/*main関数。*/
int main(void){
int i;
double x;
/*s(x) : 標準シグモイド関数を表示。*/
printf("s(x) = 1 / {1 + exp(-x)}\n");
/*s(x)が0.1~0.9となるような解xをそれぞれえ求める。*/
for(i = 1; i < 10; i++){
/*xの初期値を0とし、s(x)=0.iの解をニュートン法で得る。*/
x = Newton(s, ds, i / 10.0, 0);
/*求めた解をs(x)に代入し、解が正しいか確認。*/
printf("s(%f) = %f\n", x, s(x));
}
printf("\n");
/*f(x) : 最小値を求めたい関数を表示。*/
printf("f(x) = 3x^4 - 4x^3 - 12x^2 + 32\n");
/*xの初期値を3とし、f(x)の傾きが0となるx座標をニュートン法で求める。*/
x = Newton(df, ddf, 0, 3);
/*xにはf(x)の最小値の候補のx座標が入っている。*/
/*求めたxをf(x)に代入し最小値となっているか確認。*/
printf("f(%f) = %f (min)\n", x, f(x));
return 0;
}
int main(int argc, char const *argv[])
{
double a;
printf("%fl\n", Newton(10));
a=Newton(10);
return 0;
}
int main(){
printf("Calculando la longitud de la elipse obtenemos como resultado\nL=%.10g\n\n", L);
printf("El punto encontrado aproximando las integrales mediante integracion de polinomios ");
/*Mostramos el cero de la función g obtenido aplicando el método de Newton con tolerancia 1.e-6.*/
printf("de Splines es\n%.10g\n\n", Newton(g, g1, 0.5, 1.e-6));
printf("Reformulando la funcion L_{EA}(x) tal y como se indica en la memoria obtenemos ");
/*Mostramos el cero de la función h obtenido aplicando el método de Newton con tolerancia 1.e-6.*/
printf("\n%.10g\n\nAproximando las integrales haciendo uso de ", Newton(h, g1, 0.5, 1.e-6));
printf("polinomios interpoladores de Splines y\n%.10g\n\nAproximando ", Newton(h1, g1, 0.5, 1.e-6));
printf("las integrales mediante cuadratura de Gauss-Chebyschev\n\n");
return 0;
}
//int CollaborativeFiltering::MLEWeibullDistribution(vector<double> time_vec){
int CollaborativeFiltering::MLEWeibullDistribution(const string sql){
// ifstream is;
// is.open(filename.c_str());
// if(!is){
// cout << "cannnot open" << endl;
// exit(1);
// }
//
// vector<double> time_vec;
// vector<double>::iterator it;
//
// while(!is.fail()){
// double time;
// is >> time;
// time_vec.push_back(time);
// }
//
// it = time_vec.end();
// time_vec.erase(--it);
// Newton(time_vec, 1);
Newton(sql, 1);
return 0;
}
void multiple_roots ( int n, dcmplx p[n], double eps, int maxit,
dcmplx r[n-1], double tol, int m[n-1] )
{
int i,nit;
dcmplx *dp[n-1];
roots(n,p,eps,maxit,r);
multiplicities(n-1,tol,r,m);
derivatives(n,n-1,p,dp);
/* write_derivatives(n,n-1,dp); */
for(i=0; i<n-1; i++)
{
if (m[i] == 1)
nit = Newton(n,p,dp[0],&r[i],eps,8);
else
nit = Newton(n-m[i]+1,dp[m[i]-2],dp[m[i]-1],&r[i],eps,8);
/* printf("refined root %d : ", i);
writeln_dcmplx(r[i]); */
}
}
//牛顿迭代函数
double Newton(double x)
{
double a;
a = (x+115.0/x)/2;
if (fabs(a-x)<pow(10,-5))
{
return a;
}
else
{
return Newton(a);
}
}
void ChiSquareNumeric::Calc(void) { //Calculate ChiSq-Minimum
/* Minimize Chi-Square... I know, there is a Gauss-Newton Method which would
be better, but this is a proof of concept, that it would work with fundamental
functions. The optimization can be done later. */
SetActive();
*Parameter = Newton( (FuncVectorNParam) &NecessaryConditions, *Parameter);
DegOfFreedom = NValues-Parameter->GetVectorDim();
//CorrelCoeff = -a12/sqrt(a11*a22); //C(0,1)/(D*sqrt(C(0,0)/D)*sqrt(C(1,1)/D));
//StDn = sqrt(a22/D);
//StDm = sqrt(a11/D);
//StDFit = sqrt(ChiSquare(x,y,yerr,N)/DegOfFreedom);
return;
}
void main() //主函数
{
double z; //定义变量
printf("Please input the number of title \n "); //输出题目选择
scanf("%d",&b); //题号赋值给b
printf("Please input the order M \n"); //输入求解阶数要求
scanf("%d",&M);
z=Newton(1,1e-7,1000,fm,df); //调用牛顿-拉夫森函数求解
printf("z is %0.8f",z);
getchar();
getchar();
}
int main(void)
{
double init, tol;
int maxit;
printf("F(x) = x*x - 2 = 0");
printf("\n\nNewton-Raphson Method\n\nEnter initial guess: ");
scanf("%lf", &init);
printf("\nEnter maximum iterations: ");
scanf("%d", &maxit);
printf("\nEnter algorithm tolerance: ");
scanf("%lf", &tol);
printf("\nUsing %d iterations, the root of F(x) = 0 has been calculated as:\n\n", Newton(&init, F, dF, maxit, tol));
printf("x = %.12f\n\nThis has a residue of %.10g\n", init, F(init));
return 0;
}
int main(int argc, const char * argv[])
{
IFunction *targetFunction = selectFunction(); // Выбор целевой функции
Point startPoint = inputPoint(*targetFunction); // Ввод стартовой точки
Point direction = inputDirection(*targetFunction, true); // Направление принимаем за единичный вектор
double precision = inputPrecision(); // Ввод точности
Point min = Newton().optimize(*targetFunction, startPoint, direction, precision);
cout << "Minimum is:\t";
min.print(precision);
cout << endl << "F(min) is:\t";
cout << targetFunction->value(min);
return 0;
}
int LinearElasticityEvaluator::Solve(){
//ElasticityAssembler assembler(mesh,sf_factory,nodemap,bcdef);
NewtonSolver Newton(*this);
//NewtonSolver Newton(assembler);
//Newton.Solve(1.0e-14);
/*
arma::wall_clock timer;
std::cout << "In solve" << std::endl;
timer.tic();
// Solve the system
computeSolutionBelos(KGLOB,RHS,DU,1,0,1000,1000,1.0e-14,false,"RILUK");
std::cout << "Solve time: " << timer.toc() << std::endl;
*/
return 1;
}
