/*************************************************************************
* Conjugate gradient results
*
* Called after MinCG returned False.
*
* INPUT PARAMETERS:
* State - algorithm state (used by MinCGIteration).
*
* OUTPUT PARAMETERS:
* X - array[0..N-1], solution
* Rep - optimization report:
* Rep.TerminationType completetion code:
* -2 rounding errors prevent further improvement.
* X contains best point found.
* -1 incorrect parameters were specified
* 1 relative function improvement is no more than
* EpsF.
* 2 relative step is no more than EpsX.
* 4 gradient norm is no more than EpsG
* 5 MaxIts steps was taken
* 7 stopping conditions are too stringent,
* further improvement is impossible
* Rep.IterationsCount contains iterations count
* NFEV countains number of function calculations
*
* -- ALGLIB --
* Copyright 20.04.2009 by Bochkanov Sergey
*************************************************************************/
public static void mincgresults(ref mincgstate state,
ref double[] x,
ref mincgreport rep)
{
int i_ = 0;
x = new double[state.n - 1 + 1];
for (i_ = 0; i_ <= state.n - 1; i_++)
{
x[i_] = state.xn[i_];
}
rep.iterationscount = state.repiterationscount;
rep.nfev = state.repnfev;
rep.terminationtype = state.repterminationtype;
}
/*************************************************************************
Conjugate gradient results
Buffered implementation of MinCGResults(), which uses pre-allocated buffer
to store X[]. If buffer size is too small, it resizes buffer. It is
intended to be used in the inner cycles of performance critical algorithms
where array reallocation penalty is too large to be ignored.
-- ALGLIB --
Copyright 20.04.2009 by Bochkanov Sergey
*************************************************************************/
public static void mincgresultsbuf(mincgstate state,
ref double[] x,
mincgreport rep)
{
int i_ = 0;
if( alglib.ap.len(x)<state.n )
{
x = new double[state.n];
}
for(i_=0; i_<=state.n-1;i_++)
{
x[i_] = state.xn[i_];
}
rep.iterationscount = state.repiterationscount;
rep.nfev = state.repnfev;
rep.varidx = state.repvaridx;
rep.terminationtype = state.repterminationtype;
}
/*************************************************************************
Conjugate gradient results
INPUT PARAMETERS:
State - algorithm state
OUTPUT PARAMETERS:
X - array[0..N-1], solution
Rep - optimization report:
* Rep.TerminationType completetion code:
* -8 internal integrity control detected infinite
or NAN values in function/gradient. Abnormal
termination signalled.
* -7 gradient verification failed.
See MinCGSetGradientCheck() for more information.
* 1 relative function improvement is no more than
EpsF.
* 2 relative step is no more than EpsX.
* 4 gradient norm is no more than EpsG
* 5 MaxIts steps was taken
* 7 stopping conditions are too stringent,
further improvement is impossible,
we return best X found so far
* 8 terminated by user
* Rep.IterationsCount contains iterations count
* NFEV countains number of function calculations
-- ALGLIB --
Copyright 20.04.2009 by Bochkanov Sergey
*************************************************************************/
public static void mincgresults(mincgstate state,
ref double[] x,
mincgreport rep)
{
x = new double[0];
mincgresultsbuf(state, ref x, rep);
}
public override alglib.apobject make_copy()
{
mincgreport _result = new mincgreport();
_result.iterationscount = iterationscount;
_result.nfev = nfev;
_result.varidx = varidx;
_result.terminationtype = terminationtype;
return _result;
}
/*************************************************************************
Conjugate gradient results
Buffered implementation of MinCGResults(), which uses pre-allocated buffer
to store X[]. If buffer size is too small, it resizes buffer. It is
intended to be used in the inner cycles of performance critical algorithms
where array reallocation penalty is too large to be ignored.
-- ALGLIB --
Copyright 20.04.2009 by Bochkanov Sergey
*************************************************************************/
public static void mincgresultsbuf(mincgstate state, ref double[] x, mincgreport rep)
{
mincg.mincgresultsbuf(state.innerobj, ref x, rep.innerobj);
return;
}
/*************************************************************************
Conjugate gradient results
INPUT PARAMETERS:
State - algorithm state
OUTPUT PARAMETERS:
X - array[0..N-1], solution
Rep - optimization report:
* Rep.TerminationType completetion code:
* 1 relative function improvement is no more than
EpsF.
* 2 relative step is no more than EpsX.
* 4 gradient norm is no more than EpsG
* 5 MaxIts steps was taken
* 7 stopping conditions are too stringent,
further improvement is impossible,
we return best X found so far
* 8 terminated by user
* Rep.IterationsCount contains iterations count
* NFEV countains number of function calculations
-- ALGLIB --
Copyright 20.04.2009 by Bochkanov Sergey
*************************************************************************/
public static void mincgresults(mincgstate state, out double[] x, out mincgreport rep)
{
x = new double[0];
rep = new mincgreport();
mincg.mincgresults(state.innerobj, ref x, rep.innerobj);
return;
}
/*************************************************************************
Conjugate gradient results
Called after MinCG returned False.
INPUT PARAMETERS:
State - algorithm state (used by MinCGIteration).
OUTPUT PARAMETERS:
X - array[0..N-1], solution
Rep - optimization report:
* Rep.TerminationType completetion code:
* -2 rounding errors prevent further improvement.
X contains best point found.
* -1 incorrect parameters were specified
* 1 relative function improvement is no more than
EpsF.
* 2 relative step is no more than EpsX.
* 4 gradient norm is no more than EpsG
* 5 MaxIts steps was taken
* 7 stopping conditions are too stringent,
further improvement is impossible
* Rep.IterationsCount contains iterations count
* NFEV countains number of function calculations
-- ALGLIB --
Copyright 20.04.2009 by Bochkanov Sergey
*************************************************************************/
public static void mincgresults(ref mincgstate state,
ref double[] x,
ref mincgreport rep)
{
int i_ = 0;
x = new double[state.n-1+1];
for(i_=0; i_<=state.n-1;i_++)
{
x[i_] = state.xn[i_];
}
rep.iterationscount = state.repiterationscount;
rep.nfev = state.repnfev;
rep.terminationtype = state.repterminationtype;
}
