move opti into linalg

This commit is contained in:
Joachim Schoeberl 2009-01-25 19:11:56 +00:00
parent f4a0588d59
commit 93c63fc531
10 changed files with 5 additions and 1478 deletions

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@ -2,5 +2,5 @@ AM_CPPFLAGS =
METASOURCES = AUTO METASOURCES = AUTO
SUBDIRS = csg general geom2d gprim include interface linalg meshing occ opti \ SUBDIRS = csg general geom2d gprim include interface linalg meshing occ \
parallel stlgeom visualization parallel stlgeom visualization

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@ -216,7 +216,7 @@ top_builddir = @top_builddir@
top_srcdir = @top_srcdir@ top_srcdir = @top_srcdir@
AM_CPPFLAGS = AM_CPPFLAGS =
METASOURCES = AUTO METASOURCES = AUTO
SUBDIRS = csg general geom2d gprim include interface linalg meshing occ opti \ SUBDIRS = csg general geom2d gprim include interface linalg meshing occ \
parallel stlgeom visualization parallel stlgeom visualization
all: all-recursive all: all-recursive

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@ -1 +1 @@
#include "../opti/opti.hpp" #include "../linalg/opti.hpp"

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@ -1,7 +1,7 @@
noinst_HEADERS = densemat.hpp linalg.hpp polynomial.hpp vector.hpp noinst_HEADERS = densemat.hpp linalg.hpp polynomial.hpp vector.hpp opti.hpp
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libla_la_SOURCES = densemat.cpp polynomial.cpp vector.cpp libla_la_SOURCES = densemat.cpp polynomial.cpp vector.cpp bfgs.cpp linopt.cpp linsearch.cpp
libla_la_LDFLAGS = -rdynamic libla_la_LDFLAGS = -rdynamic

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@ -1,6 +0,0 @@
noinst_HEADERS = opti.hpp
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METASOURCES = AUTO
noinst_LTLIBRARIES = libopti.la
libopti_la_SOURCES = bfgs.cpp linopt.cpp linsearch.cpp

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@ -1,491 +0,0 @@
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/***************************************************************************/
/* */
/* Vorlesung Optimierung I, Gfrerer, WS94/95 */
/* BFGS-Verfahren zur Lösung freier nichtlinearer Optimierungsprobleme */
/* */
/* Programmautor: Joachim Schöberl */
/* Matrikelnummer: 9155284 */
/* */
/***************************************************************************/
#include <mystdlib.h>
#include <myadt.hpp>
#include <linalg.hpp>
#include "opti.hpp"
namespace netgen
{
void Cholesky (const DenseMatrix & a,
DenseMatrix & l, Vector & d)
{
// Factors A = L D L^T
double x;
int i, j, k;
int n = a.Height();
// (*testout) << "a = " << a << endl;
l = a;
for (i = 1; i <= n; i++)
{
for (j = i; j <= n; j++)
{
x = l.Get(i, j);
for (k = 1; k < i; k++)
x -= l.Get(i, k) * l.Get(j, k) * d.Get(k);
if (i == j)
{
d.Elem(i) = x;
}
else
{
l.Elem(j, i) = x / d.Get(k);
}
}
}
for (i = 1; i <= n; i++)
{
l.Elem(i, i) = 1;
for (j = i+1; j <= n; j++)
l.Elem(i, j) = 0;
}
/*
// Multiply:
(*testout) << "multiplied factors: " << endl;
for (i = 1; i <= n; i++)
for (j = 1; j <= n; j++)
{
x = 0;
for (k = 1; k <= n; k++)
x += l.Get(i, k) * l.Get(j, k) * d.Get(k);
(*testout) << x << " ";
}
(*testout) << endl;
*/
}
void MultLDLt (const DenseMatrix & l, const Vector & d, const Vector & g, Vector & p)
{
/*
int i, j, n;
double val;
n = l.Height();
p = g;
for (i = 1; i <= n; i++)
{
val = 0;
for (j = i; j <= n; j++)
val += p.Get(j) * l.Get(j, i);
p.Set(i, val);
}
for (i = 1; i <= n; i++)
p.Elem(i) *= d.Get(i);
for (i = n; i >= 1; i--)
{
val = 0;
for (j = 1; j <= i; j++)
val += p.Get(j) * l.Get(i, j);
p.Set(i, val);
}
*/
double val;
int n = l.Height();
p = g;
for (int i = 0; i < n; i++)
{
val = 0;
for (int j = i; j < n; j++)
val += p(j) * l(j, i);
p(i) = val;
}
for (int i = 0; i < n; i++)
p(i) *= d(i);
for (int i = n-1; i >= 0; i--)
{
val = 0;
for (int j = 0; j <= i; j++)
val += p(j) * l(i, j);
p(i) = val;
}
}
void SolveLDLt (const DenseMatrix & l, const Vector & d, const Vector & g, Vector & p)
{
double val;
int n = l.Height();
p = g;
for (int i = 0; i < n; i++)
{
val = 0;
for (int j = 0; j < i; j++)
val += p(j) * l(i,j);
p(i) -= val;
}
for (int i = 0; i < n; i++)
p(i) /= d(i);
for (int i = n-1; i >= 0; i--)
{
val = 0;
for (int j = i+1; j < n; j++)
val += p(j) * l(j, i);
p(i) -= val;
}
}
int LDLtUpdate (DenseMatrix & l, Vector & d, double a, const Vector & u)
{
// Bemerkung: Es wird a aus R erlaubt
// Rueckgabewert: 0 .. D bleibt positiv definit
// 1 .. sonst
int i, j, n;
n = l.Height();
Vector v(n);
double t, told, xi;
told = 1;
v = u;
for (j = 1; j <= n; j++)
{
t = told + a * sqr (v.Elem(j)) / d.Get(j);
if (t <= 0)
{
(*testout) << "update err, t = " << t << endl;
return 1;
}
xi = a * v.Elem(j) / (d.Get(j) * t);
d.Elem(j) *= t / told;
for (i = j + 1; i <= n; i++)
{
v.Elem(i) -= v.Elem(j) * l.Elem(i, j);
l.Elem(i, j) += xi * v.Elem(i);
}
told = t;
}
return 0;
}
double BFGS (
Vector & x, // i: Startwert
// o: Loesung, falls IFAIL = 0
const MinFunction & fun,
const OptiParameters & par,
double eps
)
{
int i, j, n = x.Size();
long it;
char a1crit, a3acrit;
Vector d(n), g(n), p(n), temp(n), bs(n), xneu(n), y(n), s(n), x0(n);
DenseMatrix l(n);
DenseMatrix hesse(n);
double /* normg, */ alphahat, hd, fold;
double a1, a2;
const double mu1 = 0.1, sigma = 0.1, xi1 = 1, xi2 = 10;
const double tau = 0.1, tau1 = 0.1, tau2 = 0.6;
Vector typx(x.Size()); // i: typische Groessenordnung der Komponenten
double f, f0;
double typf; // i: typische Groessenordnung der Loesung
double fmin = -1e5; // i: untere Schranke fuer Funktionswert
// double eps = 1e-8; // i: Abbruchschranke fuer relativen Gradienten
double tauf = 0.1; // i: Abbruchschranke fuer die relative Aenderung der
// Funktionswerte
int ifail; // o: 0 .. Erfolg
// -1 .. Unterschreitung von fmin
// 1 .. kein Erfolg bei Liniensuche
// 2 .. Überschreitung von itmax
typx = par.typx;
typf = par.typf;
l = 0;
for (i = 1; i <= n; i++)
l.Elem(i, i) = 1;
f = fun.FuncGrad (x, g);
f0 = f;
x0 = x;
it = 0;
do
{
// Restart
if (it % (5 * n) == 0)
{
for (i = 1; i <= n; i++)
d.Elem(i) = typf/ sqr (typx.Get(i)); // 1;
for (i = 2; i <= n; i++)
for (j = 1; j < i; j++)
l.Elem(i, j) = 0;
/*
hesse = 0;
for (i = 1; i <= n; i++)
hesse.Elem(i, i) = typf / sqr (typx.Get(i));
fun.ApproximateHesse (x, hesse);
Cholesky (hesse, l, d);
*/
}
it++;
if (it > par.maxit_bfgs)
{
ifail = 2;
break;
}
// Solve with factorized B
SolveLDLt (l, d, g, p);
// (*testout) << "l " << l << endl
// << "d " << d << endl
// << "g " << g << endl
// << "p " << p << endl;
p *= -1;
y = g;
fold = f;
// line search
alphahat = 1;
lines (x, xneu, p, f, g, fun, par, alphahat, fmin,
mu1, sigma, xi1, xi2, tau, tau1, tau2, ifail);
if(ifail == 1)
(*testout) << "no success with linesearch" << endl;
/*
// if (it > par.maxit_bfgs/2)
{
(*testout) << "x = " << x << endl;
(*testout) << "xneu = " << xneu << endl;
(*testout) << "f = " << f << endl;
(*testout) << "g = " << g << endl;
}
*/
// (*testout) << "it = " << it << " f = " << f << endl;
// if (ifail != 0) break;
s.Set2 (1, xneu, -1, x);
y *= -1;
y.Add (1,g); // y += g;
x = xneu;
// BFGS Update
MultLDLt (l, d, s, bs);
a1 = y * s;
a2 = s * bs;
if (a1 > 0 && a2 > 0)
{
if (LDLtUpdate (l, d, 1 / a1, y) != 0)
{
cerr << "BFGS update error1" << endl;
(*testout) << "BFGS update error1" << endl;
(*testout) << "l " << endl << l << endl
<< "d " << d << endl;
ifail = 1;
break;
}
if (LDLtUpdate (l, d, -1 / a2, bs) != 0)
{
cerr << "BFGS update error2" << endl;
(*testout) << "BFGS update error2" << endl;
(*testout) << "l " << endl << l << endl
<< "d " << d << endl;
ifail = 1;
break;
}
}
// Calculate stop conditions
hd = eps * max2 (typf, fabs (f));
a1crit = 1;
for (i = 1; i <= n; i++)
if ( fabs (g.Elem(i)) * max2 (typx.Elem(i), fabs (x.Elem(i))) > hd)
a1crit = 0;
a3acrit = (fold - f <= tauf * max2 (typf, fabs (f)));
// testout << "g = " << g << endl;
// testout << "a1crit, a3crit = " << int(a1crit) << ", " << int(a3acrit) << endl;
/*
// Output for tests
normg = sqrt (g * g);
testout << "it =" << setw (5) << it
<< " f =" << setw (12) << setprecision (5) << f
<< " |g| =" << setw (12) << setprecision (5) << normg;
testout << " x = (" << setw (12) << setprecision (5) << x.Elem(1);
for (i = 2; i <= n; i++)
testout << "," << setw (12) << setprecision (5) << x.Elem(i);
testout << ")" << endl;
*/
//(*testout) << "it = " << it << " f = " << f << " x = " << x << endl
// << " g = " << g << " p = " << p << endl << endl;
// (*testout) << "|g| = " << g.L2Norm() << endl;
if (g.L2Norm() < fun.GradStopping (x)) break;
}
while (!a1crit || !a3acrit);
/*
(*testout) << "it = " << it << " g = " << g << " f = " << f
<< " fail = " << ifail << endl;
*/
if (f0 < f || (ifail == 1))
{
(*testout) << "fail, f = " << f << " f0 = " << f0 << endl;
f = f0;
x = x0;
}
// (*testout) << "x = " << x << ", x0 = " << x0 << endl;
return f;
}
}

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@ -1,73 +0,0 @@
#include <mystdlib.h>
#include <myadt.hpp>
#include <linalg.hpp>
#include "opti.hpp"
namespace netgen
{
void LinearOptimize (const DenseMatrix & a, const Vector & b,
const Vector & c, Vector & x)
{
int i1, i2, i3, j;
DenseMatrix m(3), inv(3);
Vector rs(3), hx(3), res(a.Height()), res2(3);
double f, fmin;
int nrest;
if (a.Width() != 3)
{
cerr << "LinearOptimize only implemented for 3 unknowns" << endl;
return;
}
fmin = 1e10;
x = 0;
nrest = a.Height();
for (i1 = 1; i1 <= nrest; i1++)
for (i2 = i1 + 1; i2 <= nrest; i2++)
for (i3 = i2 + 1; i3 <= nrest; i3++)
{
for (j = 1; j <= 3; j++)
{
m.Elem(1, j) = a.Get(i1, j);
m.Elem(2, j) = a.Get(i2, j);
m.Elem(3, j) = a.Get(i3, j);
}
rs.Elem(1) = b.Get(i1);
rs.Elem(2) = b.Get(i2);
rs.Elem(3) = b.Get(i3);
if (fabs (m.Det()) < 1e-12) continue;
CalcInverse (m, inv);
inv.Mult (rs, hx);
a.Residuum (hx, b, res);
// m.Residuum (hx, rs, res2);
f = c * hx;
/*
testout -> precision(12);
(*testout) << "i = (" << i1 << "," << i2 << "," << i3
<< "), f = " << f << " x = " << x << " res = " << res
<< " resmin = " << res.Min()
<< " res2 = " << res2 << " prod = " << prod << endl;
*/
double rmin = res.Elem(1);
for (int hi = 2; hi <= res.Size(); hi++)
if (res.Elem(hi) < rmin) rmin = res.Elem(hi);
if ( (f < fmin) && rmin >= -1e-8)
{
fmin = f;
x = hx;
}
}
}
}

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@ -1,351 +0,0 @@
/***************************************************************************/
/* */
/* Problem: Liniensuche */
/* */
/* Programmautor: Joachim Schöberl */
/* Matrikelnummer: 9155284 */
/* */
/* Algorithmus nach: */
/* */
/* Optimierung I, Gfrerer, WS94/95 */
/* Algorithmus 2.1: Liniensuche Problem (ii) */
/* */
/***************************************************************************/
#include <mystdlib.h>
#include <myadt.hpp> // min, max, sqr
#include <linalg.hpp>
#include "opti.hpp"
namespace netgen
{
const double eps0 = 1E-15;
// Liniensuche
double MinFunction :: Func (const Vector & /* x */) const
{
cerr << "Func of MinFunction called" << endl;
return 0;
}
void MinFunction :: Grad (const Vector & /* x */, Vector & /* g */) const
{
cerr << "Grad of MinFunction called" << endl;
}
double MinFunction :: FuncGrad (const Vector & x, Vector & g) const
{
cerr << "Grad of MinFunction called" << endl;
return 0;
/*
int n = x.Size();
static Vector xr;
static Vector xl;
xr.SetSize(n);
xl.SetSize(n);
double eps = 1e-6;
double fl, fr;
for (int i = 1; i <= n; i++)
{
xr.Set (1, x);
xl.Set (1, x);
xr.Elem(i) += eps;
fr = Func (xr);
xl.Elem(i) -= eps;
fl = Func (xl);
g.Elem(i) = (fr - fl) / (2 * eps);
}
double f = Func(x);
// (*testout) << "f = " << f << " grad = " << g << endl;
return f;
*/
}
double MinFunction :: FuncDeriv (const Vector & x, const Vector & dir, double & deriv) const
{
Vector g(x.Size());
double f = FuncGrad (x, g);
deriv = (g * dir);
// (*testout) << "g = " << g << ", dir = " << dir << ", deriv = " << deriv << endl;
return f;
}
void MinFunction :: ApproximateHesse (const Vector & x,
DenseMatrix & hesse) const
{
int n = x.Size();
int i, j;
static Vector hx;
hx.SetSize(n);
double eps = 1e-6;
double f, f11, f12, f21, f22;
for (i = 1; i <= n; i++)
{
for (j = 1; j < i; j++)
{
hx = x;
hx.Elem(i) = x.Get(i) + eps;
hx.Elem(j) = x.Get(j) + eps;
f11 = Func(hx);
hx.Elem(i) = x.Get(i) + eps;
hx.Elem(j) = x.Get(j) - eps;
f12 = Func(hx);
hx.Elem(i) = x.Get(i) - eps;
hx.Elem(j) = x.Get(j) + eps;
f21 = Func(hx);
hx.Elem(i) = x.Get(i) - eps;
hx.Elem(j) = x.Get(j) - eps;
f22 = Func(hx);
hesse.Elem(i, j) = hesse.Elem(j, i) =
(f11 + f22 - f12 - f21) / (2 * eps * eps);
}
hx = x;
f = Func(x);
hx.Elem(i) = x.Get(i) + eps;
f11 = Func(hx);
hx.Elem(i) = x.Get(i) - eps;
f22 = Func(hx);
hesse.Elem(i, i) = (f11 + f22 - 2 * f) / (eps * eps);
}
// (*testout) << "hesse = " << hesse << endl;
}
/// Line search, modified Mangasarien conditions
void lines (Vector & x, // i: initial point of line-search
Vector & xneu, // o: solution, if successful
Vector & p, // i: search direction
double & f, // i: function-value at x
// o: function-value at xneu, iff ifail = 0
Vector & g, // i: gradient at x
// o: gradient at xneu, iff ifail = 0
const MinFunction & fun, // function to minimize
const OptiParameters & par,
double & alphahat, // i: initial value for alpha_hat
// o: solution alpha iff ifail = 0
double fmin, // i: lower bound for f
double mu1, // i: Parameter mu_1 of Alg.2.1
double sigma, // i: Parameter sigma of Alg.2.1
double xi1, // i: Parameter xi_1 of Alg.2.1
double xi2, // i: Parameter xi_1 of Alg.2.1
double tau, // i: Parameter tau of Alg.2.1
double tau1, // i: Parameter tau_1 of Alg.2.1
double tau2, // i: Parameter tau_2 of Alg.2.1
int & ifail) // o: 0 on success
// -1 bei termination because lower limit fmin
// 1 bei illegal termination due to different reasons
{
double phi0, phi0prime, phi1, phi1prime, phihatprime;
double alpha1, alpha2, alphaincr, c;
char flag = 1;
long it;
alpha1 = 0;
alpha2 = 1e50;
phi0 = phi1 = f;
phi0prime = g * p;
if (phi0prime > 0)
{
ifail = 1;
return;
}
ifail = 1; // Markus
phi1prime = phi0prime;
// (*testout) << "phi0prime = " << phi0prime << endl;
// it = 100000l;
it = 0;
while (it++ <= par.maxit_linsearch)
{
xneu.Set2 (1, x, alphahat, p);
// f = fun.FuncGrad (xneu, g);
// f = fun.Func (xneu);
f = fun.FuncDeriv (xneu, p, phihatprime);
// (*testout) << "lines, f = " << f << " phip = " << phihatprime << endl;
if (f < fmin)
{
ifail = -1;
break;
}
if (alpha2 - alpha1 < eps0 * alpha2)
{
ifail = 0;
break;
}
// (*testout) << "i = " << it << " al = " << alphahat << " f = " << f << " fprime " << phihatprime << endl;;
if (f - phi0 > mu1 * alphahat * phi1prime + eps0 * fabs (phi0))
{
flag = 0;
alpha2 = alphahat;
c =
(f - phi1 - phi1prime * (alphahat-alpha1)) /
sqr (alphahat-alpha1);
alphahat = alpha1 - 0.5 * phi1prime / c;
if (alphahat > alpha2)
alphahat = alpha1 + 1/(4*c) *
( (sigma+mu1) * phi0prime - 2*phi1prime
+ sqrt (sqr(phi1prime - mu1 * phi0prime) -
4 * (phi1 - phi0 - mu1 * alpha1 * phi0prime) * c));
alphahat = max2 (alphahat, alpha1 + tau * (alpha2 - alpha1));
alphahat = min2 (alphahat, alpha2 - tau * (alpha2 - alpha1));
// (*testout) << " if-branch" << endl;
}
else
{
/*
f = fun.FuncGrad (xneu, g);
phihatprime = g * p;
*/
f = fun.FuncDeriv (xneu, p, phihatprime);
if (phihatprime < sigma * phi0prime * (1 + eps0))
{
if (phi1prime < phihatprime)
// Approximationsfunktion ist konvex
alphaincr = (alphahat - alpha1) * phihatprime /
(phi1prime - phihatprime);
else
alphaincr = 1e99; // MAXDOUBLE;
if (flag)
{
alphaincr = max2 (alphaincr, xi1 * (alphahat-alpha1));
alphaincr = min2 (alphaincr, xi2 * (alphahat-alpha1));
}
else
{
alphaincr = max2 (alphaincr, tau1 * (alpha2 - alphahat));
alphaincr = min2 (alphaincr, tau2 * (alpha2 - alphahat));
}
alpha1 = alphahat;
alphahat += alphaincr;
phi1 = f;
phi1prime = phihatprime;
}
else
{
ifail = 0; // Erfolg !!
break;
}
// (*testout) << " else, " << endl;
}
}
// (*testout) << "linsearch: it = " << it << " ifail = " << ifail << endl;
fun.FuncGrad (xneu, g);
if (it < 0)
ifail = 1;
// (*testout) << "fail = " << ifail << endl;
}
void SteepestDescent (Vector & x, const MinFunction & fun,
const OptiParameters & par)
{
int it, n = x.Size();
Vector xnew(n), p(n), g(n), g2(n);
double val, alphahat;
int fail;
val = fun.FuncGrad(x, g);
alphahat = 1;
// testout << "f = ";
for (it = 0; it < 10; it++)
{
// testout << val << " ";
// p = -g;
p.Set (-1, g);
lines (x, xnew, p, val, g, fun, par, alphahat, -1e5,
0.1, 0.1, 1, 10, 0.1, 0.1, 0.6, fail);
x = xnew;
}
// testout << endl;
}
}

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#ifndef FILE_OPTI
#define FILE_OPTI
/**************************************************************************/
/* File: opti.hpp */
/* Author: Joachim Schoeberl */
/* Date: 01. Jun. 95 */
/**************************************************************************/
namespace netgen
{
/**
Function to be minimized.
*/
class MinFunction
{
public:
///
virtual double Func (const Vector & x) const;
///
virtual void Grad (const Vector & x, Vector & g) const;
/// function and gradient
virtual double FuncGrad (const Vector & x, Vector & g) const;
/// directional derivative
virtual double FuncDeriv (const Vector & x, const Vector & dir, double & deriv) const;
/// if |g| < gradaccuray, then stop bfgs
virtual double GradStopping (const Vector & /* x */) const { return 0; }
///
virtual void ApproximateHesse (const Vector & /* x */,
DenseMatrix & /* hesse */) const;
};
class OptiParameters
{
public:
int maxit_linsearch;
int maxit_bfgs;
double typf;
double typx;
OptiParameters ()
{
maxit_linsearch = 100;
maxit_bfgs = 100;
typf = 1;
typx = 1;
}
};
/** Implementation of BFGS method.
Efficient method for non-linear minimiztion problems.
@param x initial value and solution
@param fun function to be minimized
*/
extern double BFGS (Vector & x, const MinFunction & fun,
const OptiParameters & par,
double eps = 1e-8);
/** Steepest descent method.
Simple method for non-linear minimization problems.
@param x initial value and solution
@param fun function to be minimized
*/
void SteepestDescent (Vector & x, const MinFunction & fun,
const OptiParameters & par);
extern void lines (
Vector & x, // i: Ausgangspunkt der Liniensuche
Vector & xneu, // o: Loesung der Liniensuche bei Erfolg
Vector & p, // i: Suchrichtung
double & f, // i: Funktionswert an der Stelle x
// o: Funktionswert an der Stelle xneu, falls ifail = 0
Vector & g, // i: Gradient an der Stelle x
// o: Gradient an der Stelle xneu, falls ifail = 0
const MinFunction & fun, // function to minmize
const OptiParameters & par, // parameters
double & alphahat, // i: Startwert für alpha_hat
// o: Loesung falls ifail = 0
double fmin, // i: untere Schranke für f
double mu1, // i: Parameter mu_1 aus Alg.2.1
double sigma, // i: Parameter sigma aus Alg.2.1
double xi1, // i: Parameter xi_1 aus Alg.2.1
double xi2, // i: Parameter xi_1 aus Alg.2.1
double tau, // i: Parameter tau aus Alg.2.1
double tau1, // i: Parameter tau_1 aus Alg.2.1
double tau2, // i: Parameter tau_2 aus Alg.2.1
int & ifail); // o: 0 bei erfolgreicher Liniensuche
// -1 bei Abbruch wegen Unterschreiten von fmin
// 1 bei Abbruch, aus sonstigen Gründen
/**
Solver for linear programming problem.
\begin{verbatim}
min c^t x
A x <= b
\end{verbatim}
*/
extern void LinearOptimize (const DenseMatrix & a, const Vector & b,
const Vector & c, Vector & x);
#ifdef NONE
/**
Simple projection iteration.
find $u = argmin_{v >= 0} 0.5 u A u - f u$
*/
extern void ApproxProject (const BaseMatrix & a, Vector & u,
const Vector & f,
double tau, int its);
/**
CG Algorithm for quadratic programming problem.
See: Dostal ...
d ... diag(A) ^{-1}
*/
extern void ApproxProjectCG (const BaseMatrix & a, Vector & x,
const Vector & b, const class DiagMatrix & d,
double gamma, int & steps, int & changes);
#endif
}
#endif