void sens(void f(double t, double z[], double param[], double zp[]), void fjac(double t, double z[], double param[], double** jac), int ijac, double param[], int neq, int np, double t, double y[], double sp[] ) { int i,j,k,nx; double **jac, sum; // allocate memory for Jacobian nx = neq/(1+np); jac = allocDoubleMatrix(nx, nx+np,false); // if (ijac == 1) fjac( t,y,param,jac ); else findiff1(f,param,nx,np,t,y,jac); // righthandside of sensitivity equation for (i=0;i<np;i++) { for (k=0;k<nx;k++) { sum = 0.0; for (j=0;j<nx;j++) sum = sum + jac[k][j]*y[i*nx+j]; sum = sum + jac[k][nx+i]; sp[i*nx+k] = sum; } } // deallocate memory freeDoubleMatrix(jac,nx); }
EndCriteria::Type LevenbergMarquardt::minimize(Problem& P, const EndCriteria& endCriteria) { EndCriteria::Type ecType = EndCriteria::None; P.reset(); Array x_ = P.currentValue(); currentProblem_ = &P; initCostValues_ = P.costFunction().values(x_); int m = initCostValues_.size(); int n = x_.size(); boost::scoped_array<double> xx(new double[n]); std::copy(x_.begin(), x_.end(), xx.get()); boost::scoped_array<double> fvec(new double[m]); boost::scoped_array<double> diag(new double[n]); int mode = 1; double factor = 1; int nprint = 0; int info = 0; int nfev =0; boost::scoped_array<double> fjac(new double[m*n]); int ldfjac = m; boost::scoped_array<int> ipvt(new int[n]); boost::scoped_array<double> qtf(new double[n]); boost::scoped_array<double> wa1(new double[n]); boost::scoped_array<double> wa2(new double[n]); boost::scoped_array<double> wa3(new double[n]); boost::scoped_array<double> wa4(new double[m]); // requirements; check here to get more detailed error messages. QL_REQUIRE(n > 0, "no variables given"); QL_REQUIRE(m >= n, "less functions (" << m << ") than available variables (" << n << ")"); QL_REQUIRE(endCriteria.functionEpsilon() >= 0.0, "negative f tolerance"); QL_REQUIRE(xtol_ >= 0.0, "negative x tolerance"); QL_REQUIRE(gtol_ >= 0.0, "negative g tolerance"); QL_REQUIRE(endCriteria.maxIterations() > 0, "null number of evaluations"); // call lmdif to minimize the sum of the squares of m functions // in n variables by the Levenberg-Marquardt algorithm. MINPACK::LmdifCostFunction lmdifCostFunction = boost::bind(&LevenbergMarquardt::fcn, this, _1, _2, _3, _4, _5); MINPACK::lmdif(m, n, xx.get(), fvec.get(), static_cast<double>(endCriteria.functionEpsilon()), static_cast<double>(xtol_), static_cast<double>(gtol_), static_cast<int>(endCriteria.maxIterations()), static_cast<double>(epsfcn_), diag.get(), mode, factor, nprint, &info, &nfev, fjac.get(), ldfjac, ipvt.get(), qtf.get(), wa1.get(), wa2.get(), wa3.get(), wa4.get(), lmdifCostFunction); info_ = info; // check requirements & endCriteria evaluation QL_REQUIRE(info != 0, "MINPACK: improper input parameters"); //QL_REQUIRE(info != 6, "MINPACK: ftol is too small. no further " // "reduction in the sum of squares " // "is possible."); if (info != 6) ecType = QuantLib::EndCriteria::StationaryFunctionValue; //QL_REQUIRE(info != 5, "MINPACK: number of calls to fcn has " // "reached or exceeded maxfev."); endCriteria.checkMaxIterations(nfev, ecType); QL_REQUIRE(info != 7, "MINPACK: xtol is too small. no further " "improvement in the approximate " "solution x is possible."); QL_REQUIRE(info != 8, "MINPACK: gtol is too small. fvec is " "orthogonal to the columns of the " "jacobian to machine precision."); // set problem std::copy(xx.get(), xx.get()+n, x_.begin()); P.setCurrentValue(x_); P.setFunctionValue(P.costFunction().value(x_)); return ecType; }