double Graph::
golden(const int function,double xmin,double xcenter,double xmax,bool solveMax,double tolerance)
{
static const double phi=.5*(1.+sqrt(5.)),resPhi=2-phi;
//double xcenter=.5*(xmin+xmax);
double xnew=xcenter+resPhi*(xmax-xcenter);
if(fabs(xmax-xmin)<1e-5*(fabs(xnew)+fabs(xcenter))) return (xmax+xmin)/2;
#if 0
double f_xcenter=funcs.eval(function,xcenter);
double f_xnew=funcs.eval(function,xnew);
if(solveMax){
f_xcenter*=-1;
f_xnew*=-1;
}
if(f_xnew<f_xcenter) return golden(function,xcenter,xnew,xmax,solveMax,tolerance);
else return golden(function,xnew,xcenter,xmin,solveMax,tolerance);
#else
UNUSED(function);
UNUSED(solveMax);
UNUSED(tolerance);
#endif
return 0;
}
/***定义梯度函数求解极值函数**********/
double* grad(double (*f)(double,double),double *p,double a,double b,double tol,FILE *fp) //f数学公式，p搜索起点的向量，a、b用于求解黄金分割时的范围、tol实验精度、fp指向文本
{
double s[2],sa,ta,r;
double hmin;
int i;
solveS(p[0],p[1],s); //求解搜索方向向量
hmin=golden(gema,a,b,tol,s,p,fp); //求解gama极小值
for(i=0;i<2;i++)
{
p[i]=p[i]+hmin*s[i]; //计算新的搜索点
printf("%0.8f\t",p[i]);
fprintf(fp,"%0.8f\t",p[i]);
}
r=sqrt(f(p[0],p[1])); //求出到定点的距离
printf("%0.8f\n",r);
fprintf(fp,"%0.8f\n",r);
sa=sqrt(s[0]*s[0]+s[1]*s[1]);
ta=hmin*sa; //计算变化的距离
if(fabs(ta)<10*tol) return p; //如果变化距离小于精度，结束运算
p=grad(f,p,a,b,tol,fp); //否则继续搜索。
return p;
}
void Kaffee::golden()
{
dirty = TRUE;
t = (t == Red ? Golden : Red);
int r = random() % 40000;
QTimer::singleShot( r, this, SLOT(golden()) );
}
Kaffee::Kaffee(int pos, int r1, int r2)
{
p = pos;
t = Red;
r = r2;
QTimer::singleShot( r1, this, SLOT(golden()) );
dirty = true;
}
Kaffee::Kaffee(int pos)
{
p = pos;
t = Red;
int r = random() % 40000;
QTimer::singleShot( r, this, SLOT(golden()) );
dirty = TRUE;
}
void testRoot(){
printf("\nRoot finding and minimisation\n");
golden(functionTest3,0,2);
golden(functionTest1,3,7);
golden(functionTest2,5,7);
golden(poly,-0.5,1.0);
brute (poly,-1.0,1.0, 0.0001);
brute (functionTest1,3,7, 0.0001);
secant(poly,0.0,1.0);
newton(poly, dpoly, 0.0);
regulaFalsi(poly,-1.0,1.0);
bisect(poly,-1.0,1.0);
fixedPointIteration(cosine,1.0);
squareRoot(20);
}
void Graph::solveMin(const int function, double xmin, double xmax, bool solveMax) {
if (xmax < xmin) std::swap(xmin, xmax);
double xsolve = golden(function, xmin, .5 * (xmin + xmax), xmax, solveMax, 1e-5);
rootX = xsolve;
rootY = funcs.eval(function, xsolve);
rootShow = false;
minShow = true;
repaint();
}
REAL cIEC_OptimCurDir::Optim(REAL aV0,REAL aV1)
{
REAL aX = aV0;
REAL bX = aV1;
REAL cX;
REAL aY,bY,cY;
mnbrack(&aX,&bX,&cX,&aY,&bY,&cY);
REAL XMin;
golden(aX,bX,cX,1e-5,&XMin);
return XMin;
}
void BenchDMR::test()
{
REAL ax=-1,bx=1,cx,fa,fb,fc;
mnbrack(&ax,&bx,&cx,&fa,&fb,&fc);
REAL xmin1;
golden(ax,bx,cx,1e-15,&xmin1);
if (_pol.degre() <= 4)
BENCH_ASSERT(std::abs(_dpol(xmin1)) < BIG_epsilon);
BENCH_ASSERT(std::abs(_dpol(xmin1)) < GIGANTESQUE_epsilon);
Pt2dr aP = brent(true);
if (_pol.degre() <= 4)
BENCH_ASSERT(std::abs(_dpol(aP.x)) < BIG_epsilon);
BENCH_ASSERT(std::abs(_dpol(aP.x)) < GIGANTESQUE_epsilon);
}
bool cOneTestLSQ::OneOptimOnLine()
{
if (! OneLSQItere())
return false;
double aL0 = 0;
double aC0 = mCorel0LSQ;
double aL1 = 1;
double aC1 = CorrelOnLine(aL1);
if (aC1 == -1)
return false;
if (aC1<aC0)
return false;
double aL2 = std::max(2.0,TheNormMinLSQ/euclid(mDepLSQ));
double aC2 = CorrelOnLine(aL2);
if (aC2 == -1)
return false;
while (aC2 > aC1)
{
aL0 = aL1;
aC0 = aC1;
aL1 = aL2;
aC1 = aC2;
aL2 *= TheMult;
aC2 = CorrelOnLine(aL2);
if (aC2 == -1)
return false;
}
double aLMin;
golden(aL0,aL1,aL2,1e-3,&aLMin);
SetPIm2(mIm2->PVois(0,0) +mDepLSQ *aLMin);
return true;
}
DEF_TEST(SkRecordingAccuracyXfermode, reporter) {
#define FINEGRAIN 0
const Drawer drawer;
BitmapBackedCanvasStrategy golden(drawer.imageInfo());
PictureStrategy picture(drawer.imageInfo());
#if !FINEGRAIN
unsigned numErrors = 0;
SkString errors;
#endif
for (int iMode = 0; iMode < int(SkBlendMode::kLastMode); iMode++) {
const SkRect& clip = SkRect::MakeXYWH(100, 0, 100, 100);
SkBlendMode mode = SkBlendMode(iMode);
const SkBitmap& goldenBM = golden.recordAndReplay(drawer, clip, mode);
const SkBitmap& pictureBM = picture.recordAndReplay(drawer, clip, mode);
size_t pixelsSize = goldenBM.computeByteSize();
REPORTER_ASSERT(reporter, pixelsSize == pictureBM.computeByteSize());
// The pixel arrays should match.
#if FINEGRAIN
REPORTER_ASSERT(reporter,
0 == memcmp(goldenBM.getPixels(), pictureBM.getPixels(), pixelsSize));
#else
if (memcmp(goldenBM.getPixels(), pictureBM.getPixels(), pixelsSize)) {
numErrors++;
errors.appendf("For SkXfermode %d %s: SkPictureRecorder bitmap is wrong\n",
iMode, SkBlendMode_Name(mode));
}
#endif
}
#if !FINEGRAIN
REPORTER_ASSERT(reporter, 0 == numErrors, errors.c_str());
#endif
}
/******定义黄金分割函数*************/
double golden(double (*f)(double,double *,double *),double a,double b,double tol,double *s,double *p,FILE *fp) //f为数学函数，i为对应题目0-3对应a-d，a为左端点，b为右端点，tol为精度，fp指向文本
{
double r;
double a1,b1,c,d,length;
double tc,td,z;
r=0.5*(-1+sqrt(5.));
length=b-a;
c=a+(1-r)*length; //计算ck
d=b-(1-r)*length; //计算dk
tc=f(c,s,p); //计算两点对应的函数值
td=f(d,s,p);
if(tc<td) //判断下一个ak，bk的取值
{
a1=a;
b1=d;
}
else
{
a1=c;
b1=b;
}
if(fabs(a1-b1)>tol) //如果没有满足精度，继续细分
{
z=golden(f,a1,b1,tol,s,p,fp);
}
else
{
z=0.5*(a1+b1); //取最后两点的中点为极值点
}
return z;
}
// Compute the price of a bermudan swaption.
static int MC_BermSwpaption_Andersen(NumFunc_1 *p, Libor *ptLib, Swaption *ptBermSwpt, Volatility *ptVol, double Nominal, long NbrMCsimulation_param, long NbrMCsimulation, int NbrStepPerTenor, int generator, int flag_numeraire, int q, double *PriceBermSwp)
{
int alpha, beta, i, NbrExerciseDates;
double tenor, numeraire_0;
double ax, bx, cx, tol, xmin;
AndersenStruct andersen_struct;
PnlFunc FuncToMinimize;
Create_AndersenStruct(&andersen_struct);
//Nfac = ptVol->numberOfFactors;
//N = ptLib->numberOfMaturities;
tenor = ptBermSwpt->tenor;
alpha = pnl_iround(ptBermSwpt->swaptionMaturity/tenor); // T(alpha) is the swaption maturity
beta = pnl_iround(ptBermSwpt->swapMaturity/tenor); // T(beta) is the swap maturity
NbrExerciseDates = beta-alpha;
numeraire_0 = Numeraire(0, ptLib, flag_numeraire);
tol = 1e-10;
q = MIN(q, NbrExerciseDates-1); // The maximum number of kink-points that can be used is NbrExerciseDates-1.
q = MAX(1, q); // q must be greater than zero.
FuncToMinimize.function = &func_to_minimize;
FuncToMinimize.params = &andersen_struct;
// Initialize the structure andersen_struct using "NbrMCsimulation_param" paths.
// We will use these paths to estimates the optimal parameters of exercise strategy.
Init_AndersenStruct(&andersen_struct, ptLib, ptBermSwpt, ptVol, p, NbrMCsimulation_param, NbrStepPerTenor, generator, flag_numeraire, Nominal, q);
// At maturity, the parameter is null, because we exercise whenever payoff is positif.
pnl_vect_set_zero(andersen_struct.AndersenParams);
ax = 0; // lower point for GoldenSearch method
cx = andersen_struct.H_max; // upper point for GoldenSectionSearch method
bx = 0.5*(ax+cx); // middle point for GoldenSectionSearch method
for (i=q-1; i>=0; i--)
{
// Index of exercise date where we compute parameter of exercise strategy.
andersen_struct.j_start = pnl_vect_int_get(andersen_struct.AndersenIndices, i);
// Find optimal parameter at current exercise date.
golden(&FuncToMinimize, ax, bx, cx, tol, &xmin);
// Store this parameter in AndersenParams.
LET(andersen_struct.AndersenParams, andersen_struct.j_start) = xmin;
ax = 0.5*xmin;
bx = 0.5*(ax+cx);
}
// We simulate another set of Libor paths, independants of the ones used to estimate the parameters of exercise strategy.
// In general, choose NbrMCsimulation >> NbrMCsimulation_param.
Init_AndersenStruct(&andersen_struct, ptLib, ptBermSwpt, ptVol, p, NbrMCsimulation, NbrStepPerTenor, generator, flag_numeraire, Nominal, q);
// Finaly, we use the found parameters to estimate the price of bermudan swaption following.
andersen_struct.j_start = 0;
*PriceBermSwp = numeraire_0*AmOption_Price_Andersen(&andersen_struct);
// Free memory.
Free_AndersenStruct(&andersen_struct);
return OK;
}
void Kaffee::golden()
{
dirty = true;
t = (t == Red ? Golden : Red);
QTimer::singleShot( r, this, SLOT(golden()) );
}
void ICM_iteration(int N, int n, int K, int nlast, int first[], int m[], int **freq, int **ind, double **y,
double **F, double **F_new, double **cumw, double **cs, double **v, double **w,
double **nabla, double *lambda1, double *alpha1)
{
int i,j,k;
double a,lambda,alpha;
lambda=*lambda1;
for (k=1;k<=K;k++)
{
for (i=1;i<=m[k];i++)
v[k][i]=w[k][i]=0;
}
for (k=1;k<=K;k++)
{
j=0;
for (i=first[k];i<=n;i++)
{
if (freq[k][i]>0)
{
j++;
v[k][j] = freq[k][i]/(N*F[k][i]);
w[k][j] = freq[k][i]/(N*SQR(F[k][i]));
}
if (freq[0][i]>0)
{
v[k][j] -= freq[0][i]/(N*(1-F[K+1][i]));
w[k][j] += freq[0][i]/(N*SQR(1-F[K+1][i]));
}
}
if (nlast<n)
v[k][m[k]] -=lambda;
for (i=1;i<=m[k];i++)
v[k][i] += w[k][i]*F[k][ind[k][i]];
}
cumsum(K,m,v,cs);
convexmin(K,m,cumw,cs,w,y);
Compute_F(K,n,freq,first,y,F_new);
if (F_new[K+1][nlast]>=1)
{
a=(1-F[K+1][nlast])/(F_new[K+1][nlast]-F[K+1][nlast]);
for (k=1;k<=K;k++)
{
for (i=first[k];i<=n;i++)
F_new[k][i]=F[k][i]+a*(F_new[k][i]-F[k][i]);
}
compute_Fplus(n,K,F_new);
for (k=1;k<=K;k++)
{
j=0;
for (i=first[k];i<=n;i++)
{
if (freq[k][i]>0)
{
j++;
y[k][j]=F_new[k][i];
}
}
}
}
if (f_alpha_prime(N,n,K,freq,1.0,F,F_new,lambda)<=0) alpha=1;
else
alpha=golden(N,n,K,freq,F,F_new,0.1,1,f_alpha,lambda);
for (k=1;k<=K;k++)
{
for (i=first[k];i<=n;i++)
F[k][i] += alpha*(F_new[k][i]-F[k][i]);
}
compute_Fplus(n,K,F);
if (nlast<n)
lambda=compute_lambda(N,n,K,freq,F);
else
lambda=0;
compute_nabla(N,n,K,first,m,freq,F,lambda,nabla);
*lambda1=lambda;
*alpha1=alpha;
}
