *cov, gsl_multifit_robust_workspace *w)¶
This function computes the bestfit parameters
c
of the model for the observationsy
and the matrix of predictor variablesX
, attemping to reduce the influence of outliers using the algorithm outlined above. The by variancecovariance matrix of the model parameterscov
is estimated as , where is an approximation of the residual standard deviation using the theory of robust regression. Special care must be taken when estimating and other statistics such as , and so these are computed internally and are available by calling the functiongsl_multifit_robust_statistics()
.If the coefficients do not converge within the maximum iteration limit, the function returns
GSL_EMAXITER
. In this case, the current estimates of the coefficients and covariance matrix are returned inc
andcov
and the internal fit statistics are computed with these estimates.

int gsl_multifit_robust_est(const gsl_vector *x, const gsl_vector *c, const gsl_matrix *cov, double *y, double *y_err)¶
This function uses the bestfit robust regression coefficients
c
and their covariance matrixcov
to compute the fitted function valuey
and its standard deviationy_err
for the model at the pointx
.

int gsl_multifit_robust_residuals(const gsl_matrix *X, const gsl_vector *y, const gsl_vector *c, gsl_vector *r, gsl_multifit_robust_workspace *w)¶
This function computes the vector of studentized residuals for the observations
y
, coefficientsc
and matrix of predictor variablesX
. The routinegsl_multifit_robust()
must first be called to compute the statisical leverages of the matrixX
and residual standard deviation estimate .

gsl_multifit_robust_stats gsl_multifit_robust_statistics(const gsl_multifit_robust_workspace *w)¶
This function returns a structure containing relevant statistics from a robust regression. The function
gsl_multifit_robust()
must be called first to perform the regression and calculate these statistics. The returnedgsl_multifit_robust_stats
structure contains the following fields.
type gsl_multifit_robust_stats¶
double sigma_ols
This contains the standard deviation of the residuals as computed from ordinary least squares (OLS).
double sigma_mad
This contains an estimate of the standard deviation of the final residuals using the MedianAbsoluteDeviation statistic
double sigma_rob
This contains an estimate of the standard deviation of the final residuals from the theory of robust regression (see Street et al, 1988).
double sigma
This contains an estimate of the standard deviation of the final residuals by attemping to reconcile
sigma_rob
andsigma_ols
in a reasonable way.double Rsq
This contains the coefficient of determination statistic using the estimate
sigma
.double adj_Rsq
This contains the adjusted coefficient of determination statistic using the estimate
sigma
.double rmse
This contains the root mean squared error of the final residuals
double sse
This contains the residual sum of squares taking into account the robust covariance matrix.
size_t dof
This contains the number of degrees of freedom
size_t numit
Upon successful convergence, this contains the number of iterations performed
gsl_vector * weights
This contains the final weight vector of length
n
gsl_vector * r
This contains the final residual vector of length
n
,

type gsl_multifit_robust_stats¶
Large dense linear systems¶
This module is concerned with solving large dense least squares systems where the by matrix has (ie: many more rows than columns). This type of matrix is called a “tall skinny” matrix, and for some applications, it may not be possible to fit the entire matrix in memory at once to use the standard SVD approach. Therefore, the algorithms in this module are designed to allow the user to construct smaller blocks of the matrix and accumulate those blocks into the larger system one at a time. The algorithms in this module never need to store the entire matrix in memory. The large linear least squares routines support data weights and Tikhonov regularization, and are designed to minimize the residual
where is the by observation vector, is the by design matrix, is the by solution vector, is the data weighting matrix, is an by regularization matrix, is a regularization parameter, and . In the discussion which follows, we will assume that the system has been converted into Tikhonov standard form,
and we will drop the tilde characters from the various parameters. For a discussion of the transformation to standard form, see Regularized regression.
The basic idea is to partition the matrix and observation vector as
into blocks, where each block () may have
any number of rows, but each has columns.
The sections below describe the methods available for solving
this partitioned system. The functions are declared in
the header file gsl_multilarge.h
.
Normal Equations Approach¶
The normal equations approach to the large linear least squares problem described above is popular due to its speed and simplicity. Since the normal equations solution to the problem is given by
only the by matrix and by1 vector need to be stored. Using the partition scheme described above, these are given by
Since the matrix is symmetric, only half of it needs to be calculated. Once all of the blocks have been accumulated into the final and , the system can be solved with a Cholesky factorization of the matrix. The matrix is first transformed via a diagonal scaling transformation to attempt to reduce its condition number as much as possible to recover a more accurate solution vector. The normal equations approach is the fastest method for solving the large least squares problem, and is accurate for wellconditioned matrices . However, for illconditioned matrices, as is often the case for large systems, this method can suffer from numerical instabilities (see Trefethen and Bau, 1997). The number of operations for this method is .
Tall Skinny QR (TSQR) Approach¶
An algorithm which has better numerical stability for illconditioned problems is known as the Tall Skinny QR (TSQR) method. This method is based on computing the thin QR decomposition of the least squares matrix , where is an by matrix with orthogonal columns, and is a by upper triangular matrix. Once these factors are calculated, the residual becomes
which can be written as the matrix equation
The matrix on the left hand side is now a much smaller by matrix which can be solved with a standard SVD approach. The matrix is just as large as the original matrix , however it does not need to be explicitly constructed. The TSQR algorithm computes only the by matrix and the by1 vector , and updates these quantities as new blocks are added to the system. Each time a new block of rows () is added, the algorithm performs a QR decomposition of the matrix
where is the upper triangular factor for the matrix
This QR decomposition is done efficiently taking into account the sparse structure of . See Demmel et al, 2008 for more details on how this is accomplished. The number of operations for this method is .
Large Dense Linear Systems Solution Steps¶
The typical steps required to solve large regularized linear least squares problems are as follows:
Choose the regularization matrix .
Construct a block of rows of the least squares matrix, right hand side vector, and weight vector (, , ).
Transform the block to standard form (, ). This step can be skipped if and .
Accumulate the standard form block (, ) into the system.
Repeat steps 24 until the entire matrix and right hand side vector have been accumulated.
Determine an appropriate regularization parameter (using for example Lcurve analysis).
Solve the standard form system using the chosen .
Backtransform the standard form solution to recover the original solution vector .
Large Dense Linear Least Squares Routines¶

type gsl_multilarge_linear_workspace¶
This workspace contains parameters for solving large linear least squares problems.

gsl_multilarge_linear_workspace *gsl_multilarge_linear_alloc(const gsl_multilarge_linear_type *T, const size_t p)¶
This function allocates a workspace for solving large linear least squares systems. The least squares matrix has
p
columns, but may have any number of rows.
type gsl_multilarge_linear_type¶
The parameter
T
specifies the method to be used for solving the large least squares system and may be selected from the following choices
gsl_multilarge_linear_type *gsl_multilarge_linear_normal¶
This specifies the normal equations approach for solving the least squares system. This method is suitable in cases where performance is critical and it is known that the least squares matrix is well conditioned. The size of this workspace is .

gsl_multilarge_linear_type *gsl_multilarge_linear_tsqr¶
This specifies the sequential Tall Skinny QR (TSQR) approach for solving the least squares system. This method is a good general purpose choice for large systems, but requires about twice as many operations as the normal equations method for . The size of this workspace is .

gsl_multilarge_linear_type *gsl_multilarge_linear_normal¶

type gsl_multilarge_linear_type¶

void gsl_multilarge_linear_free(gsl_multilarge_linear_workspace *w)¶
This function frees the memory associated with the workspace
w
.

const char *gsl_multilarge_linear_name(gsl_multilarge_linear_workspace *w)¶
This function returns a string pointer to the name of the multilarge solver.

int gsl_multilarge_linear_reset(gsl_multilarge_linear_workspace *w)¶
This function resets the workspace
w
so it can begin to accumulate a new least squares system.

int gsl_multilarge_linear_stdform1(const gsl_vector *L, const gsl_matrix *X, const gsl_vector *y, gsl_matrix *Xs, gsl_vector *ys, gsl_multilarge_linear_workspace *work)¶

int gsl_multilarge_linear_wstdform1(const gsl_vector *L, const gsl_matrix *X, const gsl_vector *w, const gsl_vector *y, gsl_matrix *Xs, gsl_vector *ys, gsl_multilarge_linear_workspace *work)¶
These functions define a regularization matrix . The diagonal matrix element is provided by the th element of the input vector
L
. The block (X
,y
) is converted to standard form and the parameters (, ) are stored inXs
andys
on output.Xs
andys
have the same dimensions asX
andy
. Optional data weights may be supplied in the vectorw
. In order to apply this transformation, must exist and so none of the may be zero. After the standard form system has been solved, usegsl_multilarge_linear_genform1()
to recover the original solution vector. It is allowed to haveX
=Xs
andy
=ys
for an inplace transform.

int gsl_multilarge_linear_L_decomp(gsl_matrix *L, gsl_vector *tau)¶
This function calculates the QR decomposition of the by regularization matrix
L
.L
must have . On output, the Householder scalars are stored in the vectortau
of size . These outputs will be used bygsl_multilarge_linear_wstdform2()
to complete the transformation to standard form.

int gsl_multilarge_linear_stdform2(const gsl_matrix *LQR, const gsl_vector *Ltau, const gsl_matrix *X, const gsl_vector *y, gsl_matrix *Xs, gsl_vector *ys, gsl_multilarge_linear_workspace *work)¶

int gsl_multilarge_linear_wstdform2(const gsl_matrix *LQR, const gsl_vector *Ltau, const gsl_matrix *X, const gsl_vector *w, const gsl_vector *y, gsl_matrix *Xs, gsl_vector *ys, gsl_multilarge_linear_workspace *work)¶
These functions convert a block of rows (
X
,y
,w
) to standard form (, ) which are stored inXs
andys
respectively.X
,y
, andw
must all have the same number of rows. The by regularization matrixL
is specified by the inputsLQR
andLtau
, which are outputs fromgsl_multilarge_linear_L_decomp()
.Xs
andys
have the same dimensions asX
andy
. After the standard form system has been solved, usegsl_multilarge_linear_genform2()
to recover the original solution vector. Optional data weights may be supplied in the vectorw
, where .

int gsl_multilarge_linear_accumulate(gsl_matrix *X, gsl_vector *y, gsl_multilarge_linear_workspace *w)¶
This function accumulates the standard form block () into the current least squares system.
X
andy
have the same number of rows, which can be arbitrary.X
must have columns. For the TSQR method,X
andy
are destroyed on output. For the normal equations method, they are both unchanged.

int gsl_multilarge_linear_solve(const double lambda, gsl_vector *c, double *rnorm, double *snorm, gsl_multilarge_linear_workspace *w)¶
After all blocks () have been accumulated into the large least squares system, this function will compute the solution vector which is stored in
c
on output. The regularization parameter is provided inlambda
. On output,rnorm
contains the residual norm andsnorm
contains the solution norm .

int gsl_multilarge_linear_genform1(const gsl_vector *L, const gsl_vector *cs, gsl_vector *c, gsl_multilarge_linear_workspace *work)¶
After a regularized system has been solved with , this function backtransforms the standard form solution vector
cs
to recover the solution vector of the original problemc
. The diagonal matrix elements are provided in the vectorL
. It is allowed to havec
=cs
for an inplace transform.

int gsl_multilarge_linear_genform2(const gsl_matrix *LQR, const gsl_vector *Ltau, const gsl_vector *cs, gsl_vector *c, gsl_multilarge_linear_workspace *work)¶
After a regularized system has been solved with a regularization matrix , specified by (
LQR
,Ltau
), this function backtransforms the standard form solutioncs
to recover the solution vector of the original problem, which is stored inc
, of length .

int gsl_multilarge_linear_lcurve(gsl_vector *reg_param, gsl_vector *rho, gsl_vector *eta, gsl_multilarge_linear_workspace *work)¶
This function computes the Lcurve for a large least squares system after it has been fully accumulated into the workspace
work
. The output vectorsreg_param
,rho
, andeta
must all be the same size, and will contain the regularization parameters , residual norms , and solution norms which compose the Lcurve, where is the regularized solution vector corresponding to . The user may determine the number of points on the Lcurve by adjusting the size of these input arrays. For the TSQR method, the regularization parameters are estimated from the singular values of the triangular factor. For the normal equations method, they are estimated from the eigenvalues of the matrix.

const gsl_matrix *gsl_multilarge_linear_matrix_ptr(const gsl_multilarge_linear_workspace *work)¶
For the normal equations method, this function returns a pointer to the matrix. For the TSQR method, this function returns a pointer to the upper triangular matrix.

const gsl_vector *gsl_multilarge_linear_rhs_ptr(const gsl_multilarge_linear_workspace *work)¶
For the normal equations method, this function returns a pointer to the right hand side vector. For the TSQR method, this function returns a pointer to the right hand side vector.

int gsl_multilarge_linear_rcond(double *rcond, gsl_multilarge_linear_workspace *work)¶
This function computes the reciprocal condition number, stored in
rcond
, of the least squares matrix after it has been accumulated into the workspacework
. For the TSQR algorithm, this is accomplished by calculating the SVD of the factor, which has the same singular values as the matrix . For the normal equations method, this is done by computing the eigenvalues of , which could be inaccurate for illconditioned matrices .
Troubleshooting¶
When using models based on polynomials, care should be taken when constructing the design matrix . If the values are large, then the matrix could be illconditioned since its columns are powers of , leading to unstable leastsquares solutions. In this case it can often help to center and scale the values using the mean and standard deviation:
and then construct the matrix using the transformed values .
Examples¶
The example programs in this section demonstrate the various linear regression methods.
Simple Linear Regression Example¶
The following program computes a least squares straightline fit to a simple dataset, and outputs the bestfit line and its associated one standarddeviation error bars.
#include <stdio.h>
#include <gsl/gsl_fit.h>
int
main (void)
{
int i, n = 4;
double x[4] = { 1970, 1980, 1990, 2000 };
double y[4] = { 12, 11, 14, 13 };
double w[4] = { 0.1, 0.2, 0.3, 0.4 };
double c0, c1, cov00, cov01, cov11, chisq;
gsl_fit_wlinear (x, 1, w, 1, y, 1, n,
&c0, &c1, &cov00, &cov01, &cov11,
&chisq);
printf ("# best fit: Y = %g + %g X\n", c0, c1);
printf ("# covariance matrix:\n");
printf ("# [ %g, %g\n# %g, %g]\n",
cov00, cov01, cov01, cov11);
printf ("# chisq = %g\n", chisq);
for (i = 0; i < n; i++)
printf ("data: %g %g %g\n",
x[i], y[i], 1/sqrt(w[i]));
printf ("\n");
for (i = 30; i < 130; i++)
{
double xf = x[0] + (i/100.0) * (x[n1]  x[0]);
double yf, yf_err;
gsl_fit_linear_est (xf,
c0, c1,
cov00, cov01, cov11,
&yf, &yf_err);
printf ("fit: %g %g\n", xf, yf);
printf ("hi : %g %g\n", xf, yf + yf_err);
printf ("lo : %g %g\n", xf, yf  yf_err);
}
return 0;
}
The following commands extract the data from the output of the program and display it using the GNU plotutils “graph” utility:
$ ./demo > tmp
$ more tmp
# best fit: Y = 106.6 + 0.06 X
# covariance matrix:
# [ 39602, 19.9
# 19.9, 0.01]
# chisq = 0.8
$ for n in data fit hi lo ;
do
grep "^$n" tmp  cut d: f2 > $n ;
done
$ graph T X X x Y y y 0 20 m 0 S 2 Ie data
S 0 I a m 1 fit m 2 hi m 2 lo
The result is shown in Fig. 29.
Multiparameter Linear Regression Example¶
The following program performs a quadratic fit
to a weighted dataset using the generalised linear fitting function
gsl_multifit_wlinear()
. The model matrix for a quadratic
fit is given by,
where the column of ones corresponds to the constant term . The two remaining columns corresponds to the terms and .
The program reads n
lines of data in the format (x
, y
,
err
) where err
is the error (standard deviation) in the
value y
.
#include <stdio.h>
#include <gsl/gsl_multifit.h>
int
main (int argc, char **argv)
{
int i, n;
double xi, yi, ei, chisq;
gsl_matrix *X, *cov;
gsl_vector *y, *w, *c;
if (argc != 2)
{
fprintf (stderr,"usage: fit n < data\n");
exit (1);
}
n = atoi (argv[1]);
X = gsl_matrix_alloc (n, 3);
y = gsl_vector_alloc (n);
w = gsl_vector_alloc (n);
c = gsl_vector_alloc (3);
cov = gsl_matrix_alloc (3, 3);
for (i = 0; i < n; i++)
{
int count = fscanf (stdin, "%lg %lg %lg",
&xi, &yi, &ei);
if (count != 3)
{
fprintf (stderr, "error reading file\n");
exit (1);
}
printf ("%g %g +/ %g\n", xi, yi, ei);
gsl_matrix_set (X, i, 0, 1.0);
gsl_matrix_set (X, i, 1, xi);
gsl_matrix_set (X, i, 2, xi*xi);
gsl_vector_set (y, i, yi);
gsl_vector_set (w, i, 1.0/(ei*ei));
}
{
gsl_multifit_linear_workspace * work
= gsl_multifit_linear_alloc (n, 3);
gsl_multifit_wlinear (X, w, y, c, cov,
&chisq, work);
gsl_multifit_linear_free (work);
}
#define C(i) (gsl_vector_get(c,(i)))
#define COV(i,j) (gsl_matrix_get(cov,(i),(j)))
{
printf ("# best fit: Y = %g + %g X + %g X^2\n",
C(0), C(1), C(2));
printf ("# covariance matrix:\n");
printf ("[ %+.5e, %+.5e, %+.5e \n",
COV(0,0), COV(0,1), COV(0,2));
printf (" %+.5e, %+.5e, %+.5e \n",
COV(1,0), COV(1,1), COV(1,2));
printf (" %+.5e, %+.5e, %+.5e ]\n",
COV(2,0), COV(2,1), COV(2,2));
printf ("# chisq = %g\n", chisq);
}
gsl_matrix_free (X);
gsl_vector_free (y);
gsl_vector_free (w);
gsl_vector_free (c);
gsl_matrix_free (cov);
return 0;
}
A suitable set of data for fitting can be generated using the following program. It outputs a set of points with gaussian errors from the curve in the region .
#include <stdio.h>
#include <math.h>
#include <gsl/gsl_randist.h>
int
main (void)
{
double x;
const gsl_rng_type * T;
gsl_rng * r;
gsl_rng_env_setup ();
T = gsl_rng_default;
r = gsl_rng_alloc (T);
for (x = 0.1; x < 2; x+= 0.1)
{
double y0 = exp (x);
double sigma = 0.1 * y0;
double dy = gsl_ran_gaussian (r, sigma);
printf ("%g %g %g\n", x, y0 + dy, sigma);
}
gsl_rng_free(r);
return 0;
}
The data can be prepared by running the resulting executable program:
$ GSL_RNG_TYPE=mt19937_1999 ./generate > exp.dat
$ more exp.dat
0.1 0.97935 0.110517
0.2 1.3359 0.12214
0.3 1.52573 0.134986
0.4 1.60318 0.149182
0.5 1.81731 0.164872
0.6 1.92475 0.182212
....
To fit the data use the previous program, with the number of data points given as the first argument. In this case there are 19 data points:
$ ./fit 19 < exp.dat
0.1 0.97935 +/ 0.110517
0.2 1.3359 +/ 0.12214
...
# best fit: Y = 1.02318 + 0.956201 X + 0.876796 X^2
# covariance matrix:
[ +1.25612e02, 3.64387e02, +1.94389e02
3.64387e02, +1.42339e01, 8.48761e02
+1.94389e02, 8.48761e02, +5.60243e02 ]
# chisq = 23.0987
The parameters of the quadratic fit match the coefficients of the expansion of , taking into account the errors on the parameters and the difference between the exponential and quadratic functions for the larger values of . The errors on the parameters are given by the squareroot of the corresponding diagonal elements of the covariance matrix. The chisquared per degree of freedom is 1.4, indicating a reasonable fit to the data.
Fig. 30 shows the resulting fit.
Regularized Linear Regression Example 1¶
The next program demonstrates the difference between ordinary and regularized least squares when the design matrix is nearsingular. In this program, we generate two random normally distributed variables and , with so that and are nearly colinear. We then set a third dependent variable and solve for the coefficients of the model . Since , the design matrix is nearly singular, leading to unstable ordinary least squares solutions.
Here is the program output:
matrix condition number = 1.025113e+04
=== Unregularized fit ===
best fit: y = 43.6588 u + 45.6636 v
residual norm = 31.6248
solution norm = 63.1764
chisq/dof = 1.00213
=== Regularized fit (Lcurve) ===
optimal lambda: 4.51103
best fit: y = 1.00113 u + 1.0032 v
residual norm = 31.6547
solution norm = 1.41728
chisq/dof = 1.04499
=== Regularized fit (GCV) ===
optimal lambda: 0.0232029
best fit: y = 19.8367 u + 21.8417 v
residual norm = 31.6332
solution norm = 29.5051
chisq/dof = 1.00314
We see that the ordinary least squares solution is completely wrong, while the Lcurve regularized method with the optimal finds the correct solution . The GCV regularized method finds a regularization parameter which is too small to give an accurate solution, although it performs better than OLS. The Lcurve and its computed corner, as well as the GCV curve and its minimum are plotted in Fig. 31.
The program is given below.
#include <gsl/gsl_math.h>
#include <gsl/gsl_vector.h>
#include <gsl/gsl_matrix.h>
#include <gsl/gsl_rng.h>
#include <gsl/gsl_randist.h>
#include <gsl/gsl_multifit.h>
int
main()
{
const size_t n = 1000; /* number of observations */
const size_t p = 2; /* number of model parameters */
size_t i;
gsl_rng *r = gsl_rng_alloc(gsl_rng_default);
gsl_matrix *X = gsl_matrix_alloc(n, p);
gsl_vector *y = gsl_vector_alloc(n);
for (i = 0; i < n; ++i)
{
/* generate first random variable u */
double ui = 5.0 * gsl_ran_gaussian(r, 1.0);
/* set v = u + noise */
double vi = ui + gsl_ran_gaussian(r, 0.001);
/* set y = u + v + noise */
double yi = ui + vi + gsl_ran_gaussian(r, 1.0);
/* since u =~ v, the matrix X is illconditioned */
gsl_matrix_set(X, i, 0, ui);
gsl_matrix_set(X, i, 1, vi);
/* rhs vector */
gsl_vector_set(y, i, yi);
}
{
const size_t npoints = 200; /* number of points on Lcurve and GCV curve */
gsl_multifit_linear_workspace *w =
gsl_multifit_linear_alloc(n, p);
gsl_vector *c = gsl_vector_alloc(p); /* OLS solution */
gsl_vector *c_lcurve = gsl_vector_alloc(p); /* regularized solution (Lcurve) */
gsl_vector *c_gcv = gsl_vector_alloc(p); /* regularized solution (GCV) */
gsl_vector *reg_param = gsl_vector_alloc(npoints);
gsl_vector *rho = gsl_vector_alloc(npoints); /* residual norms */
gsl_vector *eta = gsl_vector_alloc(npoints); /* solution norms */
gsl_vector *G = gsl_vector_alloc(npoints); /* GCV function values */
double lambda_l; /* optimal regularization parameter (Lcurve) */
double lambda_gcv; /* optimal regularization parameter (GCV) */
double G_gcv; /* G(lambda_gcv) */
size_t reg_idx; /* index of optimal lambda */
double rcond; /* reciprocal condition number of X */
double chisq, rnorm, snorm;
/* compute SVD of X */
gsl_multifit_linear_svd(X, w);
rcond = gsl_multifit_linear_rcond(w);
fprintf(stderr, "matrix condition number = %e\n\n", 1.0 / rcond);
/* unregularized (standard) least squares fit, lambda = 0 */
gsl_multifit_linear_solve(0.0, X, y, c, &rnorm, &snorm, w);
chisq = pow(rnorm, 2.0);
fprintf(stderr, "=== Unregularized fit ===\n");
fprintf(stderr, "best fit: y = %g u + %g v\n",
gsl_vector_get(c, 0), gsl_vector_get(c, 1));
fprintf(stderr, "residual norm = %g\n", rnorm);
fprintf(stderr, "solution norm = %g\n", snorm);
fprintf(stderr, "chisq/dof = %g\n", chisq / (n  p));
/* calculate Lcurve and find its corner */
gsl_multifit_linear_lcurve(y, reg_param, rho, eta, w);
gsl_multifit_linear_lcorner(rho, eta, ®_idx);
/* store optimal regularization parameter */
lambda_l = gsl_vector_get(reg_param, reg_idx);
/* regularize with lambda_l */
gsl_multifit_linear_solve(lambda_l, X, y, c_lcurve, &rnorm, &snorm, w);
chisq = pow(rnorm, 2.0) + pow(lambda_l * snorm, 2.0);
fprintf(stderr, "\n=== Regularized fit (Lcurve) ===\n");
fprintf(stderr, "optimal lambda: %g\n", lambda_l);
fprintf(stderr, "best fit: y = %g u + %g v\n",
gsl_vector_get(c_lcurve, 0), gsl_vector_get(c_lcurve, 1));
fprintf(stderr, "residual norm = %g\n", rnorm);
fprintf(stderr, "solution norm = %g\n", snorm);
fprintf(stderr, "chisq/dof = %g\n", chisq / (n  p));
/* calculate GCV curve and find its minimum */
gsl_multifit_linear_gcv(y, reg_param, G, &lambda_gcv, &G_gcv, w);
/* regularize with lambda_gcv */
gsl_multifit_linear_solve(lambda_gcv, X, y, c_gcv, &rnorm, &snorm, w);
chisq = pow(rnorm, 2.0) + pow(lambda_gcv * snorm, 2.0);
fprintf(stderr, "\n=== Regularized fit (GCV) ===\n");
fprintf(stderr, "optimal lambda: %g\n", lambda_gcv);
fprintf(stderr, "best fit: y = %g u + %g v\n",
gsl_vector_get(c_gcv, 0), gsl_vector_get(c_gcv, 1));
fprintf(stderr, "residual norm = %g\n", rnorm);
fprintf(stderr, "solution norm = %g\n", snorm);
fprintf(stderr, "chisq/dof = %g\n", chisq / (n  p));
/* output Lcurve and GCV curve */
for (i = 0; i < npoints; ++i)
{
printf("%e %e %e %e\n",
gsl_vector_get(reg_param, i),
gsl_vector_get(rho, i),
gsl_vector_get(eta, i),
gsl_vector_get(G, i));
}
/* output Lcurve corner point */
printf("\n\n%f %f\n",
gsl_vector_get(rho, reg_idx),
gsl_vector_get(eta, reg_idx));
/* output GCV curve corner minimum */
printf("\n\n%e %e\n",
lambda_gcv,
G_gcv);
gsl_multifit_linear_free(w);
gsl_vector_free(c);
gsl_vector_free(c_lcurve);
gsl_vector_free(reg_param);
gsl_vector_free(rho);
gsl_vector_free(eta);
gsl_vector_free(G);
}
gsl_rng_free(r);
gsl_matrix_free(X);
gsl_vector_free(y);
return 0;
}
Regularized Linear Regression Example 2¶
The following example program minimizes the cost function
where is the by Hilbert matrix whose entries are given by
and the right hand side vector is given by . Solutions are computed for (unregularized) as well as for optimal parameters chosen by analyzing the Lcurve and GCV curve.
Here is the program output:
matrix condition number = 3.565872e+09
=== Unregularized fit ===
residual norm = 2.15376
solution norm = 2.92217e+09
chisq/dof = 2.31934
=== Regularized fit (Lcurve) ===
optimal lambda: 7.11407e07
residual norm = 2.60386
solution norm = 424507
chisq/dof = 3.43565
=== Regularized fit (GCV) ===
optimal lambda: 1.72278
residual norm = 3.1375
solution norm = 0.139357
chisq/dof = 4.95076
Here we see the unregularized solution results in a large solution norm due to the illconditioned matrix. The Lcurve solution finds a small value of which still results in a badly conditioned system and a large solution norm. The GCV method finds a parameter which results in a wellconditioned system and small solution norm.
The Lcurve and its computed corner, as well as the GCV curve and its minimum are plotted in Fig. 32.
The program is given below.
#include <gsl/gsl_math.h>
#include <gsl/gsl_vector.h>
#include <gsl/gsl_matrix.h>
#include <gsl/gsl_multifit.h>
#include <gsl/gsl_blas.h>
static int
hilbert_matrix(gsl_matrix * m)
{
const size_t N = m>size1;
const size_t M = m>size2;
size_t i, j;
for (i = 0; i < N; i++)
{
for (j = 0; j < M; j++)
{
gsl_matrix_set(m, i, j, 1.0/(i+j+1.0));
}
}
return GSL_SUCCESS;
}
int
main()
{
const size_t n = 10; /* number of observations */
const size_t p = 8; /* number of model parameters */
size_t i;
gsl_matrix *X = gsl_matrix_alloc(n, p);
gsl_vector *y = gsl_vector_alloc(n);
/* construct Hilbert matrix and rhs vector */
hilbert_matrix(X);
{
double val = 1.0;
for (i = 0; i < n; ++i)
{
gsl_vector_set(y, i, val);
val *= 1.0;
}
}
{
const size_t npoints = 200; /* number of points on Lcurve and GCV curve */
gsl_multifit_linear_workspace *w =
gsl_multifit_linear_alloc(n, p);
gsl_vector *c = gsl_vector_alloc(p); /* OLS solution */
gsl_vector *c_lcurve = gsl_vector_alloc(p); /* regularized solution (Lcurve) */
gsl_vector *c_gcv = gsl_vector_alloc(p); /* regularized solution (GCV) */
gsl_vector *reg_param = gsl_vector_alloc(npoints);
gsl_vector *rho = gsl_vector_alloc(npoints); /* residual norms */
gsl_vector *eta = gsl_vector_alloc(npoints); /* solution norms */
gsl_vector *G = gsl_vector_alloc(npoints); /* GCV function values */
double lambda_l; /* optimal regularization parameter (Lcurve) */
double lambda_gcv; /* optimal regularization parameter (GCV) */
double G_gcv; /* G(lambda_gcv) */
size_t reg_idx; /* index of optimal lambda */
double rcond; /* reciprocal condition number of X */
double chisq, rnorm, snorm;
/* compute SVD of X */
gsl_multifit_linear_svd(X, w);
rcond = gsl_multifit_linear_rcond(w);
fprintf(stderr, "matrix condition number = %e\n", 1.0 / rcond);
/* unregularized (standard) least squares fit, lambda = 0 */
gsl_multifit_linear_solve(0.0, X, y, c, &rnorm, &snorm, w);
chisq = pow(rnorm, 2.0);
fprintf(stderr, "\n=== Unregularized fit ===\n");
fprintf(stderr, "residual norm = %g\n", rnorm);
fprintf(stderr, "solution norm = %g\n", snorm);
fprintf(stderr, "chisq/dof = %g\n", chisq / (n  p));
/* calculate Lcurve and find its corner */
gsl_multifit_linear_lcurve(y, reg_param, rho, eta, w);
gsl_multifit_linear_lcorner(rho, eta, ®_idx);
/* store optimal regularization parameter */
lambda_l = gsl_vector_get(reg_param, reg_idx);
/* regularize with lambda_l */
gsl_multifit_linear_solve(lambda_l, X, y, c_lcurve, &rnorm, &snorm, w);
chisq = pow(rnorm, 2.0) + pow(lambda_l * snorm, 2.0);
fprintf(stderr, "\n=== Regularized fit (Lcurve) ===\n");
fprintf(stderr, "optimal lambda: %g\n", lambda_l);
fprintf(stderr, "residual norm = %g\n", rnorm);
fprintf(stderr, "solution norm = %g\n", snorm);
fprintf(stderr, "chisq/dof = %g\n", chisq / (n  p));
/* calculate GCV curve and find its minimum */
gsl_multifit_linear_gcv(y, reg_param, G, &lambda_gcv, &G_gcv, w);
/* regularize with lambda_gcv */
gsl_multifit_linear_solve(lambda_gcv, X, y, c_gcv, &rnorm, &snorm, w);
chisq = pow(rnorm, 2.0) + pow(lambda_gcv * snorm, 2.0);
fprintf(stderr, "\n=== Regularized fit (GCV) ===\n");
fprintf(stderr, "optimal lambda: %g\n", lambda_gcv);
fprintf(stderr, "residual norm = %g\n", rnorm);
fprintf(stderr, "solution norm = %g\n", snorm);
fprintf(stderr, "chisq/dof = %g\n", chisq / (n  p));
/* output Lcurve and GCV curve */
for (i = 0; i < npoints; ++i)
{
printf("%e %e %e %e\n",
gsl_vector_get(reg_param, i),
gsl_vector_get(rho, i),
gsl_vector_get(eta, i),
gsl_vector_get(G, i));
}
/* output Lcurve corner point */
printf("\n\n%f %f\n",
gsl_vector_get(rho, reg_idx),
gsl_vector_get(eta, reg_idx));
/* output GCV curve corner minimum */
printf("\n\n%e %e\n",
lambda_gcv,
G_gcv);
gsl_multifit_linear_free(w);
gsl_vector_free(c);
gsl_vector_free(c_lcurve);
gsl_vector_free(reg_param);
gsl_vector_free(rho);
gsl_vector_free(eta);
gsl_vector_free(G);
}
gsl_matrix_free(X);
gsl_vector_free(y);
return 0;
}
Robust Linear Regression Example¶
The next program demonstrates the advantage of robust least squares on a dataset with outliers. The program generates linear data pairs on the line , adds some random noise, and inserts 3 outliers into the dataset. Both the robust and ordinary least squares (OLS) coefficients are computed for comparison.
#include <stdio.h>
#include <gsl/gsl_multifit.h>
#include <gsl/gsl_randist.h>
int
dofit(const gsl_multifit_robust_type *T,
const gsl_matrix *X, const gsl_vector *y,
gsl_vector *c, gsl_matrix *cov)
{
int s;
gsl_multifit_robust_workspace * work
= gsl_multifit_robust_alloc (T, X>size1, X>size2);
s = gsl_multifit_robust (X, y, c, cov, work);
gsl_multifit_robust_free (work);
return s;
}
int
main (int argc, char **argv)
{
size_t i;
size_t n;
const size_t p = 2; /* linear fit */
gsl_matrix *X, *cov;
gsl_vector *x, *y, *c, *c_ols;
const double a = 1.45; /* slope */
const double b = 3.88; /* intercept */
gsl_rng *r;
if (argc != 2)
{
fprintf (stderr,"usage: robfit n\n");
exit (1);
}
n = atoi (argv[1]);
X = gsl_matrix_alloc (n, p);
x = gsl_vector_alloc (n);
y = gsl_vector_alloc (n);
c = gsl_vector_alloc (p);
c_ols = gsl_vector_alloc (p);
cov = gsl_matrix_alloc (p, p);
r = gsl_rng_alloc(gsl_rng_default);
/* generate linear dataset */
for (i = 0; i < n  3; i++)
{
double dx = 10.0 / (n  1.0);
double ei = gsl_rng_uniform(r);
double xi = 5.0 + i * dx;
double yi = a * xi + b;
gsl_vector_set (x, i, xi);
gsl_vector_set (y, i, yi + ei);
}
/* add a few outliers */
gsl_vector_set(x, n  3, 4.7);
gsl_vector_set(y, n  3, 8.3);
gsl_vector_set(x, n  2, 3.5);
gsl_vector_set(y, n  2, 6.7);
gsl_vector_set(x, n  1, 4.1);
gsl_vector_set(y, n  1, 6.0);
/* construct design matrix X for linear fit */
for (i = 0; i < n; ++i)
{
double xi = gsl_vector_get(x, i);
gsl_matrix_set (X, i, 0, 1.0);
gsl_matrix_set (X, i, 1, xi);
}
/* perform robust and OLS fit */
dofit(gsl_multifit_robust_ols, X, y, c_ols, cov);
dofit(gsl_multifit_robust_bisquare, X, y, c, cov);
/* output data and model */
for (i = 0; i < n; ++i)
{
double xi = gsl_vector_get(x, i);
double yi = gsl_vector_get(y, i);
gsl_vector_view v = gsl_matrix_row(X, i);
double y_ols, y_rob, y_err;
gsl_multifit_robust_est(&v.vector, c, cov, &y_rob, &y_err);
gsl_multifit_robust_est(&v.vector, c_ols, cov, &y_ols, &y_err);
printf("%g %g %g %g\n", xi, yi, y_rob, y_ols);
}
#define C(i) (gsl_vector_get(c,(i)))
#define COV(i,j) (gsl_matrix_get(cov,(i),(j)))
{
printf ("# best fit: Y = %g + %g X\n",
C(0), C(1));
printf ("# covariance matrix:\n");
printf ("# [ %+.5e, %+.5e\n",
COV(0,0), COV(0,1));
printf ("# %+.5e, %+.5e\n",
COV(1,0), COV(1,1));
}
gsl_matrix_free (X);
gsl_vector_free (x);
gsl_vector_free (y);
gsl_vector_free (c);
gsl_vector_free (c_ols);
gsl_matrix_free (cov);
gsl_rng_free(r);
return 0;
}
The output from the program is shown in Fig. 33.
Large Dense Linear Regression Example¶
The following program demostrates the large dense linear least squares solvers. This example is adapted from Trefethen and Bau, and fits the function on the interval with a degree 15 polynomial. The program generates equally spaced points on this interval, calculates the function value and adds random noise to determine the observation value . The entries of the least squares matrix are , representing a polynomial fit. The matrix is highly illconditioned, with a condition number of about . The program accumulates the matrix into the least squares system in 5 blocks, each with 10000 rows. This way the full matrix is never stored in memory. We solve the system with both the normal equations and TSQR methods. The results are shown in Fig. 34. In the top left plot, the TSQR solution provides a reasonable agreement to the exact solution, while the normal equations method fails completely since the Cholesky factorization fails due to the illconditioning of the matrix. In the bottom left plot, we show the Lcurve calculated from TSQR, which exhibits multiple corners. In the top right panel, we plot a regularized solution using . The TSQR and normal solutions now agree, however they are unable to provide a good fit due to the damping. This indicates that for some illconditioned problems, regularizing the normal equations does not improve the solution. This is further illustrated in the bottom right panel, where we plot the Lcurve calculated from the normal equations. The curve agrees with the TSQR curve for larger damping parameters, but for small , the normal equations approach cannot provide accurate solution vectors leading to numerical inaccuracies in the left portion of the curve.
#include <gsl/gsl_math.h>
#include <gsl/gsl_vector.h>
#include <gsl/gsl_matrix.h>
#include <gsl/gsl_rng.h>
#include <gsl/gsl_randist.h>
#include <gsl/gsl_multifit.h>
#include <gsl/gsl_multilarge.h>
#include <gsl/gsl_blas.h>
#include <gsl/gsl_errno.h>
/* function to be fitted */
double
func(const double t)
{
double x = sin(10.0 * t);
return exp(x*x*x);
}
/* construct a row of the least squares matrix */
int
build_row(const double t, gsl_vector *row)
{
const size_t p = row>size;
double Xj = 1.0;
size_t j;
for (j = 0; j < p; ++j)
{
gsl_vector_set(row, j, Xj);
Xj *= t;
}
return 0;
}
int
solve_system(const int print_data, const gsl_multilarge_linear_type * T,
const double lambda, const size_t n, const size_t p,
gsl_vector * c)
{
const size_t nblock = 5; /* number of blocks to accumulate */
const size_t nrows = n / nblock; /* number of rows per block */
gsl_multilarge_linear_workspace * w =
gsl_multilarge_linear_alloc(T, p);
gsl_matrix *X = gsl_matrix_alloc(nrows, p);
gsl_vector *y = gsl_vector_alloc(nrows);
gsl_rng *r = gsl_rng_alloc(gsl_rng_default);
const size_t nlcurve = 200;
gsl_vector *reg_param = gsl_vector_alloc(nlcurve);
gsl_vector *rho = gsl_vector_calloc(nlcurve);
gsl_vector *eta = gsl_vector_calloc(nlcurve);
size_t rowidx = 0;
double rnorm, snorm, rcond;
double t = 0.0;
double dt = 1.0 / (n  1.0);
while (rowidx < n)
{
size_t nleft = n  rowidx; /* number of rows left to accumulate */
size_t nr = GSL_MIN(nrows, nleft); /* number of rows in this block */
gsl_matrix_view Xv = gsl_matrix_submatrix(X, 0, 0, nr, p);
gsl_vector_view yv = gsl_vector_subvector(y, 0, nr);
size_t i;
/* build (X,y) block with 'nr' rows */
for (i = 0; i < nr; ++i)
{
gsl_vector_view row = gsl_matrix_row(&Xv.matrix, i);
double fi = func(t);
double ei = gsl_ran_gaussian (r, 0.1 * fi); /* noise */
double yi = fi + ei;
/* construct this row of LS matrix */
build_row(t, &row.vector);
/* set right hand side value with added noise */
gsl_vector_set(&yv.vector, i, yi);
if (print_data && (i % 100 == 0))
printf("%f %f\n", t, yi);
t += dt;
}
/* accumulate (X,y) block into LS system */
gsl_multilarge_linear_accumulate(&Xv.matrix, &yv.vector, w);
rowidx += nr;
}
if (print_data)
printf("\n\n");
/* compute Lcurve */
gsl_multilarge_linear_lcurve(reg_param, rho, eta, w);
/* solve large LS system and store solution in c */
gsl_multilarge_linear_solve(lambda, c, &rnorm, &snorm, w);
/* compute reciprocal condition number */
gsl_multilarge_linear_rcond(&rcond, w);
fprintf(stderr, "=== Method %s ===\n", gsl_multilarge_linear_name(w));
fprintf(stderr, "condition number = %e\n", 1.0 / rcond);
fprintf(stderr, "residual norm = %e\n", rnorm);
fprintf(stderr, "solution norm = %e\n", snorm);
/* output Lcurve */
{
size_t i;
for (i = 0; i < nlcurve; ++i)
{
printf("%.12e %.12e %.12e\n",
gsl_vector_get(reg_param, i),
gsl_vector_get(rho, i),
gsl_vector_get(eta, i));
}
printf("\n\n");
}
gsl_matrix_free(X);
gsl_vector_free(y);
gsl_multilarge_linear_free(w);
gsl_rng_free(r);
gsl_vector_free(reg_param);
gsl_vector_free(rho);
gsl_vector_free(eta);
return 0;
}
int
main(int argc, char *argv[])
{
const size_t n = 50000; /* number of observations */
const size_t p = 16; /* polynomial order + 1 */
double lambda = 0.0; /* regularization parameter */
gsl_vector *c_tsqr = gsl_vector_calloc(p);
gsl_vector *c_normal = gsl_vector_calloc(p);
if (argc > 1)
lambda = atof(argv[1]);
/* turn off error handler so normal equations method won't abort */
gsl_set_error_handler_off();
/* solve system with TSQR method */
solve_system(1, gsl_multilarge_linear_tsqr, lambda, n, p, c_tsqr);
/* solve system with Normal equations method */
solve_system(0, gsl_multilarge_linear_normal, lambda, n, p, c_normal);
/* output solutions */
{
gsl_vector *v = gsl_vector_alloc(p);
double t;
for (t = 0.0; t <= 1.0; t += 0.01)
{
double f_exact = func(t);
double f_tsqr, f_normal;
build_row(t, v);
gsl_blas_ddot(v, c_tsqr, &f_tsqr);
gsl_blas_ddot(v, c_normal, &f_normal);
printf("%f %e %e %e\n", t, f_exact, f_tsqr, f_normal);
}
gsl_vector_free(v);
}
gsl_vector_free(c_tsqr);
gsl_vector_free(c_normal);
return 0;
}
References and Further Reading¶
A summary of formulas and techniques for least squares fitting can be found in the “Statistics” chapter of the Annual Review of Particle Physics prepared by the Particle Data Group,
Review of Particle Properties, R.M. Barnett et al., Physical Review D54, 1 (1996) http://pdg.lbl.gov
The Review of Particle Physics is available online at the website given above.
The tests used to prepare these routines are based on the NIST Statistical Reference Datasets. The datasets and their documentation are available from NIST at the following website,
http://www.nist.gov/itl/div898/strd/index.html
More information on Tikhonov regularization can be found in
Hansen, P. C. (1998), RankDeficient and Discrete IllPosed Problems: Numerical Aspects of Linear Inversion. SIAM Monogr. on Mathematical Modeling and Computation, Society for Industrial and Applied Mathematics
M. Rezghi and S. M. Hosseini (2009), A new variant of Lcurve for Tikhonov regularization, Journal of Computational and Applied Mathematics, Volume 231, Issue 2, pages 914924.
The GSL implementation of robust linear regression closely follows the publications
DuMouchel, W. and F. O’Brien (1989), “Integrating a robust option into a multiple regression computing environment,” Computer Science and Statistics: Proceedings of the 21st Symposium on the Interface, American Statistical Association
Street, J.O., R.J. Carroll, and D. Ruppert (1988), “A note on computing robust regression estimates via iteratively reweighted least squares,” The American Statistician, v. 42, pp. 152154.
More information about the normal equations and TSQR approach for solving large linear least squares systems can be found in the publications
Trefethen, L. N. and Bau, D. (1997), “Numerical Linear Algebra”, SIAM.
Demmel, J., Grigori, L., Hoemmen, M. F., and Langou, J. “Communicationoptimal parallel and sequential QR and LU factorizations”, UCB Technical Report No. UCB/EECS200889, 2008.