255 lines
5.4 KiB
C++
255 lines
5.4 KiB
C++
#ifndef _MATEXP_H_
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#define _MATEXP_H_
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//general routine for polynomial of a matrix, tuned to minimize the number
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//of matrix-matrix multiplications on cost of additions and memory
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// the polynom and exp routines will work on any type, for which traits class
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// is defined containing definition of an element type, norm and axpy operation
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#include "la_traits.h"
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template<class T,class R>
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const T polynom2(const T &x, const NRVec<R> &c)
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{
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int order=c.size()-1;
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T z,y;
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//trivial reference implementation by horner scheme
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if(order==0) {y=x; y=c[0];} //to avoid the problem: we do not know the size of the matrix to contruct a scalar one
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else
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{
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int i;
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z=x*c[order];
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for(i=order-1; i>=0; i--)
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{
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if(i<order-1) z=y*x;
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y=z+c[i];
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}
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}
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return y;
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}
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template<class T,class R>
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const T polynom(const T &x, const NRVec<R> &c)
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{
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int n=c.size()-1;
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int i,j,k,m=0,t;
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if(n<=4) return polynom2(x,c); //here the horner scheme is optimal
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//first find m which minimizes the number of multiplications
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j=10*n;
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for(i=2;i<=n+1;i++)
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{
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t=i-2+2*(n/i)-(n%i)?0:1;
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if(t<j)
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{
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j=t;
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m=i;
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}
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}
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//allocate array for powers up to m
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T *xpows = new T[m];
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xpows[0]=x;
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for(i=1;i<m;i++) xpows[i]=xpows[i-1]*x;
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//run the summation loop
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T r,s,f;
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k= -1;
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for(i=0; i<=n/m;i++)
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{
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for(j=0;j<m;j++)
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{
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k++;
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if(k>n) break;
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if(j==0) {if(i==0) s=x; /*just to get the dimensions of the matrix*/ s=c[k]; /*create diagonal matrix*/}
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else
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LA_traits<T>::axpy(s,xpows[j-1],c[k]); //general s+=xpows[j-1]*c[k]; but more efficient for matrices
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}
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if(i==0) {r=s; f=xpows[m-1];}
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else
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{
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r+= s*f;
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f=f*xpows[m-1];
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}
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}
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delete[] xpows;
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return r;
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}
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//for general objects
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template<class T>
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const T ncommutator ( const T &x, const T &y, int nest=1, const bool right=1)
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{
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T z;
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if(right) {z=x; while(--nest>=0) z=z*y-y*z;}
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else {z=y; while(--nest>=0) z=x*z-z*x;}
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return z;
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}
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template<class T>
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const T nanticommutator ( const T &x, const T &y, int nest=1, const bool right=1)
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{
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T z;
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if(right) {z=x; while(--nest>=0) z=z*y+y*z;}
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else {z=y; while(--nest>=0) z=x*z+z*x;}
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return z;
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}
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//general BCH expansion (can be written more efficiently in a specialization for matrices)
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template<class T>
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const T BCHexpansion (const T &h, const T &t, const int n, const bool verbose=1)\
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{
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T result=h;
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double factor=1.;
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T z=h;
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for(int i=1; i<=n; ++i)
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{
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factor/=i;
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z= z*t-t*z;
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if(verbose) cerr << "BCH contribution at order "<<i<<" : "<<z.norm()<<endl;
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result+= z*factor;
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}
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return result;
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}
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template<class T>
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const T ipow( const T &x, int i)
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{
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if(i<0) laerror("negative exponent in ipow");
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if(i==0) {T r=x; r=(typename LA_traits<T>::elementtype)1; return r;}//trick for matrix dimension
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if(i==1) return x;
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T y,z;
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z=x;
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while(!(i&1))
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{
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z = z*z;
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i >>= 1;
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}
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y=z;
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while((i >>= 1)/*!=0*/)
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{
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z = z*z;
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if(i&1) y = y*z;
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}
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return y;
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}
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inline int nextpow2(const double n)
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{
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const double log2=log(2.);
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if(n<=.75) return 0; //try to keep the taylor expansion short
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if(n<=1.) return 1;
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return int(ceil(log(n)/log2-log(.75)));
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}
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template<class T>
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NRVec<typename LA_traits<T>::elementtype> exp_aux(const T &x, int &power)
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{
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//should better be computed by mathematica to have accurate last digits, chebyshev instead, see exp in glibc
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static double exptaylor[]={
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1.,
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1.,
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0.5,
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0.1666666666666666666666,
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0.0416666666666666666666,
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0.0083333333333333333333,
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0.0013888888888888888888,
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0.00019841269841269841253,
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2.4801587301587301566e-05,
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2.7557319223985892511e-06,
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2.7557319223985888276e-07,
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2.5052108385441720224e-08,
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2.0876756987868100187e-09,
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1.6059043836821613341e-10,
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1.1470745597729724507e-11,
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7.6471637318198164055e-13,
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4.7794773323873852534e-14,
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2.8114572543455205981e-15,
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1.5619206968586225271e-16,
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8.2206352466243294955e-18,
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4.1103176233121648441e-19,
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0.};
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double mnorm= LA_traits<T>::norm(x);
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power=nextpow2(mnorm);
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double scale=exp(-log(2.)*power);
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//find how long taylor expansion will be necessary
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const double precision=1e-16;
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double s,t;
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s=mnorm*scale;
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int n=0;
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t=1.;
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do {
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n++;
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t*=s;
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}
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while(t*exptaylor[n]>precision);//taylor 0 will terminate in any case
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int i; //adjust the coefficients in order to avoid scaling the argument
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NRVec<typename LA_traits<T>::elementtype> taylor2(n+1);
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for(i=0,t=1.;i<=n;i++)
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{
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taylor2[i]=exptaylor[i]*t;
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t*=scale;
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}
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return taylor2;
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}
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template<class T>
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const T exp(const T &x)
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{
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int power;
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//prepare the polynom of and effectively scale T
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NRVec<typename LA_traits<T>::elementtype> taylor2=exp_aux(x,power);
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T r=polynom(x,taylor2); //for accuracy summing from the smallest terms up would be better, but this is more efficient for matrices
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//power the result back
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for(int i=0; i<power; i++) r=r*r;
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return r;
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}
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template<class M, class V>
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const V exptimes(const M &mat, V vec) //uses just matrix vector multiplication
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{
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if(mat.nrows()!=mat.ncols()||(unsigned int) mat.nrows() != (unsigned int)vec.size()) laerror("inappropriate sizes in exptimes");
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int power;
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//prepare the polynom of and effectively scale the matrix
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NRVec<typename LA_traits<M>::elementtype> taylor2=exp_aux(mat,power);
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V result(mat.nrows());
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for(int i=1; i<=(1<<power); ++i) //unfortunatelly, here we have to repeat it many times, unlike if the matrix is stored explicitly
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{
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if(i>1) vec=result; //apply again to the result of previous application
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//apply polynom of the matrix to the vector iteratively
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V y=vec;
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result=y*taylor2[0];
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for(int j=1; j<taylor2.size(); ++j)
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{
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y=mat*y;
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result.axpy(taylor2[j],y);
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}
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}
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return result;
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}
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#endif
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