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hes_lagrangian.cpp |
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@(@\newcommand{\W}[1]{ \; #1 \; }
\newcommand{\R}[1]{ {\rm #1} }
\newcommand{\B}[1]{ {\bf #1} }
\newcommand{\D}[2]{ \frac{\partial #1}{\partial #2} }
\newcommand{\DD}[3]{ \frac{\partial^2 #1}{\partial #2 \partial #3} }
\newcommand{\Dpow}[2]{ \frac{\partial^{#1}}{\partial {#2}^{#1}} }
\newcommand{\dpow}[2]{ \frac{ {\rm d}^{#1}}{{\rm d}\, {#2}^{#1}} }@)@This is cppad-20221105 documentation. Here is a link to its
current documentation
.
Hessian of Lagrangian and ADFun Default Constructor: Example and Test
# include <cppad/cppad.hpp>
# include <cassert>
namespace {
CppAD::AD<double> Lagragian(
const CppAD::vector< CppAD::AD<double> > &xyz )
{ using CppAD::AD;
assert( xyz.size() == 6 );
AD<double> x0 = xyz[0];
AD<double> x1 = xyz[1];
AD<double> x2 = xyz[2];
AD<double> y0 = xyz[3];
AD<double> y1 = xyz[4];
AD<double> z = xyz[5];
// compute objective function
AD<double> f = x0 * x0;
// compute constraint functions
AD<double> g0 = 1. + 2.*x1 + 3.*x2;
AD<double> g1 = log( x0 * x2 );
// compute the Lagragian
AD<double> L = y0 * g0 + y1 * g1 + z * f;
return L;
}
CppAD::vector< CppAD::AD<double> > fg(
const CppAD::vector< CppAD::AD<double> > &x )
{ using CppAD::AD;
using CppAD::vector;
assert( x.size() == 3 );
vector< AD<double> > fg(3);
fg[0] = x[0] * x[0];
fg[1] = 1. + 2. * x[1] + 3. * x[2];
fg[2] = log( x[0] * x[2] );
return fg;
}
bool CheckHessian(
CppAD::vector<double> H ,
double x0, double x1, double x2, double y0, double y1, double z )
{ using CppAD::NearEqual;
double eps99 = 99.0 * std::numeric_limits<double>::epsilon();
bool ok = true;
size_t n = 3;
assert( H.size() == n * n );
/*
L = z*x0*x0 + y0*(1 + 2*x1 + 3*x2) + y1*log(x0*x2)
L_0 = 2 * z * x0 + y1 / x0
L_1 = y0 * 2
L_2 = y0 * 3 + y1 / x2
*/
// L_00 = 2 * z - y1 / ( x0 * x0 )
double check = 2. * z - y1 / (x0 * x0);
ok &= NearEqual(H[0 * n + 0], check, eps99, eps99);
// L_01 = L_10 = 0
ok &= NearEqual(H[0 * n + 1], 0., eps99, eps99);
ok &= NearEqual(H[1 * n + 0], 0., eps99, eps99);
// L_02 = L_20 = 0
ok &= NearEqual(H[0 * n + 2], 0., eps99, eps99);
ok &= NearEqual(H[2 * n + 0], 0., eps99, eps99);
// L_11 = 0
ok &= NearEqual(H[1 * n + 1], 0., eps99, eps99);
// L_12 = L_21 = 0
ok &= NearEqual(H[1 * n + 2], 0., eps99, eps99);
ok &= NearEqual(H[2 * n + 1], 0., eps99, eps99);
// L_22 = - y1 / (x2 * x2)
check = - y1 / (x2 * x2);
ok &= NearEqual(H[2 * n + 2], check, eps99, eps99);
return ok;
}
bool UseL()
{ using CppAD::AD;
using CppAD::vector;
// double values corresponding to x, y, and z vectors
double x0(.5), x1(1e3), x2(1), y0(2.), y1(3.), z(4.);
// domain space vector
size_t n = 3;
vector< AD<double> > a_x(n);
a_x[0] = x0;
a_x[1] = x1;
a_x[2] = x2;
// declare a_x as independent variable vector and start recording
CppAD::Independent(a_x);
// vector including x, y, and z
vector< AD<double> > a_xyz(n + 2 + 1);
a_xyz[0] = a_x[0];
a_xyz[1] = a_x[1];
a_xyz[2] = a_x[2];
a_xyz[3] = y0;
a_xyz[4] = y1;
a_xyz[5] = z;
// range space vector
size_t m = 1;
vector< AD<double> > a_L(m);
a_L[0] = Lagragian(a_xyz);
// create K: x -> L and stop tape recording.
// Use default ADFun construction for example purposes.
CppAD::ADFun<double> K;
K.Dependent(a_x, a_L);
// Operation sequence corresponding to K depends on
// value of y0, y1, and z. Must redo calculations above when
// y0, y1, or z changes.
// declare independent variable vector and Hessian
vector<double> x(n);
vector<double> H( n * n );
// point at which we are computing the Hessian
// (must redo calculations below each time x changes)
x[0] = x0;
x[1] = x1;
x[2] = x2;
H = K.Hessian(x, 0);
// check this Hessian calculation
return CheckHessian(H, x0, x1, x2, y0, y1, z);
}
bool Usefg()
{ using CppAD::AD;
using CppAD::vector;
// parameters defining problem
double x0(.5), x1(1e3), x2(1), y0(2.), y1(3.), z(4.);
// domain space vector
size_t n = 3;
vector< AD<double> > a_x(n);
a_x[0] = x0;
a_x[1] = x1;
a_x[2] = x2;
// declare a_x as independent variable vector and start recording
CppAD::Independent(a_x);
// range space vector
size_t m = 3;
vector< AD<double> > a_fg(m);
a_fg = fg(a_x);
// create K: x -> fg and stop tape recording
CppAD::ADFun<double> K;
K.Dependent(a_x, a_fg);
// Operation sequence corresponding to K does not depend on
// value of x0, x1, x2, y0, y1, or z.
// forward and reverse mode arguments and results
vector<double> x(n);
vector<double> H( n * n );
vector<double> dx(n);
vector<double> w(m);
vector<double> dw(2*n);
// compute Hessian at this value of x
// (must redo calculations below each time x changes)
x[0] = x0;
x[1] = x1;
x[2] = x2;
K.Forward(0, x);
// set weights to Lagrange multiplier values
// (must redo calculations below each time y0, y1, or z changes)
w[0] = z;
w[1] = y0;
w[2] = y1;
// initialize dx as zero
size_t i, j;
for(i = 0; i < n; i++)
dx[i] = 0.;
// loop over components of x
for(i = 0; i < n; i++)
{ dx[i] = 1.; // dx is i-th elementary vector
K.Forward(1, dx); // partial w.r.t dx
dw = K.Reverse(2, w); // deritavtive of partial
for(j = 0; j < n; j++)
H[ i * n + j ] = dw[ j * 2 + 1 ];
dx[i] = 0.; // dx is zero vector
}
// check this Hessian calculation
return CheckHessian(H, x0, x1, x2, y0, y1, z);
}
}
bool HesLagrangian(void)
{ bool ok = true;
// UseL is simpler, but must retape every time that y of z changes
ok &= UseL();
// Usefg does not need to retape unless operation sequence changes
ok &= Usefg();
return ok;
}
Input File: example/general/hes_lagrangian.cpp