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uncmin.c
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uncmin.c
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/*
* R : A Computer Language for Statistical Data Analysis
* Copyright (C) 1997-2001 Saikat DebRoy and the
* R Development Core Team
*
* This program is free software; you can redistribute it and/or modify
* it under the terms of the GNU General Public License as published by
* the Free Software Foundation; either version 2 of the License, or
* (at your option) any later version.
*
* This program is distributed in the hope that it will be useful,
* but WITHOUT ANY WARRANTY; without even the implied warranty of
* MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the
* GNU General Public License for more details.
*
* You should have received a copy of the GNU General Public License
* along with this program; if not, write to the Free Software
* Foundation, Inc., 59 Temple Place, Suite 330, Boston, MA 02111-1307
* USA
*/
/* ../appl/uncmin.f
-- translated by f2c (version of 1 June 1993 23:00:00).
-- and hand edited by Saikat DebRoy
*/
/*--- The Dennis + Schnabel Minimizer -- used by R's nlm() ---*/
#include <math.h>
#include <R_ext/Applic.h>
#include <R_ext/Boolean.h>
#include <R_ext/PrtUtil.h> /* printRealVector */
#include <R_ext/Linpack.h> /* ddot, dnrm2, dtrsl, dscal */
#define MATHLIB_PRIVATE
#include <Rmath.h> /* for dimach */
#undef MATHLIB_PRIVATE
/* CC subroutines mvmlt[lsu] should be REPLACED by BLAS ones!
* CC
* CC--- choldc(nr,n,a,diagmx,tol,addmax) is ``choleski + tolerance''
* CC ------
* CC it should make use of BLAS routines as [linkpack's dpofa!] */
#ifndef HAVE_HYPOT
# define hypot pythag
#endif
void fdhess(int n, double *x, double fval, fcn_p fun, void *state,
double *h, int nfd, double *step, double *f,
int ndigit, double *typx)
{
/* calculates a numerical approximation to the upper triangular
* portion of the second derivative matrix (the hessian).
* Algorithm A5.6.2 from Dennis and Schnabel (1983), numerical methods
* for unconstrained optimization and nonlinear equations,
* prentice-hall, 321-322.
* programmed by richard h. jones, january 11, 1989
* INPUT to subroutine
* n the number of parameters
* x vector of parameter values
* fval double precision value of function at x
* fun a function provided by the user which must be declared as
* external in the calling program. its call must
* be of the call fun(n,x,state,fval) where fval is the
* computed value of the function
* state information other than x and n that fun requires.
* state is not modified in fdhess (but can be modified by fun).
* nfd first dimension of h in the calling program
* OUTPUT from subroutine
* h an n by n matrix of the approximate hessian
* Work space :
* step a real array of length n
* f a double precision array of length n
*/
int i, j;
double tempi, tempj, fii, eta, fij;
eta = pow(10.0, -ndigit/3.0);
for (i = 0; i < n; ++i) {
step[i] = eta * fmax2(x[i], typx[i]);
if (typx[i] < 0.)
step[i] = -step[i];
tempi = x[i];
x[i] += step[i];
step[i] = x[i] - tempi;
(*fun)(n, x, &f[i], state);
x[i] = tempi;
}
for (i = 0; i < n; ++i) {
tempi = x[i];
x[i] += step[i] * 2.;
(*fun)(n, x, &fii, state);
h[i + i * nfd] = (fval - f[i] + (fii - f[i]))/(step[i] * step[i]);
x[i] = tempi + step[i];
for (j = i + 1; j < n; ++j) {
tempj = x[j];
x[j] += step[j];
(*fun)(n, x, &fij, state);
h[i + j * nfd] = (fval - f[i] + (fij - f[j]))/(step[i] * step[j]);
x[j] = tempj;
}
x[i] = tempi;
}
} /* fdhess */
static void d1fcn_dum(int n, double *x, double *g, void *state)
{
/* dummy routine to prevent unsatisfied external diagnostic
* when specific analytic gradient function not supplied. */
}
static void d2fcn_dum(int nr, int n, double *x, double *h, void *state)
{
/* dummy routine to prevent unsatisfied external diagnostic
* when specific analytic hessian function not supplied. */
}
static void mvmltl(int nr, int n, double *a, double *x, double *y)
{
/* compute y = l x
* where l is a lower triangular matrix stored in a
* PARAMETERS :
* nr --> row dimension of matrix
* n --> dimension of problem
* a(n,n) --> lower triangular (n*n) matrix
* x(n) --> operand vector
* y(n) <-- result vector
* note
* x and y cannot share storage */
int i, j;
double sum;
for (i = 0; i < n; ++i) {
sum = 0.;
for (j = 0; j <= i; ++j)
sum += a[i + j * nr] * x[j];
y[i] = sum;
}
} /* mvmltl */
static void mvmltu(int nr, int n, double *a, double *x, double *y)
{
/* compute y = (L+) x
* where L is a lower triangular matrix stored in a
* (L-transpose (L+) is taken implicitly)
* ARGUMENTS :
* nr --> row dimension of matrix
* n --> dimension of problem
* a(nr,1) --> lower triangular (n*n) matrix
* x(n) --> operand vector
* y(n) <-- result vector
* NOTE : x and y cannot share storage */
int i, length, one = 1;
for (i = 0, length = n; i < n; --length, ++i)
y[i] = F77_CALL(ddot)(&length, &a[i + i * nr], &one, &x[i], &one);
} /* mvmltu */
static void mvmlts(int nr, int n, double *a, double *x, double *y)
{
/* compute y=ax
* where "a" is a symmetric (n*n) matrix stored in its lower
* triangular part and x,y are n-vectors
* PARAMETERS :
* nr --> row dimension of matrix
* n --> dimension of problem
* a(n,n) --> symmetric (n*n) matrix stored in
* lower triangular part and diagonal
* x(n) --> operand vector
* y(n) <-- result vector
* NOTE: x and y cannot share storage.
*/
int i, j;
double sum;
for (i = 0; i < n; ++i) {
sum = 0.;
for (j = 0; j <= i; ++j) {
sum += a[i + j * nr] * x[j];
}
for (j = i+1; j < n; ++j) {
sum += a[j + i * nr] * x[j];
}
y[i] = sum;
}
} /* mvmlts */
static void lltslv(int nr, int n, double *a, double *x, double *b)
{
/* solve ax=b where a has the form l(l-transpose)
* but only the lower triangular part, l, is stored.
* PARAMETERS :
* nr --> row dimension of matrix
* n --> dimension of problem
* a(n,n) --> matrix of form l(l-transpose).
* on return a is unchanged.
* x(n) <-- solution vector
* b(n) --> right-hand side vector
* note
* if b is not required by calling program, then
* b and x may share the same storage. */
int job = 0, info;
if( x != b) Memcpy(x, b, n);
F77_CALL(dtrsl)(a, &nr, &n, x, &job, &info);
job = 10;
F77_CALL(dtrsl)(a, &nr, &n, x, &job, &info);
} /* lltslv */
static void
choldc(int nr, int n, double *a, double diagmx, double tol, double *addmax)
{
/* Find 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.
* PARAMETERS :
* nr --> row dimension of matrix
* n --> dimension of problem
* a(n,n) <--> on entry: matrix for which to find perturbed
* cholesky decomposition
* on exit: contains l of ll+ decomposition
* in lower triangular part and diagonal of "a"
* diagmx --> maximum diagonal element of "a"
* tol --> tolerance
* addmax <-- maximum amount implicitly added to diagonal of "a"
* in forming the cholesky decomposition of a+d
* internal variables
* aminl smallest element allowed on diagonal of l
* amnlsq =aminl**2
* offmax maximum off-diagonal element in column of a
* description
* the normal cholesky decomposition is performed. however, if at any
* point the algorithm would attempt to set l(i,i)=sqrt(temp)
* with temp < tol*diagmx, then l(i,i) is set to sqrt(tol*diagmx)
* instead. this is equivalent to adding tol*diagmx-temp to a(i,i)
*/
double tmp1, tmp2;
int i, j, k;
double aminl, offmax, amnlsq;
double sum;
*addmax = 0.0;
aminl = sqrt(diagmx * tol);
amnlsq = aminl * aminl;
/* form row i of l */
for (i = 0; i < n; ++i) {
/* find diagonal elements of l */
sum = 0.;
for (k = 0; k < i; ++k)
sum += a[i + k * nr] * a[i + k * nr];
tmp1 = a[i + i * nr] - sum;
if (tmp1 >= amnlsq) {
a[i + i * nr] = sqrt(tmp1);
}
else {
/* find maximum off-diagonal element in row */
offmax = 0.;
for (j = 0; j < i; ++j) {
if(offmax < (tmp2 = fabs(a[i + j * nr])))
offmax = tmp2;
}
if (offmax <= amnlsq) offmax = amnlsq;
/* add to diagonal element to
* allow cholesky decomposition to continue */
a[i + i * nr] = sqrt(offmax);
if(*addmax < (tmp2 = offmax - tmp1)) *addmax = tmp2;
}
/* find i,j element of lower triangular matrix */
for (j = 0; j < i; ++j) {
sum = 0.;
for (k = 0; k < j; ++k)
sum += a[i + k * nr] * a[j + k * nr];
a[i + j * nr] = (a[i + j * nr] - sum) / a[j + j * nr];
}
}
} /* choldc */
static void qraux1(int nr, int n, double *r, int i)
{
/* Interchange rows i,i+1 of the upper hessenberg matrix r, columns i to n .
* PARAMETERS :
* nr --> row dimension of matrix
* n --> dimension of matrix
* r[n*n] <--> upper hessenberg matrix
* i --> index of row to interchange (i < n-1)
*/
double tmp;
double *r1, *r2;
/* pointer arithmetic : */
r1 = r + i + i * nr;
r2 = r1 + 1;
while(n-- > i) {
tmp = *r1; *r1 = *r2; *r2 = tmp;
r1 += nr;
r2 += nr;
}
} /* qraux1 */
static void qraux2(int nr, int n, double *r, int i, double a, double b)
{
/* Pre-multiply r by the jacobi rotation j(i,i+1,a,b) .
* PARAMETERS :
* nr --> row dimension of matrix
* n --> dimension of matrix
* r(n,n) <--> upper hessenberg matrix
* i --> index of row
* a --> scalar
* b --> scalar */
double c, s;
double y, z, den;
double *r1, *r2;
den = hypot(a,b);
c = a / den;
s = b / den;
/* pointer arithmetic : */
r1 = r + i + i*nr;
r2 = r1 + 1;
while(n-- > i) {
y = *r1;
z = *r2;
*r1 = c * y - s * z;
*r2 = s * y + c * z;
r1 += nr;
r2 += nr;
}
} /* qraux2 */
static void
qrupdt(int nr, int n, double *a, double *u, double *v)
{
/* Find an orthogonal (n*n) matrix (q*) and an upper triangular (n*n)
* matrix (r*) such that (q*)(r*)=r+u(v+)
* PARAMETERS :
* nr --> row dimension of matrix
* n --> dimension of problem
* a(n,n) <--> on input: contains r
* on output: contains (r*)
* u(n) --> vector
* v(n) --> vector */
int i, j, k;
double t1, t2;
int ii;
/* determine last non-zero in u(.) */
for(k = n-1; k > 0 && u[k] == 0.0; k--)
;
/* (k-1) jacobi rotations transform
* r + u(v+) --> (r*) + (u(1)*e1)(v+)
* which is upper hessenberg */
if (k > 0) {
ii = k;
while(ii > 0) {
i = ii - 1;
if (u[i] == 0.0) {
qraux1(nr, n, a, i);
u[i] = u[ii];
} else {
qraux2(nr, n, a, i, u[i], -u[ii]);
u[i] = hypot(u[i], u[ii]);
}
ii = i;
}
}
/* r <-- r + (u(1)*e1)(v+) */
for (j = 0; j < n; ++j)
a[j * nr] += u[0] * v[j];
/* (k-1) jacobi rotations transform upper hessenberg r
* to upper triangular (r*) */
for (i = 0; i < k; ++i) {
if (a[i + i * nr] == 0.)
qraux1(nr, n, a, i);
else {
t1 = a[i + i * nr];
t2 = -a[i + 1 + i * nr];
qraux2(nr, n, a, i, t1, t2);
}
}
} /* qrupdt */
static void
tregup(int nr, int n, double *x, double f, double *g, double *a, fcn_p fcn,
void *state, double *sc, double *sx, Rboolean nwtake,
double stepmx, double steptl, double *dlt, int *iretcd,
double *xplsp, double *fplsp, double *xpls, double *fpls,
Rboolean *mxtake,
int method, double *udiag)
{
/* TRust REGion UPdating
* == == ==
* Decide whether to accept xpls = x+sc as the next iterate and
* update the trust region radius dlt.
* Used iff method == 2 or 3
* PARAMETERS :
* nr --> row dimension of matrix
* n --> dimension of problem
* x(n) --> old iterate x[k-1]
* f --> function value at old iterate, f(x)
* g(n) --> gradient at old iterate, g(x), or approximate
* a(n,n) --> cholesky decomposition of hessian in
* lower triangular part and diagonal.
* hessian or approx in upper triangular part
* fcn --> name of subroutine to evaluate function
* state <--> information other than x and n that fcn requires.
* state is not modified in tregup (but can be
* modified by fcn).
* sc(n) --> current step
* sx(n) --> diagonal scaling matrix for x
* nwtake --> boolean, = TRUE if newton step taken
* stepmx --> maximum allowable step size
* steptl --> relative step size at which successive iterates
* considered close enough to terminate algorithm
* dlt <--> trust region radius
* iretcd <--> return code
* =0 xpls accepted as next iterate;
* dlt trust region for next iteration.
* =1 xpls unsatisfactory but accepted as next iterate
* because xpls-x < smallest allowable step length.
* =2 f(xpls) too large. continue current iteration
* with new reduced dlt.
* =3 f(xpls) sufficiently small, but quadratic model
* predicts f(xpls) sufficiently well to continue
* current iteration with new doubled dlt.
* xplsp(n) <--> workspace [value needs to be retained between
* succesive calls of k-th global step]
* fplsp <--> [retain value between successive calls]
* xpls(n) <-- new iterate x[k]
* fpls <-- function value at new iterate, f(xpls)
* mxtake <-- boolean flag indicating step of maximum length used
* ipr --> device to which to send output
* method --> algorithm to use to solve minimization problem
* =1 line search
* =2 double dogleg
* =3 more-hebdon
* udiag(n) --> diagonal of hessian in a(.,.) */
double dltf;
double temp1;
int i, j, one = 1;
double dltfp, dltmp;
double rln, slp;
*mxtake = FALSE;
for (i = 0; i < n; ++i)
xpls[i] = x[i] + sc[i];
(*fcn)(n, xpls, fpls, state);
dltf = *fpls - f;
slp = F77_CALL(ddot)(&n, g, &one, sc, &one);
/* next statement added for case of compilers which do not optimize
evaluation of next "if" statement (in which case fplsp could be
undefined).
if (*iretcd == 4) {
*fplsp = 0.;
}
*/
if (*iretcd == 3 && (*fpls >= *fplsp || dltf > slp * 1e-4)) {
/* reset xpls to xplsp and terminate global step */
*iretcd = 0;
for (i = 0; i < n; ++i)
xpls[i] = xplsp[i];
*fpls = *fplsp;
*dlt *= .5;
}
else {
/* fpls too large */
if (dltf > slp * 1e-4) {
rln = 0.;
for (i = 0; i < n; ++i) {
temp1 = fabs(sc[i])/fmax2(fabs(xpls[i]), 1./sx[i]);
if(rln < temp1) rln = temp1;
}
if (rln < steptl) {
/* cannot find satisfactory xpls sufficiently distinct from x */
*iretcd = 1;
}
else {
/* reduce trust region and continue global step */
*iretcd = 2;
dltmp = -slp * *dlt / ((dltf - slp) * 2.);
if (dltmp < *dlt * .1)
*dlt *= .1;
else
*dlt = dltmp;
}
}
else {
/* fpls sufficiently small */
dltfp = 0.;
if (method == 2) {
for (i = 0; i < n; ++i) {
temp1 = 0.;
for (j = i; j < n; ++j)
temp1 += a[j + i * nr] * sc[j];
dltfp += temp1 * temp1;
}
}
else { /* method != 2 */
for (i = 0; i < n; ++i) {
dltfp += udiag[i] * sc[i] * sc[i];
temp1 = 0.;
for (j = i+1; j < n; ++j)
temp1 += a[i + j * nr] * sc[i] * sc[j];
dltfp += temp1 * 2.;
}
}
dltfp = slp + dltfp / 2.;
if (*iretcd != 2 && fabs(dltfp - dltf) <= fabs(dltf) * 0.1
&& nwtake && *dlt <= stepmx * .99) {
/* double trust region and continue global step */
*iretcd = 3;
for (i = 0; i < n; ++i)
xplsp[i] = xpls[i];
*fplsp = *fpls;
temp1 = *dlt * 2.0;
*dlt = fmin2(temp1, stepmx);
}
else {
/* accept xpls as next iterate. choose new trust region. */
*iretcd = 0;
if (*dlt > stepmx * .99)
*mxtake = TRUE;
if (dltf >= dltfp * .1) {
/* decrease trust region for next iteration */
*dlt *= .5;
}
else {
/* check whether to increase trust region for next iteration */
if (dltf <= dltfp * .75) {
temp1 = *dlt * 2.0;
*dlt = fmin2(temp1, stepmx);
}
}
}
}
}
} /* tregup */
static void
lnsrch(int n, double *x, double f, double *g, double *p, double *xpls,
double *fpls, fcn_p fcn, void *state, Rboolean *mxtake, int *iretcd,
double stepmx, double steptl, double *sx)
{
/* Find a next newton iterate by line search. (iff method == 1)
* PARAMETERS :
* n --> dimension of problem
* x(n) --> old iterate: x[k-1]
* f --> function value at old iterate, f(x)
* g(n) --> gradient at old iterate, g(x), or approximate
* p(n) --> non-zero newton step
* xpls(n) <-- new iterate x[k]
* fpls <-- function value at new iterate, f(xpls)
* fcn --> name of subroutine to evaluate function
* state <--> information other than x and n that fcn requires.
* state is not modified in lnsrch (but can be
* modified by fcn).
* iretcd <-- return code
* mxtake <-- boolean flag indicating step of maximum length used
* stepmx --> maximum allowable step size
* steptl --> relative step size at which successive iterates
* considered close enough to terminate algorithm
* sx(n) --> diagonal scaling matrix for x
* internal variables
* sln newton length
* rln relative length of newton step
*/
int i, one = 1;
Rboolean firstback = TRUE;
double disc;
double a3, b;
double t1, t2, t3, lambda, tlmbda, rmnlmb;
double scl, rln, sln, slp;
double temp1;
double pfpls = 0., plmbda = 0.; /* -Wall */
*mxtake = FALSE;
*iretcd = 2;
temp1 = 0.;
for (i = 0; i < n; ++i)
temp1 += sx[i] * sx[i] * p[i] * p[i];
sln = sqrt(temp1);
if (sln > stepmx) {
/* newton step longer than maximum allowed */
scl = stepmx / sln;
F77_CALL(dscal)(&n, &scl, p, &one);
sln = stepmx;
}
slp = F77_CALL(ddot)(&n, g, &one, p, &one);
rln = 0.;
for (i = 0; i < n; ++i) {
temp1 = fabs(p[i])/ fmax2(fabs(x[i]), 1./sx[i]);
if(rln < temp1) rln = temp1;
}
rmnlmb = steptl / rln;
lambda = 1.0;
/* check if new iterate satisfactory. generate new lambda if necessary. */
while(*iretcd > 1) {
for (i = 0; i < n; ++i)
xpls[i] = x[i] + lambda * p[i];
(*fcn)(n, xpls, fpls, state);
if (*fpls <= f + slp * 1e-4 * lambda) { /* solution found */
*iretcd = 0;
if (lambda == 1. && sln > stepmx * .99) *mxtake = TRUE;
return;
}
/* else : solution not (yet) found */
/* First find a point with a finite value */
if (lambda < rmnlmb) {
/* no satisfactory xpls found sufficiently distinct from x */
*iretcd = 1;
return;
}
else { /* calculate new lambda */
/* modifications by BDR 2000/01/05 to cover non-finite values
* ">=" instead of "==" : MM 2001/07/24 */
if (*fpls >= DBL_MAX) {
lambda *= 0.1;
firstback = TRUE;
}
else {
if (firstback) { /* first backtrack: quadratic fit */
tlmbda = -lambda * slp / ((*fpls - f - slp) * 2.);
firstback = FALSE;
}
else { /* all subsequent backtracks: cubic fit */
t1 = *fpls - f - lambda * slp;
t2 = pfpls - f - plmbda * slp;
t3 = 1. / (lambda - plmbda);
a3 = 3. * t3 * (t1 / (lambda * lambda)
- t2 / (plmbda * plmbda));
b = t3 * (t2 * lambda / (plmbda * plmbda)
- t1 * plmbda / (lambda * lambda));
disc = b * b - a3 * slp;
if (disc > b * b)
/* only one positive critical point, must be minimum */
tlmbda = (-b + ((a3 < 0)? -sqrt(disc): sqrt(disc))) /a3;
else
/* both critical points positive, first is minimum */
tlmbda = (-b + ((a3 < 0)? sqrt(disc): -sqrt(disc))) /a3;
if (tlmbda > lambda * .5)
tlmbda = lambda * .5;
}
plmbda = lambda;
pfpls = *fpls;
if (tlmbda < lambda * .1)
lambda *= .1;
else
lambda = tlmbda;
}
}
}
} /* lnsrch */
static void
dog_1step(int nr, int n, double *g, double *a, double *p, double *sx,
double rnwtln, double *dlt, Rboolean *nwtake, Rboolean *fstdog,
double *ssd, double *v, double *cln, double *eta, double *sc,
double stepmx)
{
/* Find new step by double dogleg algorithm (iff method == 2);
* repeatedly called by dogdrv() only.
* PARAMETERS :
* nr --> row dimension of matrix
* n --> dimension of problem
* g(n) --> gradient at current iterate, g(x)
* a(n,n) --> cholesky decomposition of hessian in
* lower part and diagonal
* p(n) --> newton step
* sx(n) --> diagonal scaling matrix for x
* rnwtln --> newton step length
* dlt <--> trust region radius
* nwtake <--> boolean, =.true. if newton step taken
* fstdog <--> boolean, =.true. if on first leg of dogleg
* ssd(n) <--> workspace [cauchy step to the minimum of the
* quadratic model in the scaled steepest descent
* direction] [retain value between successive calls]
* v(n) <--> workspace [retain value between successive calls]
* cln <--> cauchy length
* [retain value between successive calls]
* eta [retain value between successive calls]
* sc(n) <-- current step
* ipr --> device to which to send output
* stepmx --> maximum allowable step size
* internal variables
* cln length of cauchy step */
int i, j, one = 1;
double alam, bet, alpha, tmp, dot1, dot2;
/* can we take newton step */
*nwtake = (rnwtln <= *dlt);
if (*nwtake) {
for (i = 0; i < n; ++i)
sc[i] = p[i];
*dlt = rnwtln;
return;
}
/* else *nwtake = FALSE :
* newton step too long -- cauchy step is on double dogleg curve */
if (*fstdog) {
/* calculate double dogleg curve (ssd) */
*fstdog = FALSE;
alpha = 0.;
for (i = 0; i < n; ++i)
alpha += g[i] * g[i] / (sx[i] * sx[i]);
bet = 0.;
for (i = 0; i < n; ++i) {
tmp = 0.;
for (j = i; j < n; ++j)
tmp += a[j + i * nr] * g[j] / (sx[j] * sx[j]);
bet += tmp * tmp;
}
for (i = 0; i < n; ++i)
ssd[i] = -(alpha / bet) * g[i] / sx[i];
*cln = alpha * sqrt(alpha) / bet;
*eta = (.8 * alpha * alpha /
(-bet * F77_CALL(ddot)(&n, g, &one, p, &one))) + .2;
for (i = 0; i < n; ++i)
v[i] = *eta * sx[i] * p[i] - ssd[i];
if (*dlt == -1.) *dlt = fmin2(*cln, stepmx);
}
if (*eta * rnwtln <= *dlt) {
/* take partial step in newton direction */
for (i = 0; i < n; ++i)
sc[i] = *dlt / rnwtln * p[i];
}
else if (*cln >= *dlt) {
/* take step in steepest descent direction */
for (i = 0; i < n; ++i)
sc[i] = *dlt / *cln * ssd[i] / sx[i];
}
else {
/* calculate convex combination of ssd and eta*p
which has scaled length dlt */
dot1 = F77_CALL(ddot)(&n, v, &one, ssd, &one);
dot2 = F77_CALL(ddot)(&n, v, &one, v, &one);
alam = (-dot1 + sqrt(dot1 * dot1 - dot2 * (*cln * *cln - *dlt * *dlt)))
/ dot2;
for (i = 0; i < n; ++i)
sc[i] = (ssd[i] + alam * v[i]) / sx[i];
}
} /* dog_1step */
static void
dogdrv(int nr, int n, double *x, double f, double *g, double *a, double *p,
double *xpls, double *fpls, fcn_p fcn, void *state, double *sx,
double stepmx, double steptl, double *dlt, int *iretcd, Rboolean *mxtake,
double *sc, double *wrk1, double *wrk2, double *wrk3, int *itncnt)
{
/* Find a next newton iterate (xpls) by the double dogleg method
* (iff method == 2 ).
* PARAMETERS :
* nr --> row dimension of matrix
* n --> dimension of problem
* x(n) --> old iterate x[k-1]
* f --> function value at old iterate, f(x)
* g(n) --> gradient at old iterate, g(x), or approximate
* a(n,n) --> cholesky decomposition of hessian
* in lower triangular part and diagonal
* p(n) --> newton step
* xpls(n) <-- new iterate x[k]
* fpls <-- function value at new iterate, f(xpls)
* fcn --> name of subroutine to evaluate function
* state <--> information other than x and n that fcn requires.
* state is not modified in dogdrv (but can be
* modified by fcn).
* sx(n) --> diagonal scaling matrix for x
* stepmx --> maximum allowable step size
* steptl --> relative step size at which successive iterates
* considered close enough to terminate algorithm
* dlt <--> trust region radius
* [retain value between successive calls]
* iretcd <-- return code
* =0 satisfactory xpls found
* =1 failed to find satisfactory xpls sufficiently
* distinct from x
* mxtake <-- boolean flag indicating step of maximum length used
* sc(n) --> workspace [current step]
* wrk1(n) --> workspace (and place holding argument to tregup)
* wrk2(n) --> workspace
* wrk3(n) --> workspace
* ipr --> device to which to send output */
Rboolean fstdog, nwtake;
int i;
double fplsp, rnwtln, eta = 0.0, cln = 0.0, tmp; /* -Wall */
*iretcd = 4;
fstdog = TRUE;
tmp = 0.;
for (i = 0; i < n; ++i)
tmp += sx[i] * sx[i] * p[i] * p[i];
rnwtln = sqrt(tmp);
while(*iretcd > 1) {
/* find new step by double dogleg algorithm */
dog_1step(nr, n, g, a, p, sx, rnwtln, dlt, &nwtake,
&fstdog, wrk1, wrk2, &cln, &eta, sc, stepmx);
/* check new point and update trust region */
tregup(nr, n, x, f, g, a, (fcn_p)fcn, state, sc, sx, nwtake, stepmx,
steptl, dlt, iretcd, wrk3, &fplsp, xpls, fpls, mxtake,
2, wrk1);
}
} /* dogdrv */
static void
hook_1step(int nr, int n, double *g, double *a, double *udiag, double *p,
double *sx, double rnwtln, double *dlt, double *amu, double dltp,
double *phi, double *phip0, Rboolean *fstime, double *sc,
Rboolean *nwtake, double *wrk0, double epsm)
{
/* Find new step by more-hebdon algorithm (iff method == 3);
* repeatedly called by hookdrv() only.
* PARAMETERS :
* nr --> row dimension of matrix
* n --> dimension of problem
* g(n) --> gradient at current iterate, g(x)
* a(n,n) --> cholesky decomposition of hessian in
* lower triangular part and diagonal.
* hessian or approx in upper triangular part
* udiag(n) --> diagonal of hessian in a(.,.)
* p(n) --> newton step
* sx(n) --> diagonal scaling matrix for n
* rnwtln --> newton step length
* dlt <--> trust region radius
* amu <--> [retain value between successive calls]
* dltp --> trust region radius at last exit from this routine
* phi <--> [retain value between successive calls]
* phip0 <--> [retain value between successive calls]
* fstime <--> boolean. =.true. if first entry to this routine
* during k-th iteration
* sc(n) <-- current step
* nwtake <-- boolean, =.true. if newton step taken
* wrk0(n) --> workspace
* epsm --> machine epsilon
*/
int one = 1, job = 0, info;
int i, j;
double phip;
double amulo, amuup;
double addmax, stepln;
double temp1;
const double hi = 1.5, alo = 0.75;
/* hi and alo are constants used in this routine. */
/* change here if other values are to be substituted. */
/* shall we take newton step ? */
*nwtake = (rnwtln <= hi * *dlt);
if (*nwtake) { /* take newton step */
for (i = 0; i < n; ++i)
sc[i] = p[i];
*dlt = fmin2(*dlt, rnwtln);
*amu = 0.;
return;
}
/* else *nwtake = FALSE : newton step not taken */
if (*amu > 0.)
*amu -= (*phi + dltp) * (dltp - *dlt + *phi) / (*dlt * *phip0);
*phi = rnwtln - *dlt;
if (*fstime) {
for (i = 0; i < n; ++i)
wrk0[i] = sx[i] * sx[i] * p[i];
/* solve l*y = (sx**2)*p */
F77_CALL(dtrsl)(a, &nr, &n, wrk0, &job, &info);
/* Computing 2nd power */
temp1 = F77_CALL(dnrm2)(&n, wrk0, &one);
*phip0 = -(temp1 * temp1) / rnwtln;
*fstime = FALSE;
}
phip = *phip0;
amulo = -(*phi) / phip;
amuup = 0.;
for (i = 0; i < n; ++i)
amuup += g[i] * g[i] / (sx[i] * sx[i]);
amuup = sqrt(amuup) / *dlt;
while (1) {
/* test value of amu; generate next amu if necessary */
if (*amu < amulo || *amu > amuup) {
*amu = fmax2(sqrt(amulo * amuup), amuup * .001);
}
/* copy (h,udiag) to l */
/* where h <-- h+amu*(sx**2) [do not actually change (h,udiag)] */
for (i = 0; i < n; ++i) {
a[i + i * nr] = udiag[i] + *amu * sx[i] * sx[i];
for (j = 0; j < i; ++i)
a[i + j * nr] = a[j + i * nr];
}
/* factor h=l(l+) */
temp1 = sqrt(epsm);
choldc(nr, n, a, 0.0, temp1, &addmax);
/* solve h*p = l(l+)*sc = -g */