NAG Library Routine Document
c06saf (fast_gauss)
1
Purpose
c06saf calculates the multidimensional fast Gauss transform.
2
Specification
Fortran Interface
Subroutine c06saf ( 
d, srcs, n, trgs, m, q, p1, p2, k, hin, lhin, tol, v, term, ifail) 
Integer, Intent (In)  ::  d, n, m, lhin  Integer, Intent (Inout)  ::  p1, p2, k, ifail  Real (Kind=nag_wp), Intent (In)  ::  srcs(d,n), trgs(d,m), q(n), hin(lhin), tol  Real (Kind=nag_wp), Intent (Out)  ::  v(m), term(m) 

C Header Interface
#include <nagmk26.h>
void 
c06saf_ (const Integer *d, const double srcs[], const Integer *n, const double trgs[], const Integer *m, const double q[], Integer *p1, Integer *p2, Integer *k, const double hin[], const Integer *lhin, const double *tol, double v[], double term[], Integer *ifail) 

3
Description
c06saf calculates the
$d$dimensional fast Gauss transform (FGT),
$\hat{G}\left(y\right)$, that approximates the discrete Gauss transform (DGT),
$G\left(y\right)$, evaluated at a set of target points
${y}_{\mathit{j}}$, for
$\mathit{j}=1,2,\dots ,m\in {\mathbb{R}}^{d}$. The DGT is defined as:
where
${x}_{\mathit{i}}$, for
$\mathit{i}=1,2,\dots ,n\in {\mathbb{R}}^{d}$, are the Gaussian source points,
${q}_{\mathit{i}}$, for
$\mathit{i}=1,2,\dots ,n\in {\mathbb{R}}^{+}$, are the source weights and
${h}_{\mathit{i}}$, for
$\mathit{i}=1,2,\dots ,n\in {\mathbb{R}}^{+}$, are the source standard deviations (alternatively source scales or source bandwidths).
This subroutine implements the improved FGT algorithm presented in
Raykar and Duraiswami (2005). The algorithm clusters the sources into
$k$ distinct clusters and then computes two Taylor series approximations per cluster with
${p}_{1}$ and
${p}_{2}$ terms respectively. You must provide
${p}_{1}$,
${p}_{2}$ and
$k$ when calling the subroutine. See
Section 7 below for a further discussion on accuracy when choosing their values.
The input array
${\mathbf{hin}}$ of this routine is designed to allow maximum flexibility in the supply of the standard deviation arguments by reusing, in a cyclic manner, elements of the array when it is less than
$n$ elements long. For example, if all Gaussian sources have the same standard deviation then it is only necessary to set
${\mathbf{lhin}}$ to
$1$ and to provide the value of the standard deviation in
${\mathbf{hin}}\left(1\right)$; the routine will then automatically expand
${\mathbf{hin}}$ to be of length
$n$. For further details please see
Section 2.6 in the G01 Chapter Introduction.
4
References
Greengard L and Strain J (1991) The Fast Gauss Transform SIAM J. Sci. Statist. Comput. 12(1) 79–94
Raykar V C and Duraiswami R (2005) Improved Fast Gauss Transform With Variable Source Scales University of Maryland Technical Report CSTR4727/UMIACSTR200534
5
Arguments
 1: $\mathbf{d}$ – IntegerInput

On entry: $d$, the number of dimensions.
Constraint:
${\mathbf{d}}>0$.
 2: $\mathbf{srcs}\left({\mathbf{d}},{\mathbf{n}}\right)$ – Real (Kind=nag_wp) arrayInput

On entry: $x$, the locations of the Gaussian sources.
 3: $\mathbf{n}$ – IntegerInput

On entry: $n$, the number of Gaussian sources.
Constraint:
${\mathbf{n}}>0$.
 4: $\mathbf{trgs}\left({\mathbf{d}},{\mathbf{m}}\right)$ – Real (Kind=nag_wp) arrayInput

On entry: $y$, the locations of the target points at which the FGT will be evaluated.
 5: $\mathbf{m}$ – IntegerInput

On entry: $m$, the number of target points.
Constraint:
${\mathbf{m}}>0$.
 6: $\mathbf{q}\left({\mathbf{n}}\right)$ – Real (Kind=nag_wp) arrayInput

On entry: $q$, the weights of the Gaussian sources.
 7: $\mathbf{p1}$ – IntegerInput/Output

On entry: ${p}_{1}$, the number of terms of the first Taylor series to be evaluated.
On exit:
p1 is unchanged.
Constraint:
${\mathbf{p1}}>0$.
 8: $\mathbf{p2}$ – IntegerInput/Output

On entry: ${p}_{2}$, the number of terms of the second Taylor series to be evaluated.
On exit:
p2 is unchanged.
Constraint:
${\mathbf{p2}}>0$.
 9: $\mathbf{k}$ – IntegerInput/Output

On entry: $k$, the number of clusters into which the source points will be aggregated.
Constraint:
$1\le {\mathbf{k}}\le {\mathbf{n}}$.
 10: $\mathbf{hin}\left({\mathbf{lhin}}\right)$ – Real (Kind=nag_wp) arrayInput

On entry:
$h$, the standard deviations of the Gaussian sources. If
${\mathbf{lhin}}<{\mathbf{n}}$, the array will be expanded automatically by repeating
hin until it is of length
n. See
Section 2.6 in the G01 Chapter Introduction for further information.
Constraint:
${\mathbf{hin}}\left(\mathit{i}\right)>0.0$, for $\mathit{i}=1,2,\dots ,{\mathbf{lhin}}$.
 11: $\mathbf{lhin}$ – IntegerInput

On entry: the length of the array
hin.
Constraint:
$1\le {\mathbf{lhin}}\le {\mathbf{n}}$.
 12: $\mathbf{tol}$ – Real (Kind=nag_wp)Input

On entry: $\epsilon $, the desired accuracy of the FGT approximation of the DGT. Determines the radius of the source clusters: the contribution of a source point to the FGT approximation at a target point is disregarded if the source is outside the corresponding cluster radius.
Constraint:
${\mathbf{tol}}>0.0$.
 13: $\mathbf{v}\left({\mathbf{m}}\right)$ – Real (Kind=nag_wp) arrayOutput

On exit: $\hat{G}\left(y\right)$, the value of the FGT evaluated at $y$.
 14: $\mathbf{term}\left({\mathbf{m}}\right)$ – Real (Kind=nag_wp) arrayOutput

On exit: ${\mathbf{term}}\left(j\right)$ contains the absolute value of the final Taylor series term that is largest, relative to the size of the sum of the corresponding series, across all clusters that contribute to the FGT at target point ${\mathbf{v}}\left(j\right)$.
 15: $\mathbf{ifail}$ – IntegerInput/Output

On entry:
ifail must be set to
$0$,
$1\text{or}1$. If you are unfamiliar with this argument you should refer to
Section 3.4 in How to Use the NAG Library and its Documentation for details.
For environments where it might be inappropriate to halt program execution when an error is detected, the value
$1\text{or}1$ is recommended. If the output of error messages is undesirable, then the value
$1$ is recommended. Otherwise, if you are not familiar with this argument, the recommended value is
$0$.
When the value $\mathbf{1}\text{or}\mathbf{1}$ is used it is essential to test the value of ifail on exit.
On exit:
${\mathbf{ifail}}={\mathbf{0}}$ unless the routine detects an error or a warning has been flagged (see
Section 6).
6
Error Indicators and Warnings
If on entry
${\mathbf{ifail}}=0$ or
$1$, explanatory error messages are output on the current error message unit (as defined by
x04aaf).
Errors or warnings detected by the routine:
 ${\mathbf{ifail}}=1$

On entry, ${\mathbf{d}}=\u2329\mathit{\text{value}}\u232a$.
Constraint: ${\mathbf{d}}>0$.
 ${\mathbf{ifail}}=2$

On entry, ${\mathbf{n}}=\u2329\mathit{\text{value}}\u232a$.
Constraint: ${\mathbf{n}}>0$.
 ${\mathbf{ifail}}=3$

On entry, ${\mathbf{m}}=\u2329\mathit{\text{value}}\u232a$.
Constraint: ${\mathbf{m}}>0$.
 ${\mathbf{ifail}}=4$

On entry, ${\mathbf{p1}}=\u2329\mathit{\text{value}}\u232a$.
Constraint: ${\mathbf{p1}}>0$.
 ${\mathbf{ifail}}=5$

On entry, ${\mathbf{p2}}=\u2329\mathit{\text{value}}\u232a$.
Constraint: ${\mathbf{p2}}>0$.
 ${\mathbf{ifail}}=6$

On entry, ${\mathbf{k}}=\u2329\mathit{\text{value}}\u232a$ and ${\mathbf{n}}=\u2329\mathit{\text{value}}\u232a$.
Constraint: $1\le {\mathbf{k}}\le {\mathbf{n}}$.
 ${\mathbf{ifail}}=7$

On entry, ${\mathbf{hin}}\left(\u2329\mathit{\text{value}}\u232a\right)=\u2329\mathit{\text{value}}\u232a$.
Constraint: ${\mathbf{hin}}\left(\mathit{i}\right)>0.0$, for $\mathit{i}=1,2,\dots ,{\mathbf{lhin}}$.
 ${\mathbf{ifail}}=8$

On entry, ${\mathbf{lhin}}=\u2329\mathit{\text{value}}\u232a$.
Constraint: $1\le {\mathbf{lhin}}\le {\mathbf{n}}$.
 ${\mathbf{ifail}}=9$

On entry, ${\mathbf{tol}}=\u2329\mathit{\text{value}}\u232a$.
Constraint: ${\mathbf{tol}}>0.0$.
 ${\mathbf{ifail}}=10$

On exit,
${\mathbf{p1}}=\u2329\mathit{\text{value}}\u232a$,
${\mathbf{p2}}=\u2329\mathit{\text{value}}\u232a$ and
${\mathbf{k}}=\u2329\mathit{\text{value}}\u232a$.
p1,
p2 or
k may have been too small to calculate
${\mathbf{v}}\left({\mathbf{m}}\right)$ to the required accuracy
tol.
 ${\mathbf{ifail}}=99$
An unexpected error has been triggered by this routine. Please
contact
NAG.
See
Section 3.9 in How to Use the NAG Library and its Documentation for further information.
 ${\mathbf{ifail}}=399$
Your licence key may have expired or may not have been installed correctly.
See
Section 3.8 in How to Use the NAG Library and its Documentation for further information.
 ${\mathbf{ifail}}=999$
Dynamic memory allocation failed.
See
Section 3.7 in How to Use the NAG Library and its Documentation for further information.
7
Accuracy
The routine does not currently implement the procedure described in
Raykar and Duraiswami (2005) for automatically determining values for
p1,
p2 and
k. Nonzero values must therefore be provided for these parameters when calling the routine.
For a given set of source and target points and a specified tolerance, there is an interaction between the number of clusters,
k, and the number of Taylor series terms,
p1 and
p2: if the sources are clustered together in fewer clusters (small
k) then more terms will be needed in each cluster's Taylor series (large
p1 and
p2) to capture the effect of the source points further from the cluster centres. Increasing the number of clusters reduces their individual radii and requires fewer terms in their Taylor series, but increases the number of Taylor series that must be evaluated overall.
If the source and target points are uniformly distributed in a unit hypercube,
Raykar and Duraiswami (2005) advise users to select
${\mathbf{k}}\sim \u2308{\left({h}_{\mathrm{max}}+{h}_{\mathrm{min}}/2\right)}^{d}\u2309$. If the points are not uniformly distributed then more clusters than this will be needed to calculate the FGT to within the specified
tol without requiring prohibitively large values for
p1 and
p2.
There is less guidance available for selecting good values for
p1 and
p2. As the number of Taylor series terms is a major factor on the computation time taken by this routine, you are advised to initially try a small number, e.g.
$20$ or so, and then tune
p1 and
p2 up or down based on the values returned. Note that
p1 and
p2 are not required to have identical values.
To aid the selection of values for
p1,
p2 and
k, the routine returns in
${\mathbf{term}}\left(j\right)$ the absolute value of the final Taylor series term that is largest, relative to the size of the sum of the corresponding series, across all clusters that contribute to the FGT at target point
$j$. If this value is larger than the requested
tol, the routine will additionally return a nonzero
ifail value and you are advised to rerun the routine with larger
p1,
p2 or
k.
8
Parallelism and Performance
c06saf is threaded by NAG for parallel execution in multithreaded implementations of the NAG Library.
c06saf makes calls to BLAS and/or LAPACK routines, which may be threaded within the vendor library used by this implementation. Consult the documentation for the vendor library for further information.
Please consult the
X06 Chapter Introduction for information on how to control and interrogate the OpenMP environment used within this routine. Please also consult the
Users' Note for your implementation for any additional implementationspecific information.
The time complexity of the algorithm implemented by this subroutine is $O\left(M+N\right)$, versus the $O\left(MN\right)$ time complexity of evaluating the DGT directly.
10
Example
In this example values for $x$, $y$, ${p}_{1}$, ${p}_{2}$, $k$ and $\epsilon $ are read in, $\hat{G}\left(y\right)$ calculated and the results displayed.
10.1
Program Text
Program Text (c06safe.f90)
10.2
Program Data
Program Data (c06safe.d)
10.3
Program Results
Program Results (c06safe.r)