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Commit e56be5fb authored by Elias Pipping's avatar Elias Pipping Committed by Elias Pipping
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Nuke obsolete files

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src/Makefile.am
+ 1
− 13
View file @ e56be5fb
......@@ -2,25 +2,13 @@
SUBDIRS =
noinst_PROGRAMS = \
bisection-example \
bisection-example-flexible \
bisection-simpler-example \
bisection-simpler-example2 \
bisection-simpler-example2-gradient
bisection-example-flexible
bisection_example_SOURCES = bisection-example.cc
bisection_example_flexible_SOURCES = \
bisection-example-flexible.cc \
properscalarincreasingconvexfunction.hh
bisection_simpler_example_SOURCES = bisection-simpler-example.cc
bisection_simpler_example2_SOURCES = bisection-simpler-example2.cc
bisection_simpler_example2_gradient_SOURCES = bisection-simpler-example2-gradient.cc
check-am:
./bisection-simpler-example
./bisection-simpler-example2
./bisection-simpler-example2-gradient
./bisection-example
./bisection-example-flexible
AM_CXXFLAGS = -Wall -Wextra
......
src/bisection-example.cc deleted 100644 → 0
+ 0
− 207
View file @ a1823d67
/* -*- mode:c++; mode: flymake -*- */
#ifdef HAVE_CONFIG_H
#include "config.h"
#endif
#include <dune/common/exceptions.hh>
#include <dune/common/stdstreams.hh>
#include <dune/fufem/interval.hh>
#include <dune/tnnmg/nonlinearities/smallfunctional.hh>
#include <dune/tnnmg/problem-classes/bisection.hh>
#include <cassert>
#include <cstdlib>
#include <limits>
template <int dimension> class SampleFunctional {
public:
typedef Dune::FieldVector<double, dimension> SmallVector;
typedef Dune::FieldMatrix<double, dimension, dimension> SmallMatrix;
// FIXME: hardcoded function H
SampleFunctional(SmallMatrix A, SmallVector b) : A_(A), b_(b) {}
double operator()(const SmallVector v) const {
SmallVector y;
A_.mv(v, y); // y = Av
y /= 2; // y = 1/2 Av
y -= b_; // y = 1/2 Av - b
return y * v + H(v.two_norm()); // <1/2 Av - b,v> + H(|v|)
}
// |dir|-times the directional derivative wrt dir/|dir|.
double pseudoDirectionalDerivative(const SmallVector x,
const SmallVector dir) const {
if (x == SmallVector(0.0))
return HPrimePlus(0) * dir.two_norm();
if (x * dir > 0)
return PlusGrad(x) * dir;
else
return MinusGrad(x) * dir;
}
double directionalDerivative(const SmallVector x,
const SmallVector dir) const {
double norm = dir.two_norm();
if (norm == 0)
DUNE_THROW(Dune::Exception, "Directional derivatives cannot be computed "
"w.r.t. the zero-direction.");
SmallVector tmp = dir;
tmp /= norm;
return pseudoDirectionalDerivative(x, tmp);
}
SmallVector minimise(const SmallVector x, unsigned int iterations) const {
SmallVector descDir = ModifiedGradient(x);
Dune::dverb << "Starting at x with J(x) = " << operator()(x) << std::endl;
Dune::dverb << "Minimizing in direction w with dJ(x,w) = "
<< directionalDerivative(x, descDir) << std::endl;
double l = 0;
double r = 1;
SmallVector tmp;
while (true) {
tmp = x;
tmp.axpy(r, descDir);
if (directionalDerivative(tmp, descDir) >= 0)
break;
l = r;
r *= 2;
Dune::dverb << "Widened interval!" << std::endl;
}
Dune::dverb << "Interval now [" << l << "," << r << "]" << std::endl;
// Debugging
{
SmallVector tmpl = x;
tmpl.axpy(l, descDir);
SmallVector tmpr = x;
tmpr.axpy(r, descDir);
assert(directionalDerivative(tmpl, descDir) < 0);
assert(directionalDerivative(tmpr, descDir) > 0);
}
double m = l / 2 + r / 2;
SmallVector middle;
for (unsigned int count = 0; count < iterations; ++count) {
Dune::dverb << "now at m = " << m << std::endl;
Dune::dverb << "Value of J here: " << operator()(x + middle) << std::endl;
middle = descDir;
middle *= m;
double derivative = directionalDerivative(x + middle, descDir);
if (derivative < 0) {
l = m;
m = (m + r) / 2;
} else if (derivative > 0) {
r = m;
m = (l + m) / 2;
} else
break;
}
return middle;
}
private:
SmallMatrix A_;
SmallVector b_;
double H(double s) const { return (s < 1) ? s : (2 * s - 1); }
double HPrimeMinus(double s) const { return (s <= 1) ? 1 : 2; }
double HPrimePlus(double s) const { return (s < 1) ? 1 : 2; }
// Gradient of the smooth part
SmallVector SmoothGrad(const SmallVector x) const {
SmallVector y;
A_.mv(x, y); // y = Av
y -= b_; // y = Av - b
return y;
}
SmallVector PlusGrad(const SmallVector x) const {
SmallVector y = SmoothGrad(x);
y.axpy(HPrimePlus(x.two_norm()) / x.two_norm(), x);
return y;
}
SmallVector MinusGrad(const SmallVector x) const {
SmallVector y = SmoothGrad(x);
y.axpy(HPrimeMinus(x.two_norm()) / x.two_norm(), x);
return y;
}
SmallVector ModifiedGradient(const SmallVector x) const {
if (x == SmallVector(0.0))
// TODO
DUNE_THROW(Dune::Exception, "The case x = 0 is not yet handled.");
SmallVector const pg = PlusGrad(x);
SmallVector const mg = MinusGrad(x);
SmallVector ret;
// TODO: collinearity checks suck
if (pg * x == pg.two_norm() * x.two_norm() &&
-(mg * x) == mg.two_norm() * x.two_norm()) {
return SmallVector(0);
} else if (pg * x >= 0 && mg * x >= 0) {
ret = pg;
} else if (pg * x <= 0 && mg * x <= 0) {
ret = mg;
} else {
ret = project(SmoothGrad(x), x);
}
ret *= -1;
return ret;
}
// No normalising is done!
SmallVector project(const SmallVector z, const SmallVector x) const {
SmallVector y = z;
y.axpy(-(z * x) / x.two_norm2(), x);
return y;
}
};
int main() {
try {
int const dim = 2;
typedef SampleFunctional<dim> SampleFunctional;
SampleFunctional::SmallMatrix A;
A[0][0] = 3;
A[0][1] = 0;
A[1][0] = 0;
A[1][1] = 3;
SampleFunctional::SmallVector b;
b[0] = 1;
b[1] = 2;
SampleFunctional J(A, b);
std::cout << J.directionalDerivative(b, b) << std::endl;
assert(J.pseudoDirectionalDerivative(b, b) == 10 + 2 * sqrt(5));
SampleFunctional::SmallVector start = b;
start *= 17;
SampleFunctional::SmallVector correction = J.minimise(start, 20);
assert(J(start + correction) <= J(start));
assert(std::abs(J(start + correction) + 0.254644) < 1e-8);
std::cout << J(start + correction) << std::endl;
return 0;
}
catch (Dune::Exception &e) {
Dune::derr << "Dune reported error: " << e << std::endl;
}
}
src/bisection-simpler-example.cc deleted 100644 → 0
+ 0
− 58
View file @ a1823d67
/* -*- mode:c++; mode: flymake -*- */
#ifdef HAVE_CONFIG_H
#include "config.h"
#endif
#include <dune/fufem/interval.hh>
#include <dune/tnnmg/nonlinearities/smallfunctional.hh>
#include <dune/tnnmg/problem-classes/bisection.hh>
#include <cassert>
#include <limits>
class SampleFunctional : public SmallFunctional<2> {
public:
SampleFunctional(SmallMatrix A, SmallVector b) : A_(A), b_(b) {}
double operator()(const SmallVector v) const {
SmallVector y;
A_.mv(v, y); // y = Av
y /= 2; // y = 1/2 Av
y -= b_; // y = 1/2 Av - b
return y * v; // <1/2 Av - b,v>
}
SmallVector d(const SmallVector v) const {
SmallVector y;
A_.mv(v, y); // y = Av
y -= b_; // y = Av - b
return y;
}
SmallMatrix d2(const SmallVector) const { return A_; }
private:
SmallMatrix A_;
SmallVector b_;
};
int main() {
SampleFunctional::SmallMatrix A;
A[0][0] = 3;
A[0][1] = 0;
A[1][0] = 0;
A[1][1] = 3;
SampleFunctional::SmallVector b;
b[0] = 1;
b[1] = 2;
SampleFunctional J(A, b);
assert(J(b) == 2.5);
assert(b.two_norm() == sqrt(5));
assert(b.two_norm2() == 5);
return 0;
}
src/bisection-simpler-example2-gradient.cc deleted 100644 → 0
+ 0
− 135
View file @ a1823d67
/* -*- mode:c++; mode: flymake -*- */
#ifdef HAVE_CONFIG_H
#include "config.h"
#endif
#include <dune/common/stdstreams.hh>
#include <dune/fufem/interval.hh>
#include <dune/tnnmg/nonlinearities/smallfunctional.hh>
#include <dune/tnnmg/problem-classes/bisection.hh>
#include <cassert>
#include <cstdlib>
#include <limits>
#include <iostream>
template <int dimension>
class SampleFunctional : public SmallFunctional<dimension> {
public:
typedef typename SmallFunctional<dimension>::SmallMatrix SmallMatrix;
typedef typename SmallFunctional<dimension>::SmallVector SmallVector;
SampleFunctional(SmallMatrix A, SmallVector b) : A_(A), b_(b) {}
double operator()(const SmallVector v) const {
SmallVector y;
A_.mv(v, y); // y = Av
y /= 2; // y = 1/2 Av
y -= b_; // y = 1/2 Av - b
return y * v; // <1/2 Av - b,v>
}
SmallVector d(const SmallVector v) const {
SmallVector y;
A_.mv(v, y); // y = Av
y -= b_; // y = Av - b
return y;
}
SmallMatrix d2(const SmallVector) const { return A_; }
// extending the interface here
double directionalDerivative(const SmallVector x,
const SmallVector dir) const {
return d(x) * dir;
}
SmallVector minimise(const SmallVector x, unsigned int iterations) const {
SmallVector descDir = d(x);
descDir *= -1; // The negative gradient
Dune::dverb << "Starting at x with J(x) = " << operator()(x) << std::endl;
Dune::dverb << "Minimizing in direction w with dJ(x,w) = "
<< directionalDerivative(x, descDir) << std::endl;
double l = 0;
double r = 1;
SmallVector tmp;
while (true) {
tmp = x;
tmp.axpy(r, descDir);
if (directionalDerivative(tmp, descDir) >= 0)
break;
l = r;
r *= 2;
Dune::dverb << "Widened interval!" << std::endl;
}
Dune::dverb << "Interval now [" << l << "," << r << "]" << std::endl;
// Debugging
{
SmallVector tmpl = x;
tmpl.axpy(l, descDir);
SmallVector tmpr = x;
tmpr.axpy(r, descDir);
assert(directionalDerivative(tmpl, descDir) < 0);
assert(directionalDerivative(tmpr, descDir) > 0);
}
double m = l / 2 + r / 2;
SmallVector middle;
for (unsigned int count = 0; count < iterations; ++count) {
Dune::dverb << "now at m = " << m << std::endl;
Dune::dverb << "Value of J here: " << operator()(x + middle) << std::endl;
middle = descDir;
middle *= m;
double derivative = directionalDerivative(x + middle, descDir);
if (derivative < 0) {
l = m;
m = (m + r) / 2;
} else if (derivative > 0) {
r = m;
m = (l + m) / 2;
} else
break;
}
return middle;
}
private:
SmallMatrix A_;
SmallVector b_;
};
int main() {
int const dim = 2;
typedef SampleFunctional<dim> SampleFunctional;
SampleFunctional::SmallMatrix A;
A[0][0] = 3;
A[0][1] = 0;
A[1][0] = 0;
A[1][1] = 3;
SampleFunctional::SmallVector b;
b[0] = 1;
b[1] = 2;
SampleFunctional J(A, b);
SampleFunctional::SmallVector start = b;
start *= 17;
SampleFunctional::SmallVector correction = J.minimise(start, 20);
assert(J(start + correction) <= J(start));
assert(std::abs(J(start + correction) + 0.833333) < 1e-6);
std::cout << J(start + correction) << std::endl;
return 0;
}
src/bisection-simpler-example2.cc deleted 100644 → 0
+ 0
− 64
View file @ a1823d67
/* -*- mode:c++; mode: flymake -*- */
#ifdef HAVE_CONFIG_H
#include "config.h"
#endif
#include <dune/fufem/interval.hh>
#include <dune/tnnmg/nonlinearities/smallfunctional.hh>
#include <dune/tnnmg/problem-classes/bisection.hh>
#include <cassert>
#include <limits>
template <int dimension>
class SampleFunctional : public SmallFunctional<dimension> {
public:
typedef typename SmallFunctional<dimension>::SmallMatrix SmallMatrix;
typedef typename SmallFunctional<dimension>::SmallVector SmallVector;
SampleFunctional(SmallMatrix A, SmallVector b) : A_(A), b_(b) {}
double operator()(const SmallVector v) const {
SmallVector y;
A_.mv(v, y); // y = Av
y /= 2; // y = 1/2 Av
y -= b_; // y = 1/2 Av - b
return y * v; // <1/2 Av - b,v>
}
SmallVector d(const SmallVector v) const {
SmallVector y;
A_.mv(v, y); // y = Av
y -= b_; // y = Av - b
return y;
}
SmallMatrix d2(const SmallVector) const { return A_; }
private:
SmallMatrix A_;
SmallVector b_;
};
int main() {
int const dim = 2;
SampleFunctional<dim>::SmallMatrix A;
A[0][0] = 3;
A[0][1] = 0;
A[1][0] = 0;
A[1][1] = 3;
SampleFunctional<dim>::SmallVector b;
b[0] = 1;
b[1] = 2;
SampleFunctional<dim> J(A, b);
assert(J(b) == 2.5);
assert(b.two_norm() == sqrt(5));
assert(b.two_norm2() == 5);
return 0;
}
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