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/* -------------------------------------------------------------------------- *
* Simbody(tm): SimTKmath *
* -------------------------------------------------------------------------- *
* This is part of the SimTK biosimulation toolkit originating from *
* Simbios, the NIH National Center for Physics-Based Simulation of *
* Biological Structures at Stanford, funded under the NIH Roadmap for *
* Medical Research, grant U54 GM072970. See https://simtk.org/home/simbody. *
* *
* Portions copyright (c) 2006-12 Stanford University and the Authors. *
* Authors: Jack Middleton *
* Contributors: *
* *
* Licensed under the Apache License, Version 2.0 (the "License"); you may *
* not use this file except in compliance with the License. You may obtain a *
* copy of the License at http://www.apache.org/licenses/LICENSE-2.0. *
* *
* Unless required by applicable law or agreed to in writing, software *
* distributed under the License is distributed on an "AS IS" BASIS, *
* WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. *
* See the License for the specific language governing permissions and *
* limitations under the License. *
* -------------------------------------------------------------------------- */
#include "SimTKmath.h"
#include <iostream>
using std::cout;
using std::endl;
using SimTK::Vector;
using SimTK::Real;
using SimTK::Optimizer;
using SimTK::OptimizerSystem;
const static int NUMBER_OF_PARAMETERS = 25;
class ProblemSystem : public OptimizerSystem {
public:
ProblemSystem( const int numParameters ) : OptimizerSystem( numParameters ) {}
int objectiveFunc( const Vector &coefficients, bool new_coefficients, Real& f ) const override {
int i;
const Real *x = &coefficients[0];
f = .25 *(x[0]-1.0)*(x[0]-1.0);
for(i=1;i<getNumParameters();i++) {
f = f + pow(x[i]-x[i-1]*x[i-1], 2.0);
}
f = 4.0* f;
return( 0 );
}
int gradientFunc( const Vector &coefficients, bool new_coefficients, Vector &gradient ) const override {
const Real *x;
Real t1,t2;
int i;
x = &coefficients[0];
t1 = x[1]-(x[0]*x[0]);
gradient[0] = 2.0*(x[0]-1.0)-16.0*x[0]*t1;
for(i=1;i<getNumParameters()-1;i++) {
t2=t1;
t1=x[i+1]-(x[i]*x[i]);
gradient[i]=8.0*t2-16.0*x[i]*t1;
}
gradient[getNumParameters()-1]=8.0*t1;
return(0);
}
};
static bool equalToTol(Real v1, Real v2, Real tol) {
const Real scale = std::max(std::max(std::abs(v1), std::abs(v2)), Real(1));
return std::abs(v1-v2) < scale*tol;
}
/* adapted from driver1.f of Lbfgsb.2.1.tar.gz */
int main() {
Real f;
int i;
int n = NUMBER_OF_PARAMETERS;
Vector results(NUMBER_OF_PARAMETERS);
Vector lower_bounds(NUMBER_OF_PARAMETERS);
Vector upper_bounds(NUMBER_OF_PARAMETERS);
ProblemSystem sys(NUMBER_OF_PARAMETERS);
/* set initial conditions */
for(i=0;i<n;i++) {
results[i] = 3.0;
}
/* set bounds */
for(i=0;i<n;i=i+2) { // even numbered
lower_bounds[i] = 1.0;
upper_bounds[i] = 100.0;
}
for(i=1;i<n;i=i+2) { // odd numbered
lower_bounds[i] = -100.0;
upper_bounds[i] = 100.0;
}
sys.setParameterLimits( lower_bounds, upper_bounds );
int returnValue = 0; // assume success
try {
Optimizer opt( sys );
opt.setConvergenceTolerance( .0001 );
opt.useNumericalGradient( true );
f = opt.optimize( results );
}
catch (const std::exception& e) {
std::cout << e.what() << std::endl;
returnValue = 1; // failure
}
printf("LBFGSBDiffTest.cpp: f = %f params = ",f);
for( i=0; i<NUMBER_OF_PARAMETERS; i++ ) {
printf(" %f",results[i]);
}
printf("\n");
static const Real TOL = 1e-3;
Real expected[] = { 1.000000, 0.999998, 1.000000, 1.000001, 1.000003,
1.000006, 1.000007, 1.000012, 1.000022, 1.000040,
1.000081, 1.000161, 1.000325, 1.000650, 1.001302,
1.002603, 1.005214, 1.010450, 1.021013, 1.042466,
1.086736, 1.180997, 1.394759, 1.945352, 3.784388 };
bool fail = false;
for( i=0; i<NUMBER_OF_PARAMETERS; i++ ) {
if(!equalToTol(results[i], expected[i], TOL)) {
printf(" LBFGSBDiffTest.cpp: error results[%d] = %f expected=%f \n",i,results[i], expected[i]);
returnValue = 1;
}
}
return( returnValue );
}
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