/* --------------------------------------------------------- */
/* --- File: cmaes.c  -------- Author: Nikolaus Hansen   --- */
/* --------------------------------------------------------- */
/*
     CMA-ES for non-linear function minimization.

     Copyright 1996, 2003, 2007 Nikolaus Hansen
     e-mail: hansen .AT. bionik.tu-berlin.de
             hansen .AT. lri.fr

     This library is free software; you can redistribute it and/or
     modify it under the terms of the GNU Lesser General Public
     License as published by the Free Software Foundation; either
     version 2.1 of the License, or (at your option) any later
     version (see http://www.gnu.org/copyleft/lesser.html).

     This library is distributed in the hope that it will be useful,
     but WITHOUT ANY WARRANTY; without even the implied warranty of
     MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE.  See the GNU
     Lesser General Public License for more details.

*/
/* --- Changes : ---
  03/03/21: argument const double *rgFunVal of
            cmaes_ReestimateDistribution() was treated incorrectly.
  03/03/29: restart via cmaes_resume_distribution() implemented.
  03/03/30: Always max std dev / largest axis is printed first.
  03/08/30: Damping is adjusted for large mueff.
  03/10/30: Damping is adjusted for large mueff always.
  04/04/22: Cumulation time and damping for step size adjusted.
            No iniphase but conditional update of pc.
  05/03/15: in ccov-setting mucov replaced by mueff.
  05/10/05: revise comment on resampling in example.c
  05/10/13: output of "coorstddev" changed from sigma * C[i][i]
            to correct sigma * sqrt(C[i][i]).
  05/11/09: Numerical problems are not anymore handled by increasing
            sigma, but lead to satisfy a stopping criterion in
            cmaes_Test().
  05/11/09: Update of eigensystem and test for numerical problems
            moved right before sampling.
  06/02/24: Non-ansi array definitions replaced (thanks to Marc
            Toussaint).
  06/02/25: Overflow in time measurement for runs longer than
            2100 seconds. This could lead to stalling the
            covariance matrix update for long periods.
            Time measurement completely rewritten.
  06/02/26: Included population size lambda as parameter to
            cmaes_init (thanks to MT).
  06/02/26: Allow no initial reading/writing of parameters via
            "non" and "writeonly" keywords for input parameter
            filename in cmaes_init.
  06/02/27: Optimized code regarding time spent in updating the
            covariance matrix in function Adapt_C2().
  07/08/03: clean up and implementation of an exhaustive test
            of the eigendecomposition (via #ifdef for now)
  07/08/04: writing of output improved
  07/08/xx: termination criteria revised and more added,
            damp replaced by damps=damp*cs, documentation improved.
            Interface significantly changed, evaluateSample function
            and therefore the function pointer argument removed.
            Renaming of functions in accordance with Java code.
            Clean up of parameter names, mainly in accordance with
            Matlab conventions. Most termination criteria can be
            changed online now. Many more small changes, but not in
            the core procedure.
  07/10/29: ReSampleSingle() got a better interface. ReSampleSingle()
            is now ReSampleSingle_old only for backward
            compatibility. Also fixed incorrect documentation. The new
            function SampleSingleInto() has an interface similar to
            the old ReSampleSingle(), but is not really necessary.
  07/11/20: bug: stopMaxIter did not translate into the correct default
            value but into -1 as default. This lead to a too large
            damps and the termination test became true from the first
            iteration. (Thanks to Michael Calonder)
  07/11/20: new default stopTolFunHist = 1e-13;  (instead of zero)
  08/09/26: initial diagonal covariance matrix in code, but not
            yet in interface
  08/09/27: diagonalCovarianceMatrix option in initials.par provided
  08/10/17: uncertainty handling implemented in example3.c.
            PerturbSolutionInto() provides the optional small
            perturbations before reevaluation.
  10/10/16: TestForTermination changed such that diagonalCovarianceMatrix
            option now yields linear time behavior

  Wish List

    o as writing time is measure for all files at once, the display
      cannot be independently written to a file via signals.par, while
      this would be desirable.

    o clean up sorting of eigenvalues and vectors which is done repeatedly.

    o either use cmaes_Get() in cmaes_WriteToFilePtr(): revise the
      cmaes_write that all keywords available with get and getptr are
      recognized. Also revise the keywords, keeping backward
      compatibility. (not only) for this it would be useful to find a
      way how cmaes_Get() signals an unrecognized keyword. For GetPtr
      it can return NULL.

    o or break cmaes_Get() into single getter functions, being a nicer
      interface, and compile instead of runtime error, and faster. For
      file signals.par it does not help.

    o writing data depending on timing in a smarter way, e.g. using 10%
      of all time. First find out whether clock() is useful for measuring
      disc writing time and then timings_t class can be utilized.
      For very large dimension the default of 1 seconds waiting might
      be too small.

    o allow modification of best solution depending on delivered f(xmean)

    o re-write input and output procedures
*/

#include <math.h>   /* sqrt() */
#include <stddef.h> /* size_t */
#include <stdlib.h> /* NULL, free */
#include <string.h> /* strlen() */
#include <stdio.h>  /* sprintf(), NULL? */
#include "cmaes_interface.h" /* <time.h> via cmaes.h */

/* --------------------------------------------------------- */
/* ------------------- Declarations ------------------------ */
/* --------------------------------------------------------- */

/* ------------------- External Visibly -------------------- */

/* see cmaes_interface.h for those, not listed here */

long   random_init(random_t *, long unsigned seed /* 0==clock */);
void   random_exit(random_t *);
double random_Gauss(random_t *); /* (0,1)-normally distributed */
double random_Uniform(random_t *);
long   random_Start(random_t *, long unsigned seed /* 0==1 */);

void   timings_init(timings_t *timing);
void   timings_start(timings_t *timing); /* fields totaltime and tictoctime */
double timings_update(timings_t *timing);
void   timings_tic(timings_t *timing);
double timings_toc(timings_t *timing);

void readpara_init (readpara_t *, int dim, int seed,  const double * xstart,
                    const double * sigma, int lambda, const char * filename);
void readpara_exit(readpara_t *);
void readpara_ReadFromFile(readpara_t *, const char *szFileName);
void readpara_SupplementDefaults(readpara_t *);
void readpara_SetWeights(readpara_t *, const char * mode);
void readpara_WriteToFile(readpara_t *, const char *filenamedest,
                          const char *parafilesource);

double const * cmaes_SetMean(cmaes_t *, const double *xmean);
double * cmaes_PerturbSolutionInto(cmaes_t *t, double *xout,
                                   double const *xin, double eps);
void cmaes_WriteToFile(cmaes_t *, const char *key, const char *name);
void cmaes_WriteToFileAW(cmaes_t *t, const char *key, const char *name,
                         char * append);
void cmaes_WriteToFilePtr(cmaes_t *, const char *key, FILE *fp);
void cmaes_ReadFromFilePtr(cmaes_t *, FILE *fp);
void cmaes_FATAL(char const *s1, char const *s2,
                 char const *s3, char const *s4);


/* ------------------- Locally visibly ----------------------- */

static char * getTimeStr(void);
static void TestMinStdDevs( cmaes_t *);
/* static void WriteMaxErrorInfo( cmaes_t *); */

static void Eigen( int N,  double **C, double *diag, double **Q,
                   double *rgtmp);
static int  Check_Eigen( int N,  double **C, double *diag, double **Q);
static void QLalgo2 (int n, double *d, double *e, double **V);
static void Householder2(int n, double **V, double *d, double *e);
static void Adapt_C2(cmaes_t *t, int hsig);

static void FATAL(char const *sz1, char const *s2,
                  char const *s3, char const *s4);
static void ERRORMESSAGE(char const *sz1, char const *s2,
                         char const *s3, char const *s4);
static void   Sorted_index( const double *rgFunVal, int *index, int n);
static int    SignOfDiff( const void *d1, const void * d2);
static double douSquare(double);
static double rgdouMax( const double *rgd, int len);
static double rgdouMin( const double *rgd, int len);
static double douMax( double d1, double d2);
static double douMin( double d1, double d2);
static int    intMin( int i, int j);
static int    MaxIdx( const double *rgd, int len);
static int    MinIdx( const double *rgd, int len);
static double myhypot(double a, double b);
static double * new_double( int n);
static void * new_void( int n, size_t size);

/* --------------------------------------------------------- */
/* ---------------- Functions: cmaes_t --------------------- */
/* --------------------------------------------------------- */

static char *
getTimeStr(void) {
  time_t tm = time(NULL);
  static char s[33];

  /* get time */
  strncpy(s, ctime(&tm), 24); /* TODO: hopefully we read something useful */
  s[24] = '\0'; /* cut the \n */
  return s;
}

char *
cmaes_SayHello(cmaes_t *t)
{
  /* write initial message */
  sprintf(t->sOutString,
          "(%d,%d)-CMA-ES(mu_eff=%.1f), Ver=\"%s\", dimension=%d, diagonalIterations=%ld, randomSeed=%d (%s)",
          t->sp.mu, t->sp.lambda, t->sp.mueff, t->version, t->sp.N, (long)t->sp.diagonalCov,
          t->sp.seed, getTimeStr());

  return t->sOutString;
}

double *
cmaes_init(cmaes_t *t, /* "this" */
                int dimension,
                double *inxstart,
                double *inrgstddev, /* initial stds */
                long int inseed,
                int lambda,
                const char *input_parameter_filename)
{
  int i, j, N;
  double dtest, trace;

  t->version = "3.11.00.beta";

  readpara_init (&t->sp, dimension, inseed, inxstart, inrgstddev,
                   lambda, input_parameter_filename);
  t->sp.seed = random_init( &t->rand, (long unsigned int) t->sp.seed);

  N = t->sp.N; /* for convenience */

  /* initialization  */
  for (i = 0, trace = 0.; i < N; ++i)
    trace += t->sp.rgInitialStds[i]*t->sp.rgInitialStds[i];
  t->sigma = sqrt(trace/N); /* t->sp.mueff/(0.2*t->sp.mueff+sqrt(N)) * sqrt(trace/N); */

  t->chiN = sqrt((double) N) * (1. - 1./(4.*N) + 1./(21.*N*N));
  t->flgEigensysIsUptodate = 1;
  t->flgCheckEigen = 0;
  t->genOfEigensysUpdate = 0;
  timings_init(&t->eigenTimings);
  t->flgIniphase = 0; /* do not use iniphase, hsig does the job now */
  t->flgresumedone = 0;
  t->flgStop = 0;

  for (dtest = 1.; dtest && dtest < 1.1 * dtest; dtest *= 2.)
    if (dtest == dtest + 1.)
      break;
  t->dMaxSignifKond = dtest / 1000.; /* not sure whether this is really save, 100 does not work well enough */

  t->gen = 0;
  t->countevals = 0;
  t->state = 0;
  t->dLastMinEWgroesserNull = 1.0;
  t->printtime = t->writetime = t->firstwritetime = t->firstprinttime = 0;

  t->rgpc = new_double(N);
  t->rgps = new_double(N);
  t->rgdTmp = new_double(N+1);
  t->rgBDz = new_double(N);
  t->rgxmean = new_double(N+2); t->rgxmean[0] = N; ++t->rgxmean;
  t->rgxold = new_double(N+2); t->rgxold[0] = N; ++t->rgxold;
  t->rgxbestever = new_double(N+3); t->rgxbestever[0] = N; ++t->rgxbestever;
  t->rgout = new_double(N+2); t->rgout[0] = N; ++t->rgout;
  t->rgD = new_double(N);
  t->C = (double**)new_void(N, sizeof(double*));
  t->B = (double**)new_void(N, sizeof(double*));
  t->publicFitness = new_double(t->sp.lambda);
  t->rgFuncValue = new_double(t->sp.lambda+1);
  t->rgFuncValue[0]=t->sp.lambda; ++t->rgFuncValue;
  t->arFuncValueHist = new_double(10+(int)ceil(3.*10.*N/t->sp.lambda)+1);
  t->arFuncValueHist[0] = (double)(10+(int)ceil(3.*10.*N/t->sp.lambda));
  t->arFuncValueHist++;

  for (i = 0; i < N; ++i) {
      t->C[i] = new_double(i+1);
      t->B[i] = new_double(N);
    }
  t->index = (int *) new_void(t->sp.lambda, sizeof(int));
  for (i = 0; i < t->sp.lambda; ++i)
    t->index[i] = i; /* should not be necessary */
  t->rgrgx = (double **)new_void(t->sp.lambda, sizeof(double*));
  for (i = 0; i < t->sp.lambda; ++i) {
    t->rgrgx[i] = new_double(N+2);
    t->rgrgx[i][0] = N;
    t->rgrgx[i]++;
  }

  /* Initialize newed space  */

  for (i = 0; i < N; ++i)
    for (j = 0; j < i; ++j)
       t->C[i][j] = t->B[i][j] = t->B[j][i] = 0.;

  for (i = 0; i < N; ++i)
    {
      t->B[i][i] = 1.;
      t->C[i][i] = t->rgD[i] = t->sp.rgInitialStds[i] * sqrt(N / trace);
      t->C[i][i] *= t->C[i][i];
      t->rgpc[i] = t->rgps[i] = 0.;
    }

  t->minEW = rgdouMin(t->rgD, N); t->minEW = t->minEW * t->minEW;
  t->maxEW = rgdouMax(t->rgD, N); t->maxEW = t->maxEW * t->maxEW;

  t->maxdiagC=t->C[0][0]; for(i=1;i<N;++i) if(t->maxdiagC<t->C[i][i]) t->maxdiagC=t->C[i][i];
  t->mindiagC=t->C[0][0]; for(i=1;i<N;++i) if(t->mindiagC>t->C[i][i]) t->mindiagC=t->C[i][i];

  /* set xmean */
  for (i = 0; i < N; ++i)
    t->rgxmean[i] = t->rgxold[i] = t->sp.xstart[i];
  /* use in case xstart as typicalX */
  if (t->sp.typicalXcase)
    for (i = 0; i < N; ++i)
      t->rgxmean[i] += t->sigma * t->rgD[i] * random_Gauss(&t->rand);

  if (strcmp(t->sp.resumefile, "_no_")  != 0)
    cmaes_resume_distribution(t, t->sp.resumefile);

  return (t->publicFitness);

} /* cmaes_init() */

/* --------------------------------------------------------- */
/* --------------------------------------------------------- */

void
cmaes_resume_distribution(cmaes_t *t, char *filename)
{
  int i, j, res, n;
  double d;
  FILE *fp = fopen( filename, "r");
  if(fp == NULL) {
    ERRORMESSAGE("cmaes_resume_distribution(): could not open '",
                 filename, "'",0);
    return;
  }
  /* count number of "resume" entries */
  i = 0; res = 0;
  while (1) {
    if ((res = fscanf(fp, " resume %lg", &d)) == EOF)
      break;
    else if (res==0)
      fscanf(fp, " %*s");
    else if(res > 0)
      i += 1;
  }

  /* go to last "resume" entry */
  n = i; i = 0; res = 0; rewind(fp);
  while (i<n) {
    if ((res = fscanf(fp, " resume %lg", &d)) == EOF)
      FATAL("cmaes_resume_distribution(): Unexpected error, bug",0,0,0);
    else if (res==0)
      fscanf(fp, " %*s");
    else if(res > 0)
      ++i;
  }
  if (d != t->sp.N)
    FATAL("cmaes_resume_distribution(): Dimension numbers do not match",0,0,0);

  /* find next "xmean" entry */
  while (1) {
    if ((res = fscanf(fp, " xmean %lg", &d)) == EOF)
      FATAL("cmaes_resume_distribution(): 'xmean' not found",0,0,0);
    else if (res==0)
      fscanf(fp, " %*s");
    else if(res > 0)
      break;
  }

  /* read xmean */
  t->rgxmean[0] = d; res = 1;
  for(i = 1; i < t->sp.N; ++i)
    res += fscanf(fp, " %lg", &t->rgxmean[i]);
  if (res != t->sp.N)
    FATAL("cmaes_resume_distribution(): xmean: dimensions differ",0,0,0);

  /* find next "path for sigma" entry */
  while (1) {
    if ((res = fscanf(fp, " path for sigma %lg", &d)) == EOF)
      FATAL("cmaes_resume_distribution(): 'path for sigma' not found",0,0,0);
    else if (res==0)
      fscanf(fp, " %*s");
    else if(res > 0)
      break;
  }

  /* read ps */
  t->rgps[0] = d; res = 1;
  for(i = 1; i < t->sp.N; ++i)
    res += fscanf(fp, " %lg", &t->rgps[i]);
  if (res != t->sp.N)
    FATAL("cmaes_resume_distribution(): ps: dimensions differ",0,0,0);

  /* find next "path for C" entry */
  while (1) {
    if ((res = fscanf(fp, " path for C %lg", &d)) == EOF)
      FATAL("cmaes_resume_distribution(): 'path for C' not found",0,0,0);
    else if (res==0)
      fscanf(fp, " %*s");
    else if(res > 0)
      break;
  }
  /* read pc */
  t->rgpc[0] = d; res = 1;
  for(i = 1; i < t->sp.N; ++i)
    res += fscanf(fp, " %lg", &t->rgpc[i]);
  if (res != t->sp.N)
    FATAL("cmaes_resume_distribution(): pc: dimensions differ",0,0,0);

  /* find next "sigma" entry */
  while (1) {
    if ((res = fscanf(fp, " sigma %lg", &d)) == EOF)
      FATAL("cmaes_resume_distribution(): 'sigma' not found",0,0,0);
    else if (res==0)
      fscanf(fp, " %*s");
    else if(res > 0)
      break;
  }
  t->sigma = d;

  /* find next entry "covariance matrix" */
  while (1) {
    if ((res = fscanf(fp, " covariance matrix %lg", &d)) == EOF)
      FATAL("cmaes_resume_distribution(): 'covariance matrix' not found",0,0,0);
    else if (res==0)
      fscanf(fp, " %*s");
    else if(res > 0)
      break;
  }
  /* read C */
  t->C[0][0] = d; res = 1;
  for (i = 1; i < t->sp.N; ++i)
    for (j = 0; j <= i; ++j)
      res += fscanf(fp, " %lg", &t->C[i][j]);
  if (res != (t->sp.N*t->sp.N+t->sp.N)/2)
    FATAL("cmaes_resume_distribution(): C: dimensions differ",0,0,0);

  t->flgIniphase = 0;
  t->flgEigensysIsUptodate = 0;
  t->flgresumedone = 1;
  cmaes_UpdateEigensystem(t, 1);

} /* cmaes_resume_distribution() */
/* --------------------------------------------------------- */
/* --------------------------------------------------------- */

void
cmaes_exit(cmaes_t *t)
{
  int i, N = t->sp.N;
  t->state = -1; /* not really useful at the moment */
  free( t->rgpc);
  free( t->rgps);
  free( t->rgdTmp);
  free( t->rgBDz);
  free( --t->rgxmean);
  free( --t->rgxold);
  free( --t->rgxbestever);
  free( --t->rgout);
  free( t->rgD);
  for (i = 0; i < N; ++i) {
    free( t->C[i]);
    free( t->B[i]);
  }
  for (i = 0; i < t->sp.lambda; ++i)
    free( --t->rgrgx[i]);
  free( t->rgrgx);
  free( t->C);
  free( t->B);
  free( t->index);
  free( t->publicFitness);
  free( --t->rgFuncValue);
  free( --t->arFuncValueHist);
  random_exit (&t->rand);
  readpara_exit (&t->sp);
} /* cmaes_exit() */


/* --------------------------------------------------------- */
/* --------------------------------------------------------- */
double const *
cmaes_SetMean(cmaes_t *t, const double *xmean)
/*
 * Distribution mean could be changed before SamplePopulation().
 * This might lead to unexpected behaviour if done repeatedly.
 */
{
  int i, N=t->sp.N;

  if (t->state >= 1 && t->state < 3)
    FATAL("cmaes_SetMean: mean cannot be set inbetween the calls of ",
          "SamplePopulation and UpdateDistribution",0,0);

  if (xmean != NULL && xmean != t->rgxmean)
    for(i = 0; i < N; ++i)
      t->rgxmean[i] = xmean[i];
  else
    xmean = t->rgxmean;

  return xmean;
}

/* --------------------------------------------------------- */
/* --------------------------------------------------------- */
double * const *
cmaes_SamplePopulation(cmaes_t *t)
{
  int iNk, i, j, N=t->sp.N;
  int flgdiag = ((t->sp.diagonalCov == 1) || (t->sp.diagonalCov >= t->gen));
  double sum;
  double const *xmean = t->rgxmean;

  /* cmaes_SetMean(t, xmean); * xmean could be changed at this point */

  /* calculate eigensystem  */
  if (!t->flgEigensysIsUptodate) {
    if (!flgdiag)
      cmaes_UpdateEigensystem(t, 0);
    else {
        for (i = 0; i < N; ++i)
          t->rgD[i] = sqrt(t->C[i][i]);
        t->minEW = douSquare(rgdouMin(t->rgD, N));
        t->maxEW = douSquare(rgdouMax(t->rgD, N));
        t->flgEigensysIsUptodate = 1;
        timings_start(&t->eigenTimings);
      }
  }

  /* treat minimal standard deviations and numeric problems */
  TestMinStdDevs(t);

  for (iNk = 0; iNk < t->sp.lambda; ++iNk)
    { /* generate scaled random vector (D * z)    */
      for (i = 0; i < N; ++i)
        if (flgdiag)
          t->rgrgx[iNk][i] = xmean[i] + t->sigma * t->rgD[i] * random_Gauss(&t->rand);
        else
          t->rgdTmp[i] = t->rgD[i] * random_Gauss(&t->rand);
      if (!flgdiag)
        /* add mutation (sigma * B * (D*z)) */
        for (i = 0; i < N; ++i) {
          for (j = 0, sum = 0.; j < N; ++j)
            sum += t->B[i][j] * t->rgdTmp[j];
          t->rgrgx[iNk][i] = xmean[i] + t->sigma * sum;
        }
    }
  if(t->state == 3 || t->gen == 0)
    ++t->gen;
  t->state = 1;

  return(t->rgrgx);
} /* SamplePopulation() */

/* --------------------------------------------------------- */
/* --------------------------------------------------------- */
double const *
cmaes_ReSampleSingle_old( cmaes_t *t, double *rgx)
{
  int i, j, N=t->sp.N;
  double sum;

  if (rgx == NULL)
    FATAL("cmaes_ReSampleSingle(): Missing input double *x",0,0,0);

  for (i = 0; i < N; ++i)
    t->rgdTmp[i] = t->rgD[i] * random_Gauss(&t->rand);
  /* add mutation (sigma * B * (D*z)) */
  for (i = 0; i < N; ++i) {
    for (j = 0, sum = 0.; j < N; ++j)
      sum += t->B[i][j] * t->rgdTmp[j];
    rgx[i] = t->rgxmean[i] + t->sigma * sum;
  }
  return rgx;
}

/* --------------------------------------------------------- */
/* --------------------------------------------------------- */
double * const *
cmaes_ReSampleSingle( cmaes_t *t, int iindex)
{
  int i, j, N=t->sp.N;
  double *rgx;
  double sum;
  static char s[99];

  if (iindex < 0 || iindex >= t->sp.lambda) {
    sprintf(s, "index==%d must be between 0 and %d", iindex, t->sp.lambda);
    FATAL("cmaes_ReSampleSingle(): Population member ",s,0,0);
  }
  rgx = t->rgrgx[iindex];

  for (i = 0; i < N; ++i)
    t->rgdTmp[i] = t->rgD[i] * random_Gauss(&t->rand);
  /* add mutation (sigma * B * (D*z)) */
  for (i = 0; i < N; ++i) {
    for (j = 0, sum = 0.; j < N; ++j)
      sum += t->B[i][j] * t->rgdTmp[j];
    rgx[i] = t->rgxmean[i] + t->sigma * sum;
  }
  return(t->rgrgx);
}

/* --------------------------------------------------------- */
/* --------------------------------------------------------- */
double *
cmaes_SampleSingleInto( cmaes_t *t, double *rgx)
{
  int i, j, N=t->sp.N;
  double sum;

  if (rgx == NULL)
    rgx = new_double(N);

  for (i = 0; i < N; ++i)
    t->rgdTmp[i] = t->rgD[i] * random_Gauss(&t->rand);
  /* add mutation (sigma * B * (D*z)) */
  for (i = 0; i < N; ++i) {
    for (j = 0, sum = 0.; j < N; ++j)
      sum += t->B[i][j] * t->rgdTmp[j];
    rgx[i] = t->rgxmean[i] + t->sigma * sum;
  }
  return rgx;
}

/* --------------------------------------------------------- */
/* --------------------------------------------------------- */
double *
cmaes_PerturbSolutionInto( cmaes_t *t, double *rgx, double const *xmean, double eps)
{
  int i, j, N=t->sp.N;
  double sum;

  if (rgx == NULL)
    rgx = new_double(N);
  if (xmean == NULL)
    FATAL("cmaes_PerturbSolutionInto(): xmean was not given",0,0,0);

  for (i = 0; i < N; ++i)
    t->rgdTmp[i] = t->rgD[i] * random_Gauss(&t->rand);
  /* add mutation (sigma * B * (D*z)) */
  for (i = 0; i < N; ++i) {
    for (j = 0, sum = 0.; j < N; ++j)
      sum += t->B[i][j] * t->rgdTmp[j];
    rgx[i] = xmean[i] + eps * t->sigma * sum;
  }
  return rgx;
}

/* --------------------------------------------------------- */
/* --------------------------------------------------------- */
double *
cmaes_UpdateDistribution( cmaes_t *t, const double *rgFunVal)
{
  int i, j, iNk, hsig, N=t->sp.N;
  int flgdiag = ((t->sp.diagonalCov == 1) || (t->sp.diagonalCov >= t->gen));
  double sum;
  double psxps;

  if(t->state == 3)
    FATAL("cmaes_UpdateDistribution(): You need to call \n",
          "SamplePopulation() before update can take place.",0,0);
  if(rgFunVal == NULL)
    FATAL("cmaes_UpdateDistribution(): ",
          "Fitness function value array input is missing.",0,0);

  if(t->state == 1)  /* function values are delivered here */
    t->countevals += t->sp.lambda;
  else
    ERRORMESSAGE("cmaes_UpdateDistribution(): unexpected state",0,0,0);

  /* assign function values */
  for (i=0; i < t->sp.lambda; ++i)
    t->rgrgx[i][N] = t->rgFuncValue[i] = rgFunVal[i];


  /* Generate index */
  Sorted_index(rgFunVal, t->index, t->sp.lambda);

  /* Test if function values are identical, escape flat fitness */
  if (t->rgFuncValue[t->index[0]] ==
      t->rgFuncValue[t->index[(int)t->sp.lambda/2]]) {
    t->sigma *= exp(0.2+t->sp.cs/t->sp.damps);
    ERRORMESSAGE("Warning: sigma increased due to equal function values\n",
                 "   Reconsider the formulation of the objective function",0,0);
  }

  /* update function value history */
  for(i = (int)*(t->arFuncValueHist-1)-1; i > 0; --i) /* for(i = t->arFuncValueHist[-1]-1; i > 0; --i) */
    t->arFuncValueHist[i] = t->arFuncValueHist[i-1];
  t->arFuncValueHist[0] = rgFunVal[t->index[0]];

  /* update xbestever */
  if (t->rgxbestever[N] > t->rgrgx[t->index[0]][N] || t->gen == 1)
    for (i = 0; i <= N; ++i) {
      t->rgxbestever[i] = t->rgrgx[t->index[0]][i];
      t->rgxbestever[N+1] = t->countevals;
    }

  /* calculate xmean and rgBDz~N(0,C) */
  for (i = 0; i < N; ++i) {
    t->rgxold[i] = t->rgxmean[i];
    t->rgxmean[i] = 0.;
    for (iNk = 0; iNk < t->sp.mu; ++iNk)
      t->rgxmean[i] += t->sp.weights[iNk] * t->rgrgx[t->index[iNk]][i];
    t->rgBDz[i] = sqrt(t->sp.mueff)*(t->rgxmean[i] - t->rgxold[i])/t->sigma;
  }

  /* calculate z := D^(-1) * B^(-1) * rgBDz into rgdTmp */
  for (i = 0; i < N; ++i) {
    if (!flgdiag)
      for (j = 0, sum = 0.; j < N; ++j)
        sum += t->B[j][i] * t->rgBDz[j];
    else
      sum = t->rgBDz[i];
    t->rgdTmp[i] = sum / t->rgD[i];
  }

  /* TODO?: check length of t->rgdTmp and set an upper limit, e.g. 6 stds */
  /* in case of manipulation of arx,
     this can prevent an increase of sigma by several orders of magnitude
     within one step; a five-fold increase in one step can still happen.
  */
  /*
    for (j = 0, sum = 0.; j < N; ++j)
      sum += t->rgdTmp[j] * t->rgdTmp[j];
    if (sqrt(sum) > chiN + 6. * sqrt(0.5)) {
      rgdTmp length should be set to upper bound and hsig should become zero
    }
  */

  /* cumulation for sigma (ps) using B*z */
  for (i = 0; i < N; ++i) {
    if (!flgdiag)
      for (j = 0, sum = 0.; j < N; ++j)
        sum += t->B[i][j] * t->rgdTmp[j];
    else
      sum = t->rgdTmp[i];
    t->rgps[i] = (1. - t->sp.cs) * t->rgps[i] +
      sqrt(t->sp.cs * (2. - t->sp.cs)) * sum;
  }

  /* calculate norm(ps)^2 */
  for (i = 0, psxps = 0.; i < N; ++i)
    psxps += t->rgps[i] * t->rgps[i];

  /* cumulation for covariance matrix (pc) using B*D*z~N(0,C) */
  hsig = sqrt(psxps) / sqrt(1. - pow(1.-t->sp.cs, 2*t->gen)) / t->chiN
    < 1.4 + 2./(N+1);
  for (i = 0; i < N; ++i) {
    t->rgpc[i] = (1. - t->sp.ccumcov) * t->rgpc[i] +
      hsig * sqrt(t->sp.ccumcov * (2. - t->sp.ccumcov)) * t->rgBDz[i];
  }

  /* stop initial phase */
  if (t->flgIniphase &&
      t->gen > douMin(1/t->sp.cs, 1+N/t->sp.mucov))
    {
      if (psxps / t->sp.damps / (1.-pow((1. - t->sp.cs), t->gen))
          < N * 1.05)
        t->flgIniphase = 0;
    }

#if 0
  /* remove momentum in ps, if ps is large and fitness is getting worse */
  /* This is obsolete due to hsig and harmful in a dynamic environment */
  if(psxps/N > 1.5 + 10.*sqrt(2./N)
     && t->arFuncValueHist[0] > t->arFuncValueHist[1]
     && t->arFuncValueHist[0] > t->arFuncValueHist[2]) {
    double tfac = sqrt((1 + douMax(0, log(psxps/N))) * N / psxps);
    for (i=0; i<N; ++i)
      t->rgps[i] *= tfac;
    psxps *= tfac*tfac;
  }
#endif

  /* update of C  */

  Adapt_C2(t, hsig);

  /* Adapt_C(t); not used anymore */

#if 0
  if (t->sp.ccov != 0. && t->flgIniphase == 0) {
    int k;

    t->flgEigensysIsUptodate = 0;

    /* update covariance matrix */
    for (i = 0; i < N; ++i)
      for (j = 0; j <=i; ++j) {
        t->C[i][j] = (1 - t->sp.ccov) * t->C[i][j]
          + t->sp.ccov * (1./t->sp.mucov)
            * (t->rgpc[i] * t->rgpc[j]
               + (1-hsig)*t->sp.ccumcov*(2.-t->sp.ccumcov) * t->C[i][j]);
        for (k = 0; k < t->sp.mu; ++k) /* additional rank mu update */
          t->C[i][j] += t->sp.ccov * (1-1./t->sp.mucov) * t->sp.weights[k]
            * (t->rgrgx[t->index[k]][i] - t->rgxold[i])
            * (t->rgrgx[t->index[k]][j] - t->rgxold[j])
            / t->sigma / t->sigma;
      }
  }
#endif


  /* update of sigma */
  t->sigma *= exp(((sqrt(psxps)/t->chiN)-1.)*t->sp.cs/t->sp.damps);

  t->state = 3;

  return (t->rgxmean);

} /* cmaes_UpdateDistribution() */


/* --------------------------------------------------------- */
/* --------------------------------------------------------- */
static void
Adapt_C2(cmaes_t *t, int hsig)
{
  int i, j, k, N=t->sp.N;
  int flgdiag = ((t->sp.diagonalCov == 1) || (t->sp.diagonalCov >= t->gen));

  if (t->sp.ccov != 0. && t->flgIniphase == 0) {

    /* definitions for speeding up inner-most loop */
    double ccov1 = douMin(t->sp.ccov * (1./t->sp.mucov) * (flgdiag ? (N+1.5) / 3. : 1.), 1.);
    double ccovmu = douMin(t->sp.ccov * (1-1./t->sp.mucov)* (flgdiag ? (N+1.5) / 3. : 1.), 1.-ccov1);
    double sigmasquare = t->sigma * t->sigma;

    t->flgEigensysIsUptodate = 0;

    /* update covariance matrix */
    for (i = 0; i < N; ++i)
      for (j = flgdiag ? i : 0; j <= i; ++j) {
        t->C[i][j] = (1 - ccov1 - ccovmu) * t->C[i][j]
          + ccov1
            * (t->rgpc[i] * t->rgpc[j]
               + (1-hsig)*t->sp.ccumcov*(2.-t->sp.ccumcov) * t->C[i][j]);
        for (k = 0; k < t->sp.mu; ++k) { /* additional rank mu update */
          t->C[i][j] += ccovmu * t->sp.weights[k]
            * (t->rgrgx[t->index[k]][i] - t->rgxold[i])
            * (t->rgrgx[t->index[k]][j] - t->rgxold[j])
            / sigmasquare;
        }
      }
    /* update maximal and minimal diagonal value */
    t->maxdiagC = t->mindiagC = t->C[0][0];
    for (i = 1; i < N; ++i) {
      if (t->maxdiagC < t->C[i][i])
        t->maxdiagC = t->C[i][i];
      else if (t->mindiagC > t->C[i][i])
        t->mindiagC = t->C[i][i];
    }
  } /* if ccov... */
}


/* --------------------------------------------------------- */
/* --------------------------------------------------------- */
static void
TestMinStdDevs(cmaes_t *t)
  /* increases sigma */
{
  int i, N = t->sp.N;
  if (t->sp.rgDiffMinChange == NULL)
    return;

  for (i = 0; i < N; ++i)
    while (t->sigma * sqrt(t->C[i][i]) < t->sp.rgDiffMinChange[i])
      t->sigma *= exp(0.05+t->sp.cs/t->sp.damps);

} /* cmaes_TestMinStdDevs() */


/* --------------------------------------------------------- */
/* --------------------------------------------------------- */
void cmaes_WriteToFile(cmaes_t *t, const char *key, const char *name)
{
  cmaes_WriteToFileAW(t, key, name, "a"); /* default is append */
}

/* --------------------------------------------------------- */
/* --------------------------------------------------------- */
void cmaes_WriteToFileAW(cmaes_t *t, const char *key, const char *name,
                         char *appendwrite)
{
  char *s = "tmpcmaes.dat";
  FILE *fp;

  if (name == NULL)
    name = s;

  fp = fopen( name, appendwrite);

  if(fp == NULL) {
    ERRORMESSAGE("cmaes_WriteToFile(): could not open '", name,
                 "' with flag ", appendwrite);
    return;
  }

  if (appendwrite[0] == 'w') {
    /* write a header line, very rudimentary */
    fprintf(fp, "%% # %s (randomSeed=%d, %s)\n", key, t->sp.seed, getTimeStr());
  } else
    if (t->gen > 0 || strncmp(name, "outcmaesfit", 11) != 0)
      cmaes_WriteToFilePtr(t, key, fp); /* do not write fitness for gen==0 */

  fclose(fp);

} /* WriteToFile */

/* --------------------------------------------------------- */
void cmaes_WriteToFilePtr(cmaes_t *t, const char *key, FILE *fp)

/* this hack reads key words from input key for data to be written to
 * a file, see file signals.par as input file. The length of the keys
 * is mostly fixed, see key += number in the code! If the key phrase
 * does not match the expectation the output might be strange.  for
 * cmaes_t *t == NULL it solely prints key as a header line. Input key
 * must be zero terminated.
 */
{
  int i, k, N=(t ? t->sp.N : 0);
  char const *keyend, *keystart;
  char *s = "few";
  if (key == NULL)
    key = s;
  keystart = key; /* for debugging purpose */
  keyend = key + strlen(key);

  while (key < keyend)
    {
      if (strncmp(key, "axisratio", 9) == 0)
        {
          fprintf(fp, "%.2e", sqrt(t->maxEW/t->minEW));
          while (*key != '+' && *key != '\0' && key < keyend)
           ++key;
          fprintf(fp, "%c", (*key=='+') ? '\t':'\n');
        }
      if (strncmp(key, "idxminSD", 8) == 0)
        {
          int mini=0; for(i=N-1;i>0;--i) if(t->mindiagC==t->C[i][i]) mini=i;
          fprintf(fp, "%d", mini+1);
          while (*key != '+' && *key != '\0' && key < keyend)
           ++key;
          fprintf(fp, "%c", (*key=='+') ? '\t':'\n');
        }
      if (strncmp(key, "idxmaxSD", 8) == 0)
        {
          int maxi=0; for(i=N-1;i>0;--i) if(t->maxdiagC==t->C[i][i]) maxi=i;
          fprintf(fp, "%d", maxi+1);
          while (*key != '+' && *key != '\0' && key < keyend)
           ++key;
          fprintf(fp, "%c", (*key=='+') ? '\t':'\n');
        }
      /* new coordinate system == all eigenvectors */
      if (strncmp(key, "B", 1) == 0)
        {
          /* int j, index[N]; */
          int j, *iindex=(int*)(new_void(N,sizeof(int))); /* MT */
          Sorted_index(t->rgD, iindex, N); /* should not be necessary, see end of QLalgo2 */
          /* One eigenvector per row, sorted: largest eigenvalue first */
          for (i = 0; i < N; ++i)
            for (j = 0; j < N; ++j)
              fprintf(fp, "%g%c", t->B[j][iindex[N-1-i]], (j==N-1)?'\n':'\t');
          ++key;
          free(iindex); /* MT */
        }
      /* covariance matrix */
      if (strncmp(key, "C", 1) == 0)
        {
          int j;
          for (i = 0; i < N; ++i)
            for (j = 0; j <= i; ++j)
              fprintf(fp, "%g%c", t->C[i][j], (j==i)?'\n':'\t');
          ++key;
        }
      /* (processor) time (used) since begin of execution */
      if (strncmp(key, "clock", 4) == 0)
        {
          timings_update(&t->eigenTimings);
          fprintf(fp, "%.1f %.1f",  t->eigenTimings.totaltotaltime,
                  t->eigenTimings.tictoctime);
          while (*key != '+' && *key != '\0' && key < keyend)
            ++key;
          fprintf(fp, "%c", (*key=='+') ? '\t':'\n');
        }
      /* ratio between largest and smallest standard deviation */
      if (strncmp(key, "stddevratio", 11) == 0) /* std dev in coordinate axes */
        {
          fprintf(fp, "%g", sqrt(t->maxdiagC/t->mindiagC));
          while (*key != '+' && *key != '\0' && key < keyend)
           ++key;
          fprintf(fp, "%c", (*key=='+') ? '\t':'\n');
        }
      /* standard deviations in coordinate directions (sigma*sqrt(C[i,i])) */
      if (strncmp(key, "coorstddev", 10) == 0
          || strncmp(key, "stddev", 6) == 0) /* std dev in coordinate axes */
        {
          for (i = 0; i < N; ++i)
            fprintf(fp, "%s%g", (i==0) ? "":"\t", t->sigma*sqrt(t->C[i][i]));
          while (*key != '+' && *key != '\0' && key < keyend)
           ++key;
          fprintf(fp, "%c", (*key=='+') ? '\t':'\n');
        }
      /* diagonal of D == roots of eigenvalues, sorted */
      if (strncmp(key, "diag(D)", 7) == 0)
        {
          for (i = 0; i < N; ++i)
            t->rgdTmp[i] = t->rgD[i];
          qsort(t->rgdTmp, (unsigned) N, sizeof(double), &SignOfDiff); /* superfluous */
          for (i = 0; i < N; ++i)
            fprintf(fp, "%s%g", (i==0) ? "":"\t", t->rgdTmp[i]);
          while (*key != '+' && *key != '\0' && key < keyend)
            ++key;
          fprintf(fp, "%c", (*key=='+') ? '\t':'\n');
        }
      if (strncmp(key, "dim", 3) == 0)
        {
          fprintf(fp, "%d", N);
          while (*key != '+' && *key != '\0' && key < keyend)
            ++key;
          fprintf(fp, "%c", (*key=='+') ? '\t':'\n');
        }
      if (strncmp(key, "eval", 4) == 0)
        {
          fprintf(fp, "%.0f", t->countevals);
          while (*key != '+' && *key != '\0' && key < keyend)
           ++key;
          fprintf(fp, "%c", (*key=='+') ? '\t':'\n');
        }
      if (strncmp(key, "few(diag(D))", 12) == 0)/* between four and six axes */
        {
          int add = (int)(0.5 + (N + 1.) / 5.);
          for (i = 0; i < N; ++i)
            t->rgdTmp[i] = t->rgD[i];
          qsort(t->rgdTmp, (unsigned) N, sizeof(double), &SignOfDiff);
          for (i = 0; i < N-1; i+=add)        /* print always largest */
            fprintf(fp, "%s%g", (i==0) ? "":"\t", t->rgdTmp[N-1-i]);
          fprintf(fp, "\t%g\n", t->rgdTmp[0]);        /* and smallest */
          break; /* number of printed values is not determined */
        }
      if (strncmp(key, "fewinfo", 7) == 0) {
        fprintf(fp," Iter Fevals  Function Value          Sigma   ");
        fprintf(fp, "MaxCoorDev MinCoorDev AxisRatio   MinDii      Time in eig\n");
        while (*key != '+' && *key != '\0' && key < keyend)
          ++key;
      }
      if (strncmp(key, "few", 3) == 0) {
        fprintf(fp, " %4.0f ", t->gen);
        fprintf(fp, " %5.0f ", t->countevals);
        fprintf(fp, "%.15e", t->rgFuncValue[t->index[0]]);
        fprintf(fp, "  %.2e  %.2e %.2e", t->sigma, t->sigma*sqrt(t->maxdiagC),
                t->sigma*sqrt(t->mindiagC));
        fprintf(fp, "  %.2e  %.2e", sqrt(t->maxEW/t->minEW), sqrt(t->minEW));
        while (*key != '+' && *key != '\0' && key < keyend)
          ++key;
        fprintf(fp, "%c", (*key=='+') ? '\t':'\n');
      }
      if (strncmp(key, "funval", 6) == 0 || strncmp(key, "fitness", 6) == 0)
        {
          fprintf(fp, "%.15e", t->rgFuncValue[t->index[0]]);
          while (*key != '+' && *key != '\0' && key < keyend)
            ++key;
          fprintf(fp, "%c", (*key=='+') ? '\t':'\n');
        }
      if (strncmp(key, "fbestever", 9) == 0)
        {
          fprintf(fp, "%.15e", t->rgxbestever[N]); /* f-value */
          while (*key != '+' && *key != '\0' && key < keyend)
            ++key;
          fprintf(fp, "%c", (*key=='+') ? '\t':'\n');
        }
      if (strncmp(key, "fmedian", 7) == 0)
        {
          fprintf(fp, "%.15e", t->rgFuncValue[t->index[(int)(t->sp.lambda/2)]]);
          while (*key != '+' && *key != '\0' && key < keyend)
            ++key;
          fprintf(fp, "%c", (*key=='+') ? '\t':'\n');
        }
      if (strncmp(key, "fworst", 6) == 0)
        {
          fprintf(fp, "%.15e", t->rgFuncValue[t->index[t->sp.lambda-1]]);
          while (*key != '+' && *key != '\0' && key < keyend)
            ++key;
          fprintf(fp, "%c", (*key=='+') ? '\t':'\n');
        }
      if (strncmp(key, "arfunval", 8) == 0 || strncmp(key, "arfitness", 8) == 0)
        {
          for (i = 0; i < N; ++i)
            fprintf(fp, "%s%.10e", (i==0) ? "" : "\t",
                    t->rgFuncValue[t->index[i]]);
          while (*key != '+' && *key != '\0' && key < keyend)
            ++key;
          fprintf(fp, "%c", (*key=='+') ? '\t':'\n');
        }
      if (strncmp(key, "gen", 3) == 0)
        {
          fprintf(fp, "%.0f", t->gen);
          while (*key != '+' && *key != '\0' && key < keyend)
            ++key;
          fprintf(fp, "%c", (*key=='+') ? '\t':'\n');
        }
      if (strncmp(key, "iter", 4) == 0)
        {
          fprintf(fp, "%.0f", t->gen);
          while (*key != '+' && *key != '\0' && key < keyend)
           ++key;
          fprintf(fp, "%c", (*key=='+') ? '\t':'\n');
        }
      if (strncmp(key, "sigma", 5) == 0)
        {
          fprintf(fp, "%.4e", t->sigma);
          while (*key != '+' && *key != '\0' && key < keyend)
           ++key;
          fprintf(fp, "%c", (*key=='+') ? '\t':'\n');
        }
      if (strncmp(key, "minSD", 5) == 0) /* minimal standard deviation */
        {
          fprintf(fp, "%.4e", sqrt(t->mindiagC));
          while (*key != '+' && *key != '\0' && key < keyend)
            ++key;
          fprintf(fp, "%c", (*key=='+') ? '\t':'\n');
        }
      if (strncmp(key, "maxSD", 5) == 0)
        {
          fprintf(fp, "%.4e", sqrt(t->maxdiagC));
          while (*key != '+' && *key != '\0' && key < keyend)
            ++key;
          fprintf(fp, "%c", (*key=='+') ? '\t':'\n');
        }
      if (strncmp(key, "mindii", 6) == 0)
        {
          fprintf(fp, "%.4e", sqrt(t->minEW));
          while (*key != '+' && *key != '\0' && key < keyend)
           ++key;
          fprintf(fp, "%c", (*key=='+') ? '\t':'\n');
        }
      if (strncmp(key, "0", 1) == 0)
        {
          fprintf(fp, "0");
          ++key;
          fprintf(fp, "%c", (*key=='+') ? '\t':'\n');
        }
      if (strncmp(key, "lambda", 6) == 0)
        {
          fprintf(fp, "%d", t->sp.lambda);
          while (*key != '+' && *key != '\0' && key < keyend)
            ++key;
          fprintf(fp, "%c", (*key=='+') ? '\t':'\n');
        }
      if (strncmp(key, "N", 1) == 0)
        {
          fprintf(fp, "%d", N);
          ++key;
          fprintf(fp, "%c", (*key=='+') ? '\t':'\n');
        }
      if (strncmp(key, "resume", 6) == 0)
        {
          fprintf(fp, "\n# resume %d\n", N);
          fprintf(fp, "xmean\n");
          cmaes_WriteToFilePtr(t, "xmean", fp);
          fprintf(fp, "path for sigma\n");
          for(i=0; i<N; ++i)
            fprintf(fp, "%g%s", t->rgps[i], (i==N-1) ? "\n":"\t");
          fprintf(fp, "path for C\n");
          for(i=0; i<N; ++i)
            fprintf(fp, "%g%s", t->rgpc[i], (i==N-1) ? "\n":"\t");
          fprintf(fp, "sigma %g\n", t->sigma);
          /* note than B and D might not be up-to-date */
          fprintf(fp, "covariance matrix\n");
          cmaes_WriteToFilePtr(t, "C", fp);
          while (*key != '+' && *key != '\0' && key < keyend)
            ++key;
        }
      if (strncmp(key, "xbest", 5) == 0) { /* best x in recent generation */
        for(i=0; i<N; ++i)
          fprintf(fp, "%s%g", (i==0) ? "":"\t", t->rgrgx[t->index[0]][i]);
        while (*key != '+' && *key != '\0' && key < keyend)
          ++key;
        fprintf(fp, "%c", (*key=='+') ? '\t':'\n');
      }
      if (strncmp(key, "xmean", 5) == 0) {
        for(i=0; i<N; ++i)
          fprintf(fp, "%s%g", (i==0) ? "":"\t", t->rgxmean[i]);
        while (*key != '+' && *key != '\0' && key < keyend)
          ++key;
        fprintf(fp, "%c", (*key=='+') ? '\t':'\n');
      }
      if (strncmp(key, "all", 3) == 0)
        {
          time_t ti = time(NULL);
          fprintf(fp, "\n# --------- %s\n", asctime(localtime(&ti)));
          fprintf(fp, " N %d\n", N);
          fprintf(fp, " seed %d\n", t->sp.seed);
          fprintf(fp, "function evaluations %.0f\n", t->countevals);
          fprintf(fp, "elapsed (CPU) time [s] %.2f\n", t->eigenTimings.totaltotaltime);
          fprintf(fp, "function value f(x)=%g\n", t->rgrgx[t->index[0]][N]);
          fprintf(fp, "maximal standard deviation %g\n", t->sigma*sqrt(t->maxdiagC));
          fprintf(fp, "minimal standard deviation %g\n", t->sigma*sqrt(t->mindiagC));
          fprintf(fp, "sigma %g\n", t->sigma);
          fprintf(fp, "axisratio %g\n", rgdouMax(t->rgD, N)/rgdouMin(t->rgD, N));
          fprintf(fp, "xbestever found after %.0f evaluations, function value %g\n",
                  t->rgxbestever[N+1], t->rgxbestever[N]);
          for(i=0; i<N; ++i)
            fprintf(fp, " %12g%c", t->rgxbestever[i],
                    (i%5==4||i==N-1)?'\n':' ');
          fprintf(fp, "xbest (of last generation, function value %g)\n",
                  t->rgrgx[t->index[0]][N]);
          for(i=0; i<N; ++i)
            fprintf(fp, " %12g%c", t->rgrgx[t->index[0]][i],
                    (i%5==4||i==N-1)?'\n':' ');
          fprintf(fp, "xmean \n");
          for(i=0; i<N; ++i)
            fprintf(fp, " %12g%c", t->rgxmean[i],
                    (i%5==4||i==N-1)?'\n':' ');
          fprintf(fp, "Standard deviation of coordinate axes (sigma*sqrt(diag(C)))\n");
          for(i=0; i<N; ++i)
            fprintf(fp, " %12g%c", t->sigma*sqrt(t->C[i][i]),
                    (i%5==4||i==N-1)?'\n':' ');
          fprintf(fp, "Main axis lengths of mutation ellipsoid (sigma*diag(D))\n");
          for (i = 0; i < N; ++i)
            t->rgdTmp[i] = t->rgD[i];
          qsort(t->rgdTmp, (unsigned) N, sizeof(double), &SignOfDiff);
          for(i=0; i<N; ++i)
            fprintf(fp, " %12g%c", t->sigma*t->rgdTmp[N-1-i],
                    (i%5==4||i==N-1)?'\n':' ');
          fprintf(fp, "Longest axis (b_i where d_ii=max(diag(D))\n");
          k = MaxIdx(t->rgD, N);
          for(i=0; i<N; ++i)
            fprintf(fp, " %12g%c", t->B[i][k], (i%5==4||i==N-1)?'\n':' ');
          fprintf(fp, "Shortest axis (b_i where d_ii=max(diag(D))\n");
          k = MinIdx(t->rgD, N);
          for(i=0; i<N; ++i)
            fprintf(fp, " %12g%c", t->B[i][k], (i%5==4||i==N-1)?'\n':' ');
          while (*key != '+' && *key != '\0' && key < keyend)
            ++key;
        } /* "all" */

#if 0 /* could become generic part */
      s0 = key;
      d = cmaes_Get(t, key); /* TODO find way to detect whether key was found */
      if (key == s0) /* this does not work, is always true */
        {
          /* write out stuff, problem: only generic format is available */
          /* move in key until "+" or end */
        }
#endif

      if (*key == '\0')
        break;
      else if (*key != '+') { /* last key was not recognized */
        ERRORMESSAGE("cmaes_t:WriteToFilePtr(): unrecognized key '", key, "'", 0);
        while (*key != '+' && *key != '\0' && key < keyend)
          ++key;
      }
      while (*key == '+')
        ++key;
    } /* while key < keyend */

  if (key > keyend)
    FATAL("cmaes_t:WriteToFilePtr(): BUG regarding key sequence",0,0,0);

} /* WriteToFilePtr */

/* --------------------------------------------------------- */
double
cmaes_Get( cmaes_t *t, char const *s)
{
  int N=t->sp.N;

  if (strncmp(s, "axisratio", 5) == 0) { /* between lengths of longest and shortest principal axis of the distribution ellipsoid */
    return (rgdouMax(t->rgD, N)/rgdouMin(t->rgD, N));
  }
  else if (strncmp(s, "eval", 4) == 0) { /* number of function evaluations */
    return (t->countevals);
  }
  else if (strncmp(s, "fctvalue", 6) == 0
           || strncmp(s, "funcvalue", 6) == 0
           || strncmp(s, "funvalue", 6) == 0
           || strncmp(s, "fitness", 3) == 0) { /* recent best function value */
    return(t->rgFuncValue[t->index[0]]);
  }
  else if (strncmp(s, "fbestever", 7) == 0) { /* ever best function value */
    return(t->rgxbestever[N]);
  }
  else if (strncmp(s, "generation", 3) == 0
           || strncmp(s, "iteration", 4) == 0) {
    return(t->gen);
  }
  else if (strncmp(s, "maxeval", 4) == 0
           || strncmp(s, "MaxFunEvals", 8) == 0
           || strncmp(s, "stopMaxFunEvals", 12) == 0) { /* maximal number of function evaluations */
    return(t->sp.stopMaxFunEvals);
  }
  else if (strncmp(s, "maxgen", 4) == 0
           || strncmp(s, "MaxIter", 7) == 0
           || strncmp(s, "stopMaxIter", 11) == 0) { /* maximal number of generations */
    return(ceil(t->sp.stopMaxIter));
  }
  else if (strncmp(s, "maxaxislength", 5) == 0) { /* sigma * max(diag(D)) */
    return(t->sigma * sqrt(t->maxEW));
  }
  else if (strncmp(s, "minaxislength", 5) == 0) { /* sigma * min(diag(D)) */
    return(t->sigma * sqrt(t->minEW));
  }
  else if (strncmp(s, "maxstddev", 4) == 0) { /* sigma * sqrt(max(diag(C))) */
    return(t->sigma * sqrt(t->maxdiagC));
  }
  else if (strncmp(s, "minstddev", 4) == 0) { /* sigma * sqrt(min(diag(C))) */
    return(t->sigma * sqrt(t->mindiagC));
  }
  else if (strncmp(s, "N", 1) == 0 || strcmp(s, "n") == 0 ||
           strncmp(s, "dimension", 3) == 0) {
    return (N);
  }
  else if (strncmp(s, "lambda", 3) == 0
           || strncmp(s, "samplesize", 8) == 0
           || strncmp(s, "popsize", 7) == 0) { /* sample size, offspring population size */
    return(t->sp.lambda);
  }
  else if (strncmp(s, "sigma", 3) == 0) {
    return(t->sigma);
  }
  FATAL( "cmaes_Get(cmaes_t, char const * s): No match found for s='", s, "'",0);
  return(0);
} /* cmaes_Get() */

/* --------------------------------------------------------- */
double *
cmaes_GetInto( cmaes_t *t, char const *s, double *res)
{
  int i, N = t->sp.N;
  double const * res0 = cmaes_GetPtr(t, s);
  if (res == NULL)
    res = new_double(N);
  for (i = 0; i < N; ++i)
    res[i] = res0[i];
  return res;
}

/* --------------------------------------------------------- */
double *
cmaes_GetNew( cmaes_t *t, char const *s)
{
  return (cmaes_GetInto(t, s, NULL));
}

/* --------------------------------------------------------- */
const double *
cmaes_GetPtr( cmaes_t *t, char const *s)
{
  int i, N=t->sp.N;

  /* diagonal of covariance matrix */
  if (strncmp(s, "diag(C)", 7) == 0) {
    for (i = 0; i < N; ++i)
      t->rgout[i] = t->C[i][i];
    return(t->rgout);
  }
  /* diagonal of axis lengths matrix */
  else if (strncmp(s, "diag(D)", 7) == 0) {
    return(t->rgD);
  }
  /* vector of standard deviations sigma*sqrt(diag(C)) */
  else if (strncmp(s, "stddev", 3) == 0) {
    for (i = 0; i < N; ++i)
      t->rgout[i] = t->sigma * sqrt(t->C[i][i]);
    return(t->rgout);
  }
  /* bestever solution seen so far */
  else if (strncmp(s, "xbestever", 7) == 0)
    return(t->rgxbestever);
  /* recent best solution of the recent population */
  else if (strncmp(s, "xbest", 5) == 0)
    return(t->rgrgx[t->index[0]]);
  /* mean of the recent distribution */
  else if (strncmp(s, "xmean", 1) == 0)
    return(t->rgxmean);

  return(NULL);
}

/* --------------------------------------------------------- */
/* tests stopping criteria
 *   returns a string of satisfied stopping criterion for each line
 *   otherwise NULL
*/
const char *
cmaes_TestForTermination( cmaes_t *t)
{
  double range, fac;
  int iAchse, iKoo;
  int flgdiag = ((t->sp.diagonalCov == 1) || (t->sp.diagonalCov >= t->gen));
  static char sTestOutString[3024];
  char * cp = sTestOutString;
  int i, cTemp, N=t->sp.N;
  cp[0] = '\0';

      /* function value reached */
      if ((t->gen > 1 || t->state > 1) && t->sp.stStopFitness.flg &&
          t->rgFuncValue[t->index[0]] <= t->sp.stStopFitness.val)
        cp += sprintf(cp, "Fitness: function value %7.2e <= stopFitness (%7.2e)\n",
                      t->rgFuncValue[t->index[0]], t->sp.stStopFitness.val);

      /* TolFun */
      range = douMax(rgdouMax(t->arFuncValueHist, (int)douMin(t->gen,*(t->arFuncValueHist-1))),
                     rgdouMax(t->rgFuncValue, t->sp.lambda)) -
        douMin(rgdouMin(t->arFuncValueHist, (int)douMin(t->gen, *(t->arFuncValueHist-1))),
               rgdouMin(t->rgFuncValue, t->sp.lambda));

      if (t->gen > 0 && range <= t->sp.stopTolFun) {
        cp += sprintf(cp,
                      "TolFun: function value differences %7.2e < stopTolFun=%7.2e\n",
                      range, t->sp.stopTolFun);
      }

      /* TolFunHist */
      if (t->gen > *(t->arFuncValueHist-1)) {
        range = rgdouMax(t->arFuncValueHist, (int)*(t->arFuncValueHist-1))
          - rgdouMin(t->arFuncValueHist, (int)*(t->arFuncValueHist-1));
        if (range <= t->sp.stopTolFunHist)
          cp += sprintf(cp,
                        "TolFunHist: history of function value changes %7.2e stopTolFunHist=%7.2e",
                        range, t->sp.stopTolFunHist);
      }

      /* TolX */
      for(i=0, cTemp=0; i<N; ++i) {
        cTemp += (t->sigma * sqrt(t->C[i][i]) < t->sp.stopTolX) ? 1 : 0;
        cTemp += (t->sigma * t->rgpc[i] < t->sp.stopTolX) ? 1 : 0;
      }
      if (cTemp == 2*N) {
        cp += sprintf(cp,
                      "TolX: object variable changes below %7.2e \n",
                      t->sp.stopTolX);
      }

      /* TolUpX */
      for(i=0; i<N; ++i) {
        if (t->sigma * sqrt(t->C[i][i]) > t->sp.stopTolUpXFactor * t->sp.rgInitialStds[i])
          break;
      }
      if (i < N) {
        cp += sprintf(cp,
                      "TolUpX: standard deviation increased by more than %7.2e, larger initial standard deviation recommended \n",
                      t->sp.stopTolUpXFactor);
      }

      /* Condition of C greater than dMaxSignifKond */
      if (t->maxEW >= t->minEW * t->dMaxSignifKond) {
        cp += sprintf(cp,
                      "ConditionNumber: maximal condition number %7.2e reached. maxEW=%7.2e,minEW=%7.2e,maxdiagC=%7.2e,mindiagC=%7.2e\n",
                      t->dMaxSignifKond, t->maxEW, t->minEW, t->maxdiagC, t->mindiagC);
      } /* if */

      /* Principal axis i has no effect on xmean, ie.
         x == x + 0.1 * sigma * rgD[i] * B[i] */
      if (!flgdiag) {
        for (iAchse = 0; iAchse < N; ++iAchse)
          {
            fac = 0.1 * t->sigma * t->rgD[iAchse];
            for (iKoo = 0; iKoo < N; ++iKoo){
              if (t->rgxmean[iKoo] != t->rgxmean[iKoo] + fac * t->B[iKoo][iAchse])
                break;
            }
            if (iKoo == N)
              {
                /* t->sigma *= exp(0.2+t->sp.cs/t->sp.damps); */
                cp += sprintf(cp,
                              "NoEffectAxis: standard deviation 0.1*%7.2e in principal axis %d without effect\n",
                              fac/0.1, iAchse);
                break;
              } /* if (iKoo == N) */
          } /* for iAchse             */
      } /* if flgdiag */
      /* Component of xmean is not changed anymore */
      for (iKoo = 0; iKoo < N; ++iKoo)
        {
          if (t->rgxmean[iKoo] == t->rgxmean[iKoo] +
              0.2*t->sigma*sqrt(t->C[iKoo][iKoo]))
            {
              /* t->C[iKoo][iKoo] *= (1 + t->sp.ccov); */
              /* flg = 1; */
              cp += sprintf(cp,
                            "NoEffectCoordinate: standard deviation 0.2*%7.2e in coordinate %d without effect\n",
                            t->sigma*sqrt(t->C[iKoo][iKoo]), iKoo);
              break;
            }

        } /* for iKoo */
      /* if (flg) t->sigma *= exp(0.05+t->sp.cs/t->sp.damps); */

      if(t->countevals >= t->sp.stopMaxFunEvals)
        cp += sprintf(cp, "MaxFunEvals: conducted function evaluations %.0f >= %g\n",
                      t->countevals, t->sp.stopMaxFunEvals);
      if(t->gen >= t->sp.stopMaxIter)
        cp += sprintf(cp, "MaxIter: number of iterations %.0f >= %g\n",
                      t->gen, t->sp.stopMaxIter);
      if(t->flgStop)
        cp += sprintf(cp, "Manual: stop signal read\n");

#if 0
  else if (0) {
    for(i=0, cTemp=0; i<N; ++i) {
      cTemp += (sigma * sqrt(C[i][i]) < stopdx) ? 1 : 0;
      cTemp += (sigma * rgpc[i] < stopdx) ? 1 : 0;
    }
    if (cTemp == 2*N)
      flgStop = 1;
  }
#endif

  if (cp - sTestOutString>320)
    ERRORMESSAGE("Bug in cmaes_t:Test(): sTestOutString too short",0,0,0);

  if (cp != sTestOutString) {
    return sTestOutString;
  }

  return(NULL);

} /* cmaes_Test() */

/* --------------------------------------------------------- */
void cmaes_ReadSignals(cmaes_t *t, char const *filename)
{
  const char *s = "signals.par";
  FILE *fp;
  if (filename == NULL)
    filename = s;
  fp = fopen( filename, "r");
  if(fp == NULL) {
    return;
  }
  cmaes_ReadFromFilePtr( t, fp);
  fclose(fp);
}
/* --------------------------------------------------------- */
void cmaes_ReadFromFilePtr( cmaes_t *t, FILE *fp)
/* reading commands e.g. from signals.par file
*/
{
  char *keys[15];
  char s[199], sin1[99], sin2[129], sin3[99], sin4[99];
  int ikey, ckeys, nb;
  double d;
  static int flglockprint = 0;
  static int flglockwrite = 0;
  static long countiterlastwritten;
  static long maxdiffitertowrite; /* to prevent long gaps at the beginning */
  int flgprinted = 0;
  int flgwritten = 0;
  double deltaprinttime = time(NULL)-t->printtime; /* using clock instead might not be a good */
  double deltawritetime = time(NULL)-t->writetime; /* idea as disc time is not CPU time? */
  double deltaprinttimefirst = t->firstprinttime ? time(NULL)-t->firstprinttime : 0; /* time is in seconds!? */
  double deltawritetimefirst = t->firstwritetime ? time(NULL)-t->firstwritetime : 0;
  if (countiterlastwritten > t->gen) { /* probably restarted */
    maxdiffitertowrite = 0;
    countiterlastwritten = 0;
  }

  keys[0] = " stop%98s %98s";        /* s=="now" or eg "MaxIter+" %lg"-number */
                                     /* works with and without space */
  keys[1] = " print %98s %98s";       /* s==keyword for WriteFile */
  keys[2] = " write %98s %128s %98s"; /* s1==keyword, s2==filename */
  keys[3] = " check%98s %98s";
  keys[4] = " maxTimeFractionForEigendecompostion %98s";
  ckeys = 5;
  strcpy(sin2, "tmpcmaes.dat");

  if (cmaes_TestForTermination(t))
    {
      deltaprinttime = time(NULL); /* forces printing */
      deltawritetime = time(NULL);
    }
  while(fgets(s, sizeof(s), fp) != NULL)
    {
      if (s[0] == '#' || s[0] == '%') /* skip comments  */
        continue;
      sin1[0] = sin2[0] = sin3[0] = sin4[0] = '\0';
      for (ikey=0; ikey < ckeys; ++ikey)
        {
          if((nb=sscanf(s, keys[ikey], sin1, sin2, sin3, sin4)) >= 1)
            {
              switch(ikey) {
              case 0 : /* "stop", reads "stop now" or eg. stopMaxIter */
                if (strncmp(sin1, "now", 3) == 0)
                  t->flgStop = 1;
                else if (strncmp(sin1, "MaxFunEvals", 11) == 0) {
                  if (sscanf(sin2, " %lg", &d) == 1)
                    t->sp.stopMaxFunEvals = d;
                }
                else if (strncmp(sin1, "MaxIter", 4) == 0) {
                  if (sscanf(sin2, " %lg", &d) == 1)
                    t->sp.stopMaxIter = d;
                }
                else if (strncmp(sin1, "Fitness", 7) == 0) {
                  if (sscanf(sin2, " %lg", &d) == 1)
                    {
                      t->sp.stStopFitness.flg = 1;
                      t->sp.stStopFitness.val = d;
                    }
                }
                else if (strncmp(sin1, "TolFunHist", 10) == 0) {
                  if (sscanf(sin2, " %lg", &d) == 1)
                    t->sp.stopTolFunHist = d;
                }
                else if (strncmp(sin1, "TolFun", 6) == 0) {
                  if (sscanf(sin2, " %lg", &d) == 1)
                    t->sp.stopTolFun = d;
                }
                else if (strncmp(sin1, "TolX", 4) == 0) {
                  if (sscanf(sin2, " %lg", &d) == 1)
                    t->sp.stopTolX = d;
                }
                else if (strncmp(sin1, "TolUpXFactor", 4) == 0) {
                  if (sscanf(sin2, " %lg", &d) == 1)
                    t->sp.stopTolUpXFactor = d;
                }
                break;
              case 1 : /* "print" */
                d = 1; /* default */
                if (sscanf(sin2, "%lg", &d) < 1 && deltaprinttimefirst < 1)
                  d = 0; /* default at first time */
                if (deltaprinttime >= d && !flglockprint) {
                  cmaes_WriteToFilePtr(t, sin1, stdout);
                  flgprinted = 1;
                }
                if(d < 0)
                  flglockprint += 2;
                break;
              case 2 : /* "write" */
                /* write header, before first generation */
                if (t->countevals < t->sp.lambda && t->flgresumedone == 0)
                  cmaes_WriteToFileAW(t, sin1, sin2, "w"); /* overwrite */
                d = 0.9; /* default is one with smooth increment of gaps */
                if (sscanf(sin3, "%lg", &d) < 1 && deltawritetimefirst < 2)
                  d = 0; /* default is zero for the first second */
                if(d < 0)
                  flglockwrite += 2;
                if (!flglockwrite) {
                  if (deltawritetime >= d) {
                    cmaes_WriteToFile(t, sin1, sin2);
                    flgwritten = 1;
                  } else if (d < 1
                             && t->gen-countiterlastwritten > maxdiffitertowrite) {
                    cmaes_WriteToFile(t, sin1, sin2);
                    flgwritten = 1;
                  }
                }
                break;
              case 3 : /* check, checkeigen 1 or check eigen 1 */
                if (strncmp(sin1, "eigen", 5) == 0) {
                  if (sscanf(sin2, " %lg", &d) == 1) {
                    if (d > 0)
                      t->flgCheckEigen = 1;
                    else
                      t->flgCheckEigen = 0;
                  }
                  else
                    t->flgCheckEigen = 0;
                }
                break;
              case 4 : /* maxTimeFractionForEigendecompostion */
                if (sscanf(sin1, " %lg", &d) == 1)
                  t->sp.updateCmode.maxtime = d;
                break;
              default :
                break;
              }
              break; /* for ikey */
            } /* if line contains keyword */
        } /* for each keyword */
    } /* while not EOF of signals.par */
  if (t->writetime == 0)
    t->firstwritetime = time(NULL);
  if (t->printtime == 0)
    t->firstprinttime = time(NULL);

  if (flgprinted)
    t->printtime = time(NULL);
  if (flgwritten) {
    t->writetime = time(NULL);
    if (t->gen-countiterlastwritten > maxdiffitertowrite)
      ++maxdiffitertowrite; /* smooth prolongation of writing gaps/intervals */
    countiterlastwritten = (long int) t->gen;
  }
  --flglockprint;
  --flglockwrite;
  flglockprint = (flglockprint > 0) ? 1 : 0;
  flglockwrite = (flglockwrite > 0) ? 1 : 0;
} /*  cmaes_ReadFromFilePtr */

/* ========================================================= */
static int
Check_Eigen( int N,  double **C, double *diag, double **Q)
/*
   exhaustive test of the output of the eigendecomposition
   needs O(n^3) operations

   writes to error file
   returns number of detected inaccuracies
*/
{
    /* compute Q diag Q^T and Q Q^T to check */
  int i, j, k, res = 0;
  double cc, dd;
  static char s[324];

  for (i=0; i < N; ++i)
    for (j=0; j < N; ++j) {
      for (cc=0.,dd=0., k=0; k < N; ++k) {
        cc += diag[k] * Q[i][k] * Q[j][k];
        dd += Q[i][k] * Q[j][k];
      }
      /* check here, is the normalization the right one? */
      if (fabs(cc - C[i>j?i:j][i>j?j:i])/sqrt(C[i][i]*C[j][j]) > 1e-10
          && fabs(cc - C[i>j?i:j][i>j?j:i]) > 3e-14) {
        sprintf(s, "%d %d: %.17e %.17e, %e",
                i, j, cc, C[i>j?i:j][i>j?j:i], cc-C[i>j?i:j][i>j?j:i]);
        ERRORMESSAGE("cmaes_t:Eigen(): imprecise result detected ",
                     s, 0, 0);
        ++res;
      }
      if (fabs(dd - (i==j)) > 1e-10) {
        sprintf(s, "%d %d %.17e ", i, j, dd);
        ERRORMESSAGE("cmaes_t:Eigen(): imprecise result detected (Q not orthog.)",
                     s, 0, 0);
        ++res;
      }
    }
  return res;
}

/* --------------------------------------------------------- */
/* --------------------------------------------------------- */
void
cmaes_UpdateEigensystem(cmaes_t *t, int flgforce)
{
  int i, N = t->sp.N;

  timings_update(&t->eigenTimings);

  if(flgforce == 0) {
    if (t->flgEigensysIsUptodate == 1)
      return;

    /* return on modulo generation number */
    if (t->sp.updateCmode.flgalways == 0 /* not implemented, always ==0 */
        && t->gen < t->genOfEigensysUpdate + t->sp.updateCmode.modulo
        )
      return;

    /* return on time percentage */
    if (t->sp.updateCmode.maxtime < 1.00
        && t->eigenTimings.tictoctime > t->sp.updateCmode.maxtime * t->eigenTimings.totaltime
        && t->eigenTimings.tictoctime > 0.0002)
      return;
  }
  timings_tic(&t->eigenTimings);

  Eigen( N, t->C, t->rgD, t->B, t->rgdTmp);

  timings_toc(&t->eigenTimings);

  /* find largest and smallest eigenvalue, they are supposed to be sorted anyway */
  t->minEW = rgdouMin(t->rgD, N);
  t->maxEW = rgdouMax(t->rgD, N);

  if (t->flgCheckEigen)
    /* needs O(n^3)! writes, in case, error message in error file */
    i = Check_Eigen( N, t->C, t->rgD, t->B);

#if 0
  /* Limit Condition of C to dMaxSignifKond+1 */
  if (t->maxEW > t->minEW * t->dMaxSignifKond) {
    ERRORMESSAGE("Warning: Condition number of covariance matrix at upper limit.",
                 " Consider a rescaling or redesign of the objective function. " ,"","");
    printf("\nWarning: Condition number of covariance matrix at upper limit\n");
    tmp = t->maxEW/t->dMaxSignifKond - t->minEW;
    tmp = t->maxEW/t->dMaxSignifKond;
    t->minEW += tmp;
    for (i=0;i<N;++i) {
      t->C[i][i] += tmp;
      t->rgD[i] += tmp;
    }
  } /* if */
  t->dLastMinEWgroesserNull = minEW;
#endif

  for (i = 0; i < N; ++i)
    t->rgD[i] = sqrt(t->rgD[i]);

  t->flgEigensysIsUptodate = 1;
  t->genOfEigensysUpdate = t->gen;

  return;

} /* cmaes_UpdateEigensystem() */


/* ========================================================= */
static void
Eigen( int N,  double **C, double *diag, double **Q, double *rgtmp)
/*
   Calculating eigenvalues and vectors.
   Input:
     N: dimension.
     C: symmetric (1:N)xN-matrix, solely used to copy data to Q
     niter: number of maximal iterations for QL-Algorithm.
     rgtmp: N+1-dimensional vector for temporal use.
   Output:
     diag: N eigenvalues.
     Q: Columns are normalized eigenvectors.
 */
{
  int i, j;

  if (rgtmp == NULL) /* was OK in former versions */
    FATAL("cmaes_t:Eigen(): input parameter double *rgtmp must be non-NULL", 0,0,0);

  /* copy C to Q */
  if (C != Q) {
    for (i=0; i < N; ++i)
      for (j = 0; j <= i; ++j)
        Q[i][j] = Q[j][i] = C[i][j];
  }

#if 0
    Householder( N, Q, diag, rgtmp);
    QLalgo( N, diag, Q, 30*N, rgtmp+1);
#else
    Householder2( N, Q, diag, rgtmp);
    QLalgo2( N, diag, rgtmp, Q);
#endif

}


/* ========================================================= */
static void
QLalgo2 (int n, double *d, double *e, double **V) {
  /*
    -> n     : Dimension.
    -> d     : Diagonale of tridiagonal matrix.
    -> e[1..n-1] : off-diagonal, output from Householder
    -> V     : matrix output von Householder
    <- d     : eigenvalues
    <- e     : garbage?
    <- V     : basis of eigenvectors, according to d

    Symmetric tridiagonal QL algorithm, iterative
    Computes the eigensystem from a tridiagonal matrix in roughtly 3N^3 operations

    code adapted from Java JAMA package, function tql2.
  */

  int i, k, l, m;
  double f = 0.0;
  double tst1 = 0.0;
  double eps = 2.22e-16; /* Math.pow(2.0,-52.0);  == 2.22e-16 */

      /* shift input e */
      for (i = 1; i < n; i++) {
         e[i-1] = e[i];
      }
      e[n-1] = 0.0; /* never changed again */

      for (l = 0; l < n; l++) {

        /* Find small subdiagonal element */

         if (tst1 < fabs(d[l]) + fabs(e[l]))
           tst1 = fabs(d[l]) + fabs(e[l]);
         m = l;
         while (m < n) {
           if (fabs(e[m]) <= eps*tst1) {
             /* if (fabs(e[m]) + fabs(d[m]+d[m+1]) == fabs(d[m]+d[m+1])) { */
               break;
            }
            m++;
         }

         /* If m == l, d[l] is an eigenvalue, */
         /* otherwise, iterate. */

         if (m > l) {  /* TODO: check the case m == n, should be rejected here!? */
            int iter = 0;
            do { /* while (fabs(e[l]) > eps*tst1); */
               double dl1, h;
               double g = d[l];
               double p = (d[l+1] - g) / (2.0 * e[l]);
               double r = myhypot(p, 1.);

               iter = iter + 1;  /* Could check iteration count here */

               /* Compute implicit shift */

               if (p < 0) {
                  r = -r;
               }
               d[l] = e[l] / (p + r);
               d[l+1] = e[l] * (p + r);
               dl1 = d[l+1];
               h = g - d[l];
               for (i = l+2; i < n; i++) {
                  d[i] -= h;
               }
               f = f + h;

               /* Implicit QL transformation. */

               p = d[m];
             {
               double c = 1.0;
               double c2 = c;
               double c3 = c;
               double el1 = e[l+1];
               double s = 0.0;
               double s2 = 0.0;
               for (i = m-1; i >= l; i--) {
                  c3 = c2;
                  c2 = c;
                  s2 = s;
                  g = c * e[i];
                  h = c * p;
                  r = myhypot(p, e[i]);
                  e[i+1] = s * r;
                  s = e[i] / r;
                  c = p / r;
                  p = c * d[i] - s * g;
                  d[i+1] = h + s * (c * g + s * d[i]);

                  /* Accumulate transformation. */

                  for (k = 0; k < n; k++) {
                     h = V[k][i+1];
                     V[k][i+1] = s * V[k][i] + c * h;
                     V[k][i] = c * V[k][i] - s * h;
                  }
               }
               p = -s * s2 * c3 * el1 * e[l] / dl1;
               e[l] = s * p;
               d[l] = c * p;
             }

               /* Check for convergence. */

            } while (fabs(e[l]) > eps*tst1);
         }
         d[l] = d[l] + f;
         e[l] = 0.0;
      }

      /* Sort eigenvalues and corresponding vectors. */
#if 1
      /* TODO: really needed here? So far not, but practical and only O(n^2) */
      {
      int j;
      double p;
      for (i = 0; i < n-1; i++) {
         k = i;
         p = d[i];
         for (j = i+1; j < n; j++) {
            if (d[j] < p) {
               k = j;
               p = d[j];
            }
         }
         if (k != i) {
            d[k] = d[i];
            d[i] = p;
            for (j = 0; j < n; j++) {
               p = V[j][i];
               V[j][i] = V[j][k];
               V[j][k] = p;
            }
         }
      }
      }
#endif
} /* QLalgo2 */


/* ========================================================= */
static void
Householder2(int n, double **V, double *d, double *e) {
  /*
     Householder transformation of a symmetric matrix V into tridiagonal form.
   -> n             : dimension
   -> V             : symmetric nxn-matrix
   <- V             : orthogonal transformation matrix:
                      tridiag matrix == V * V_in * V^t
   <- d             : diagonal
   <- e[0..n-1]     : off diagonal (elements 1..n-1)

   code slightly adapted from the Java JAMA package, function private tred2()

  */

  int i,j,k;

      for (j = 0; j < n; j++) {
         d[j] = V[n-1][j];
      }

      /* Householder reduction to tridiagonal form */

      for (i = n-1; i > 0; i--) {

        /* Scale to avoid under/overflow */

         double scale = 0.0;
         double h = 0.0;
         for (k = 0; k < i; k++) {
            scale = scale + fabs(d[k]);
         }
         if (scale == 0.0) {
            e[i] = d[i-1];
            for (j = 0; j < i; j++) {
               d[j] = V[i-1][j];
               V[i][j] = 0.0;
               V[j][i] = 0.0;
            }
         } else {

           /* Generate Householder vector */

            double f, g, hh;

            for (k = 0; k < i; k++) {
               d[k] /= scale;
               h += d[k] * d[k];
            }
            f = d[i-1];
            g = sqrt(h);
            if (f > 0) {
               g = -g;
            }
            e[i] = scale * g;
            h = h - f * g;
            d[i-1] = f - g;
            for (j = 0; j < i; j++) {
               e[j] = 0.0;
            }

            /* Apply similarity transformation to remaining columns */

            for (j = 0; j < i; j++) {
               f = d[j];
               V[j][i] = f;
               g = e[j] + V[j][j] * f;
               for (k = j+1; k <= i-1; k++) {
                  g += V[k][j] * d[k];
                  e[k] += V[k][j] * f;
               }
               e[j] = g;
            }
            f = 0.0;
            for (j = 0; j < i; j++) {
               e[j] /= h;
               f += e[j] * d[j];
            }
            hh = f / (h + h);
            for (j = 0; j < i; j++) {
               e[j] -= hh * d[j];
            }
            for (j = 0; j < i; j++) {
               f = d[j];
               g = e[j];
               for (k = j; k <= i-1; k++) {
                  V[k][j] -= (f * e[k] + g * d[k]);
               }
               d[j] = V[i-1][j];
               V[i][j] = 0.0;
            }
         }
         d[i] = h;
      }

      /* Accumulate transformations */

      for (i = 0; i < n-1; i++) {
         double h;
         V[n-1][i] = V[i][i];
         V[i][i] = 1.0;
         h = d[i+1];
         if (h != 0.0) {
            for (k = 0; k <= i; k++) {
               d[k] = V[k][i+1] / h;
            }
            for (j = 0; j <= i; j++) {
               double g = 0.0;
               for (k = 0; k <= i; k++) {
                  g += V[k][i+1] * V[k][j];
               }
               for (k = 0; k <= i; k++) {
                  V[k][j] -= g * d[k];
               }
            }
         }
         for (k = 0; k <= i; k++) {
            V[k][i+1] = 0.0;
         }
      }
      for (j = 0; j < n; j++) {
         d[j] = V[n-1][j];
         V[n-1][j] = 0.0;
      }
      V[n-1][n-1] = 1.0;
      e[0] = 0.0;

} /* Housholder() */


#if 0
/* ========================================================= */
static void
WriteMaxErrorInfo(cmaes_t *t)
{
  int i,j, N=t->sp.N;
  char *s = (char *)new_void(200+30*(N+2), sizeof(char)); s[0] = '\0';

  sprintf( s+strlen(s),"\nKomplett-Info\n");
  sprintf( s+strlen(s)," Gen       %20.12g\n", t->gen);
  sprintf( s+strlen(s)," Dimension %d\n", N);
  sprintf( s+strlen(s)," sigma     %e\n", t->sigma);
  sprintf( s+strlen(s)," lastminEW %e\n",
           t->dLastMinEWgroesserNull);
  sprintf( s+strlen(s)," maxKond   %e\n\n", t->dMaxSignifKond);
  sprintf( s+strlen(s),"     x-Vektor          rgD     Basis...\n");
  ERRORMESSAGE( s,0,0,0);
  s[0] = '\0';
  for (i = 0; i < N; ++i)
    {
      sprintf( s+strlen(s), " %20.12e", t->rgxmean[i]);
      sprintf( s+strlen(s), " %10.4e", t->rgD[i]);
      for (j = 0; j < N; ++j)
        sprintf( s+strlen(s), " %10.2e", t->B[i][j]);
      ERRORMESSAGE( s,0,0,0);
      s[0] = '\0';
    }
  ERRORMESSAGE( "\n",0,0,0);
  free( s);
} /* WriteMaxErrorInfo() */
#endif

/* --------------------------------------------------------- */
/* --------------- Functions: timings_t -------------------- */
/* --------------------------------------------------------- */
/* timings_t measures overall time and times between calls
 * of tic and toc. For small time spans (up to 1000 seconds)
 * CPU time via clock() is used. For large time spans the
 * fall-back to elapsed time from time() is used.
 * timings_update() must be called often enough to prevent
 * the fallback. */
/* --------------------------------------------------------- */
void
timings_init(timings_t *t) {
  t->totaltotaltime = 0;
  timings_start(t);
}
void
timings_start(timings_t *t) {
  t->totaltime = 0;
  t->tictoctime = 0;
  t->lasttictoctime = 0;
  t->istic = 0;
  t->lastclock = clock();
  t->lasttime = time(NULL);
  t->lastdiff = 0;
  t->tictoczwischensumme = 0;
  t->isstarted = 1;
}

double
timings_update(timings_t *t) {
/* returns time between last call of timings_*() and now,
 *    should better return totaltime or tictoctime?
 */
  double diffc, difft;
  clock_t lc = t->lastclock; /* measure CPU in 1e-6s */
  time_t lt = t->lasttime;   /* measure time in s */

  if (t->isstarted != 1)
    FATAL("timings_started() must be called before using timings... functions",0,0,0);

  t->lastclock = clock(); /* measures at most 2147 seconds, where 1s = 1e6 CLOCKS_PER_SEC */
  t->lasttime = time(NULL);

  diffc = (double)(t->lastclock - lc) / CLOCKS_PER_SEC; /* is presumably in [-21??, 21??] */
  difft = difftime(t->lasttime, lt);                    /* is presumably an integer */

  t->lastdiff = difft; /* on the "save" side */

  /* use diffc clock measurement if appropriate */
  if (diffc > 0 && difft < 1000)
    t->lastdiff = diffc;

  if (t->lastdiff < 0)
    FATAL("BUG in time measurement", 0, 0, 0);

  t->totaltime += t->lastdiff;
  t->totaltotaltime += t->lastdiff;
  if (t->istic) {
    t->tictoczwischensumme += t->lastdiff;
    t->tictoctime += t->lastdiff;
  }

  return t->lastdiff;
}

void
timings_tic(timings_t *t) {
  if (t->istic) { /* message not necessary ? */
    ERRORMESSAGE("Warning: timings_tic called twice without toc",0,0,0);
    return;
  }
  timings_update(t);
  t->istic = 1;
}

double
timings_toc(timings_t *t) {
  if (!t->istic) {
    ERRORMESSAGE("Warning: timings_toc called without tic",0,0,0);
    return -1;
  }
  timings_update(t);
  t->lasttictoctime = t->tictoczwischensumme;
  t->tictoczwischensumme = 0;
  t->istic = 0;
  return t->lasttictoctime;
}

/* --------------------------------------------------------- */
/* ---------------- Functions: random_t -------------------- */
/* --------------------------------------------------------- */
/* --------------------------------------------------------- */
/* X_1 exakt :          0.79788456)  */
/* chi_eins simuliert : 0.798xx   (seed -3) */
/*                    +-0.001 */
/* --------------------------------------------------------- */
/*
   Gauss() liefert normalverteilte Zufallszahlen
   bei vorgegebenem seed.
*/
/* --------------------------------------------------------- */
/* --------------------------------------------------------- */

long
random_init( random_t *t, long unsigned inseed)
{
  clock_t cloc = clock();

  t->flgstored = 0;
  t->rgrand = (long *) new_void(32, sizeof(long));
  if (inseed < 1) {
    while ((long) (cloc - clock()) == 0)
      ; /* TODO: remove this for time critical applications? */
    inseed = (long unsigned)abs(100*time(NULL)+clock());
  }
  return random_Start(t, inseed);
}

void
random_exit(random_t *t)
{
  free( t->rgrand);
}

/* --------------------------------------------------------- */
long random_Start( random_t *t, long unsigned inseed)
{
  long tmp;
  int i;

  t->flgstored = 0;
  t->startseed = inseed;
  if (inseed < 1)
    inseed = 1;
  t->aktseed = inseed;
  for (i = 39; i >= 0; --i)
  {
    tmp = t->aktseed/127773;
    t->aktseed = 16807 * (t->aktseed - tmp * 127773)
      - 2836 * tmp;
    if (t->aktseed < 0) t->aktseed += 2147483647;
    if (i < 32)
      t->rgrand[i] = t->aktseed;
  }
  t->aktrand = t->rgrand[0];
  return inseed;
}

/* --------------------------------------------------------- */
double random_Gauss(random_t *t)
{
  double x1, x2, rquad, fac;

  if (t->flgstored)
  {
    t->flgstored = 0;
    return t->hold;
  }
  do
  {
    x1 = 2.0 * random_Uniform(t) - 1.0;
    x2 = 2.0 * random_Uniform(t) - 1.0;
    rquad = x1*x1 + x2*x2;
  } while(rquad >= 1 || rquad <= 0);
  fac = sqrt(-2.0*log(rquad)/rquad);
  t->flgstored = 1;
  t->hold = fac * x1;
  return fac * x2;
}

/* --------------------------------------------------------- */
double random_Uniform( random_t *t)
{
  long tmp;

  tmp = t->aktseed/127773;
  t->aktseed = 16807 * (t->aktseed - tmp * 127773)
    - 2836 * tmp;
  if (t->aktseed < 0)
    t->aktseed += 2147483647;
  tmp = t->aktrand / 67108865;
  t->aktrand = t->rgrand[tmp];
  t->rgrand[tmp] = t->aktseed;
  return (double)(t->aktrand)/(2.147483647e9);
}

static char *
szCat(const char *sz1, const char*sz2,
      const char *sz3, const char *sz4);

/* --------------------------------------------------------- */
/* -------------- Functions: readpara_t -------------------- */
/* --------------------------------------------------------- */
void
readpara_init (readpara_t *t,
               int dim,
               int inseed,
               const double * inxstart,
               const double * inrgsigma,
               int lambda,
               const char * filename)
{
  int i, N;
  t->rgsformat = (char **) new_void(55, sizeof(char *));
  t->rgpadr = (void **) new_void(55, sizeof(void *));
  t->rgskeyar = (char **) new_void(11, sizeof(char *));
  t->rgp2adr = (double ***) new_void(11, sizeof(double **));
  t->weigkey = (char *)new_void(7, sizeof(char));

  /* All scalars:  */
  i = 0;
  t->rgsformat[i] = " N %d";        t->rgpadr[i++] = (void *) &t->N;
  t->rgsformat[i] = " seed %d";    t->rgpadr[i++] = (void *) &t->seed;
  t->rgsformat[i] = " stopMaxFunEvals %lg"; t->rgpadr[i++] = (void *) &t->stopMaxFunEvals;
  t->rgsformat[i] = " stopMaxIter %lg"; t->rgpadr[i++] = (void *) &t->stopMaxIter;
  t->rgsformat[i] = " stopFitness %lg"; t->rgpadr[i++]=(void *) &t->stStopFitness.val;
  t->rgsformat[i] = " stopTolFun %lg"; t->rgpadr[i++]=(void *) &t->stopTolFun;
  t->rgsformat[i] = " stopTolFunHist %lg"; t->rgpadr[i++]=(void *) &t->stopTolFunHist;
  t->rgsformat[i] = " stopTolX %lg"; t->rgpadr[i++]=(void *) &t->stopTolX;
  t->rgsformat[i] = " stopTolUpXFactor %lg"; t->rgpadr[i++]=(void *) &t->stopTolUpXFactor;
  t->rgsformat[i] = " lambda %d";      t->rgpadr[i++] = (void *) &t->lambda;
  t->rgsformat[i] = " mu %d";          t->rgpadr[i++] = (void *) &t->mu;
  t->rgsformat[i] = " weights %5s";    t->rgpadr[i++] = (void *) t->weigkey;
  t->rgsformat[i] = " fac*cs %lg";t->rgpadr[i++] = (void *) &t->cs;
  t->rgsformat[i] = " fac*damps %lg";   t->rgpadr[i++] = (void *) &t->damps;
  t->rgsformat[i] = " ccumcov %lg";    t->rgpadr[i++] = (void *) &t->ccumcov;
  t->rgsformat[i] = " mucov %lg";     t->rgpadr[i++] = (void *) &t->mucov;
  t->rgsformat[i] = " fac*ccov %lg";  t->rgpadr[i++]=(void *) &t->ccov;
  t->rgsformat[i] = " diagonalCovarianceMatrix %lg"; t->rgpadr[i++]=(void *) &t->diagonalCov;
  t->rgsformat[i] = " updatecov %lg"; t->rgpadr[i++]=(void *) &t->updateCmode.modulo;
  t->rgsformat[i] = " maxTimeFractionForEigendecompostion %lg"; t->rgpadr[i++]=(void *) &t->updateCmode.maxtime;
  t->rgsformat[i] = " resume %59s";    t->rgpadr[i++] = (void *) t->resumefile;
  t->rgsformat[i] = " fac*maxFunEvals %lg";   t->rgpadr[i++] = (void *) &t->facmaxeval;
  t->rgsformat[i] = " fac*updatecov %lg"; t->rgpadr[i++]=(void *) &t->facupdateCmode;
  t->n1para = i;
  t->n1outpara = i-2; /* disregard last parameters in WriteToFile() */

  /* arrays */
  i = 0;
  t->rgskeyar[i]  = " typicalX %d";   t->rgp2adr[i++] = &t->typicalX;
  t->rgskeyar[i]  = " initialX %d";   t->rgp2adr[i++] = &t->xstart;
  t->rgskeyar[i]  = " initialStandardDeviations %d"; t->rgp2adr[i++] = &t->rgInitialStds;
  t->rgskeyar[i]  = " diffMinChange %d"; t->rgp2adr[i++] = &t->rgDiffMinChange;
  t->n2para = i;

  t->N = dim;
  t->seed = (unsigned) inseed;
  t->xstart = NULL;
  t->typicalX = NULL;
  t->typicalXcase = 0;
  t->rgInitialStds = NULL;
  t->rgDiffMinChange = NULL;
  t->stopMaxFunEvals = -1;
  t->stopMaxIter = -1;
  t->facmaxeval = 1;
  t->stStopFitness.flg = -1;
  t->stopTolFun = 1e-12;
  t->stopTolFunHist = 1e-13;
  t->stopTolX = 0; /* 1e-11*insigma would also be reasonable */
  t->stopTolUpXFactor = 1e3;

  t->lambda = lambda;
  t->mu = -1;
  t->mucov = -1;
  t->weights = NULL;
  strcpy(t->weigkey, "log");

  t->cs = -1;
  t->ccumcov = -1;
  t->damps = -1;
  t->ccov = -1;

  t->diagonalCov = 0; /* default is 0, but this might change in future, see below */

  t->updateCmode.modulo = -1;
  t->updateCmode.maxtime = -1;
  t->updateCmode.flgalways = 0;
  t->facupdateCmode = 1;
  strcpy(t->resumefile, "_no_");

  if (strcmp(filename, "non") != 0 && strcmp(filename, "writeonly") != 0)
    readpara_ReadFromFile(t, filename);

  if (t->N <= 0)
    t->N = dim;

  N = t->N;
  if (N == 0)
    FATAL("readpara_readpara_t(): problem dimension N undefined.\n",
          "  (no default value available).",0,0);
  if (t->xstart == NULL && inxstart == NULL && t->typicalX == NULL) {
    ERRORMESSAGE("Warning: initialX undefined. typicalX = 0.5...0.5 used.","","","");
    printf("\nWarning: initialX undefined. typicalX = 0.5...0.5 used.\n");
  }
  if (t->rgInitialStds == NULL && inrgsigma == NULL) {
    /* FATAL("initialStandardDeviations undefined","","",""); */
    ERRORMESSAGE("Warning: initialStandardDeviations undefined. 0.3...0.3 used.","","","");
    printf("\nWarning: initialStandardDeviations. 0.3...0.3 used.\n");
  }

  if (t->xstart == NULL) {
    t->xstart = new_double(N);

    /* put inxstart into xstart */
    if (inxstart != NULL) {
      for (i=0; i<N; ++i)
        t->xstart[i] = inxstart[i];
    }
    /* otherwise use typicalX or default */
    else {
      t->typicalXcase = 1;
      for (i=0; i<N; ++i)
        t->xstart[i] = (t->typicalX == NULL) ? 0.5 : t->typicalX[i];
    }
  } /* xstart == NULL */

  if (t->rgInitialStds == NULL) {
    t->rgInitialStds = new_double(N);
    for (i=0; i<N; ++i)
      t->rgInitialStds[i] = (inrgsigma == NULL) ? 0.3 : inrgsigma[i];
  }

  readpara_SupplementDefaults(t);
  if (strcmp(filename, "non") != 0)
    readpara_WriteToFile(t, "actparcmaes.par", filename);
} /* readpara_init */

/* --------------------------------------------------------- */
/* --------------------------------------------------------- */
void readpara_exit(readpara_t *t)
{
  if (t->xstart != NULL) /* not really necessary */
    free( t->xstart);
  if (t->typicalX != NULL)
    free( t->typicalX);
  if (t->rgInitialStds != NULL)
    free( t->rgInitialStds);
  if (t->rgDiffMinChange != NULL)
    free( t->rgDiffMinChange);
  if (t->weights != NULL)
    free( t->weights);

  free(t->rgsformat);
  free(t->rgpadr);
  free(t->rgskeyar);
  free(t->rgp2adr);
  free(t->weigkey);
}

/* --------------------------------------------------------- */
/* --------------------------------------------------------- */
void
readpara_ReadFromFile(readpara_t *t, const char * filename)
{
  char s[1000], *ss = "initials.par";
  int ipara, i;
  int size;
  FILE *fp;
  if (filename == NULL)
    filename = ss;
  fp = fopen( filename, "r");
  if(fp == NULL) {
    ERRORMESSAGE("cmaes_ReadFromFile(): could not open '", filename, "'",0);
    return;
  }
  for (ipara=0; ipara < t->n1para; ++ipara)
    {
      rewind(fp);
      while(fgets(s, sizeof(s), fp) != NULL)
        { /* skip comments  */
          if (s[0] == '#' || s[0] == '%')
            continue;
          if(sscanf(s, t->rgsformat[ipara], t->rgpadr[ipara]) == 1) {
            if (strncmp(t->rgsformat[ipara], " stopFitness ", 13) == 0)
              t->stStopFitness.flg = 1;
            break;
          }
        }
    } /* for */
  if (t->N <= 0)
    FATAL("readpara_ReadFromFile(): No valid dimension N",0,0,0);
  for (ipara=0; ipara < t->n2para; ++ipara)
    {
      rewind(fp);
      while(fgets(s, sizeof(s), fp) != NULL) /* read one line */
        { /* skip comments  */
          if (s[0] == '#' || s[0] == '%')
            continue;
          if(sscanf(s, t->rgskeyar[ipara], &size) == 1) { /* size==number of values to be read */
            if (size > 0) {
              *t->rgp2adr[ipara] = new_double(t->N);
              for (i=0;i<size&&i<t->N;++i) /* start reading next line */
                if (fscanf(fp, " %lf", &(*t->rgp2adr[ipara])[i]) != 1)
                  break;
              if (i<size && i < t->N) {
                ERRORMESSAGE("readpara_ReadFromFile ", filename, ": ",0);
                FATAL( "'", t->rgskeyar[ipara],
                       "' not enough values found.\n",
                       "   Remove all comments between numbers.");
              }
              for (; i < t->N; ++i) /* recycle */
                (*t->rgp2adr[ipara])[i] = (*t->rgp2adr[ipara])[i%size];
            }
          }
        }
    } /* for */
  fclose(fp);
  return;
} /* readpara_ReadFromFile() */

/* --------------------------------------------------------- */
/* --------------------------------------------------------- */
void
readpara_WriteToFile(readpara_t *t, const char *filenamedest,
                     const char *filenamesource)
{
  int ipara, i;
  size_t len;
  time_t ti = time(NULL);
  FILE *fp = fopen( filenamedest, "a");
  if(fp == NULL) {
    ERRORMESSAGE("cmaes_WriteToFile(): could not open '",
                 filenamedest, "'",0);
    return;
  }
  fprintf(fp, "\n# Read from %s at %s\n", filenamesource,
          asctime(localtime(&ti))); /* == ctime() */
  for (ipara=0; ipara < 1; ++ipara) {
    fprintf(fp, t->rgsformat[ipara], *(int *)t->rgpadr[ipara]);
    fprintf(fp, "\n");
  }
  for (ipara=0; ipara < t->n2para; ++ipara) {
    if(*t->rgp2adr[ipara] == NULL)
      continue;
    fprintf(fp, t->rgskeyar[ipara], t->N);
    fprintf(fp, "\n");
    for (i=0; i<t->N; ++i)
      fprintf(fp, "%7.3g%c", (*t->rgp2adr[ipara])[i], (i%5==4)?'\n':' ');
    fprintf(fp, "\n");
  }
  for (ipara=1; ipara < t->n1outpara; ++ipara) {
    if (strncmp(t->rgsformat[ipara], " stopFitness ", 13) == 0)
      if(t->stStopFitness.flg == 0) {
        fprintf(fp, " stopFitness\n");
        continue;
      }
    len = strlen(t->rgsformat[ipara]);
    if (t->rgsformat[ipara][len-1] == 'd') /* read integer */
      fprintf(fp, t->rgsformat[ipara], *(int *)t->rgpadr[ipara]);
    else if (t->rgsformat[ipara][len-1] == 's') /* read string */
      fprintf(fp, t->rgsformat[ipara], (char *)t->rgpadr[ipara]);
    else {
      if (strncmp(" fac*", t->rgsformat[ipara], 5) == 0) {
        fprintf(fp, " ");
        fprintf(fp, t->rgsformat[ipara]+5, *(double *)t->rgpadr[ipara]);
      } else
        fprintf(fp, t->rgsformat[ipara], *(double *)t->rgpadr[ipara]);
    }
    fprintf(fp, "\n");
  } /* for */
  fprintf(fp, "\n");
  fclose(fp);
} /* readpara_WriteToFile() */

/* --------------------------------------------------------- */
/* --------------------------------------------------------- */
void
readpara_SupplementDefaults(readpara_t *t)
{
  double t1, t2;
  int N = t->N;
  clock_t cloc = clock();

  if (t->seed < 1) {
    while ((int) (cloc - clock()) == 0)
      ; /* TODO: remove this for time critical applications!? */
    t->seed = (unsigned int)abs(100*time(NULL)+clock());
  }

  if (t->stStopFitness.flg == -1)
    t->stStopFitness.flg = 0;

  if (t->lambda < 2)
    t->lambda = 4+(int)(3*log((double)N));
  if (t->mu == -1) {
    t->mu = t->lambda/2;
    readpara_SetWeights(t, t->weigkey);
  }
  if (t->weights == NULL)
    readpara_SetWeights(t, t->weigkey);

  if (t->cs > 0) /* factor was read */
    t->cs *= (t->mueff + 2.) / (N + t->mueff + 3.);
  if (t->cs <= 0 || t->cs >= 1)
    t->cs = (t->mueff + 2.) / (N + t->mueff + 3.);

  if (t->ccumcov <= 0 || t->ccumcov > 1)
    t->ccumcov = 4. / (N + 4);

  if (t->mucov < 1) {
    t->mucov = t->mueff;
  }
  t1 = 2. / ((N+1.4142)*(N+1.4142));
  t2 = (2.*t->mueff-1.) / ((N+2.)*(N+2.)+t->mueff);
  t2 = (t2 > 1) ? 1 : t2;
  t2 = (1./t->mucov) * t1 + (1.-1./t->mucov) * t2;
  if (t->ccov >= 0) /* ccov holds the read factor */
    t->ccov *= t2;
  if (t->ccov < 0 || t->ccov > 1) /* set default in case */
    t->ccov = t2;

  if (t->diagonalCov == -1)
    t->diagonalCov = 2 + 100. * N / sqrt((double)t->lambda);

  if (t->stopMaxFunEvals == -1)  /* may depend on ccov in near future */
    t->stopMaxFunEvals = t->facmaxeval*900*(N+3)*(N+3);
  else
    t->stopMaxFunEvals *= t->facmaxeval;

  if (t->stopMaxIter == -1)
    t->stopMaxIter = ceil((double)(t->stopMaxFunEvals / t->lambda));

  if (t->damps < 0)
    t->damps = 1; /* otherwise a factor was read */
  t->damps = t->damps
    * (1 + 2*douMax(0., sqrt((t->mueff-1.)/(N+1.)) - 1))     /* basic factor */
    * douMax(0.3, 1. -                                       /* modify for short runs */
                  (double)N / (1e-6+douMin(t->stopMaxIter, t->stopMaxFunEvals/t->lambda)))
    + t->cs;                                                 /* minor increment */

  if (t->updateCmode.modulo < 0)
    t->updateCmode.modulo = 1./t->ccov/(double)(N)/10.;
  t->updateCmode.modulo *= t->facupdateCmode;
  if (t->updateCmode.maxtime < 0)
    t->updateCmode.maxtime = 0.20; /* maximal 20% of CPU-time */

} /* readpara_SupplementDefaults() */


/* --------------------------------------------------------- */
/* --------------------------------------------------------- */
void
readpara_SetWeights(readpara_t *t, const char * mode)
{
  double s1, s2;
  int i;

  if(t->weights != NULL)
    free( t->weights);
  t->weights = new_double(t->mu);
  if (strcmp(mode, "lin") == 0)
    for (i=0; i<t->mu; ++i)
      t->weights[i] = t->mu - i;
  else if (strncmp(mode, "equal", 3) == 0)
    for (i=0; i<t->mu; ++i)
      t->weights[i] = 1;
  else if (strcmp(mode, "log") == 0)
    for (i=0; i<t->mu; ++i)
      t->weights[i] = log(t->mu+1.)-log(i+1.);
  else
    for (i=0; i<t->mu; ++i)
      t->weights[i] = log(t->mu+1.)-log(i+1.);

  /* normalize weights vector and set mueff */
  for (i=0, s1=0, s2=0; i<t->mu; ++i) {
    s1 += t->weights[i];
    s2 += t->weights[i]*t->weights[i];
  }
  t->mueff = s1*s1/s2;
  for (i=0; i<t->mu; ++i)
    t->weights[i] /= s1;

  if(t->mu < 1 || t->mu > t->lambda ||
     (t->mu==t->lambda && t->weights[0]==t->weights[t->mu-1]))
    FATAL("readpara_SetWeights(): invalid setting of mu or lambda",0,0,0);

} /* readpara_SetWeights() */

/* --------------------------------------------------------- */
/* --------------------------------------------------------- */
static double
douSquare(double d)
{
  return d*d;
}
static int
intMin( int i, int j)
{
  return i < j ? i : j;
}
static double
douMax( double i, double j)
{
  return i > j ? i : j;
}
static double
douMin( double i, double j)
{
  return i < j ? i : j;
}
static double
rgdouMax( const double *rgd, int len)
{
  int i;
  double max = rgd[0];
  for (i = 1; i < len; ++i)
    max = (max < rgd[i]) ? rgd[i] : max;
  return max;
}

static double
rgdouMin( const double *rgd, int len)
{
  int i;
  double min = rgd[0];
  for (i = 1; i < len; ++i)
    min = (min > rgd[i]) ? rgd[i] : min;
  return min;
}

static int
MaxIdx( const double *rgd, int len)
{
  int i, res;
  for(i=1, res=0; i<len; ++i)
    if(rgd[i] > rgd[res])
      res = i;
  return res;
}
static int
MinIdx( const double *rgd, int len)
{
  int i, res;
  for(i=1, res=0; i<len; ++i)
    if(rgd[i] < rgd[res])
      res = i;
  return res;
}

static double
myhypot(double a, double b)
/* sqrt(a^2 + b^2) numerically stable. */
{
  double r = 0;
  if (fabs(a) > fabs(b)) {
    r = b/a;
    r = fabs(a)*sqrt(1+r*r);
  } else if (b != 0) {
    r = a/b;
    r = fabs(b)*sqrt(1+r*r);
  }
  return r;
}

static int SignOfDiff(const void *d1, const void * d2)
{
  return *((double *) d1) > *((double *) d2) ? 1 : -1;
}

#if 1
/* dirty index sort */
static void Sorted_index(const double *rgFunVal, int *iindex, int n)
{
  int i, j;
  for (i=1, iindex[0]=0; i<n; ++i) {
    for (j=i; j>0; --j) {
      if (rgFunVal[iindex[j-1]] < rgFunVal[i])
        break;
      iindex[j] = iindex[j-1]; /* shift up */
    }
    iindex[j] = i; /* insert i */
  }
}
#endif

static void * new_void(int n, size_t size)
{
  static char s[70];
  void *p = calloc((unsigned) n, size);
  if (p == NULL) {
    sprintf(s, "new_void(): calloc(%ld,%ld) failed",(long)n,(long)size);
    FATAL(s,0,0,0);
  }
  return p;
}

double *
cmaes_NewDouble(int n)
{
  return new_double(n);
}

static double * new_double(int n)
{
  static char s[170];
  double *p = (double *) calloc((unsigned) n, sizeof(double));
  if (p == NULL) {
    sprintf(s, "new_double(): calloc(%ld,%ld) failed",
            (long)n,(long)sizeof(double));
    FATAL(s,0,0,0);
  }
  return p;
}

/* --------------------------------------------------------- */
/* --------------------------------------------------------- */

/* ========================================================= */
void
cmaes_FATAL(char const *s1, char const *s2, char const *s3,
            char const *s4)
{
  time_t t = time(NULL);
  ERRORMESSAGE( s1, s2, s3, s4);
  ERRORMESSAGE("*** Exiting cmaes_t ***",0,0,0);
  printf("\n -- %s %s\n", asctime(localtime(&t)),
           s2 ? szCat(s1, s2, s3, s4) : s1);
  printf(" *** CMA-ES ABORTED, see errcmaes.err *** \n");
  fflush(stdout);
  exit(1);
}

/* ========================================================= */
static void
FATAL(char const *s1, char const *s2, char const *s3,
      char const *s4)
{
  cmaes_FATAL(s1, s2, s3, s4);
}

/* ========================================================= */
void ERRORMESSAGE( char const *s1, char const *s2,
                   char const *s3, char const *s4)
{
#if 1
  /*  static char szBuf[700];  desirable but needs additional input argument
      sprintf(szBuf, "%f:%f", gen, gen*lambda);
  */
  time_t t = time(NULL);
  FILE *fp = fopen( "errcmaes.err", "a");
  if (!fp)
    {
      printf("\nFATAL ERROR: %s\n", s2 ? szCat(s1, s2, s3, s4) : s1);
      printf("cmaes_t could not open file 'errcmaes.err'.");
      printf("\n *** CMA-ES ABORTED *** ");
      fflush(stdout);
      exit(1);
    }
  fprintf( fp, "\n -- %s %s\n", asctime(localtime(&t)),
           s2 ? szCat(s1, s2, s3, s4) : s1);
  fclose (fp);
#endif
}

/* ========================================================= */
char *szCat(const char *sz1, const char*sz2,
            const char *sz3, const char *sz4)
{
  static char szBuf[700];

  if (!sz1)
    FATAL("szCat() : Invalid Arguments",0,0,0);

  strncpy ((char *)szBuf, sz1, (unsigned)intMin( (int)strlen(sz1), 698));
  szBuf[intMin( (int)strlen(sz1), 698)] = '\0';
  if (sz2)
    strncat ((char *)szBuf, sz2,
             (unsigned)intMin((int)strlen(sz2)+1, 698 - (int)strlen((char const *)szBuf)));
  if (sz3)
    strncat((char *)szBuf, sz3,
            (unsigned)intMin((int)strlen(sz3)+1, 698 - (int)strlen((char const *)szBuf)));
  if (sz4)
    strncat((char *)szBuf, sz4,
            (unsigned)intMin((int)strlen(sz4)+1, 698 - (int)strlen((char const *)szBuf)));
  return (char *) szBuf;
}
