/usr/include/minasa.h is in libalglib-dev 2.6.0-3.
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Copyright (c) 2010, Sergey Bochkanov (ALGLIB project).
>>> SOURCE LICENSE >>>
This program is free software; you can redistribute it and/or modify
it under the terms of the GNU General Public License as published by
the Free Software Foundation (www.fsf.org); either version 2 of the
License, or (at your option) any later version.
This program 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 General Public License for more details.
A copy of the GNU General Public License is available at
http://www.fsf.org/licensing/licenses
>>> END OF LICENSE >>>
*************************************************************************/
#ifndef _minasa_h
#define _minasa_h
#include "ap.h"
#include "ialglib.h"
#include "linmin.h"
struct minasastate
{
int n;
double epsg;
double epsf;
double epsx;
int maxits;
bool xrep;
double stpmax;
int cgtype;
int k;
int nfev;
int mcstage;
ap::real_1d_array bndl;
ap::real_1d_array bndu;
int curalgo;
int acount;
double mu;
double finit;
double dginit;
ap::real_1d_array ak;
ap::real_1d_array xk;
ap::real_1d_array dk;
ap::real_1d_array an;
ap::real_1d_array xn;
ap::real_1d_array dn;
ap::real_1d_array d;
double fold;
double stp;
ap::real_1d_array work;
ap::real_1d_array yk;
ap::real_1d_array gc;
ap::real_1d_array x;
double f;
ap::real_1d_array g;
bool needfg;
bool xupdated;
ap::rcommstate rstate;
int repiterationscount;
int repnfev;
int repterminationtype;
int debugrestartscount;
linminstate lstate;
double betahs;
double betady;
};
struct minasareport
{
int iterationscount;
int nfev;
int terminationtype;
int activeconstraints;
};
/*************************************************************************
NONLINEAR BOUND CONSTRAINED OPTIMIZATION USING
MODIFIED
WILLIAM W. HAGER AND HONGCHAO ZHANG
ACTIVE SET ALGORITHM
The subroutine minimizes function F(x) of N arguments with bound
constraints: BndL[i] <= x[i] <= BndU[i]
This method is globally convergent as long as grad(f) is Lipschitz
continuous on a level set: L = { x : f(x)<=f(x0) }.
INPUT PARAMETERS:
N - problem dimension. N>0
X - initial solution approximation, array[0..N-1].
BndL - lower bounds, array[0..N-1].
all elements MUST be specified, i.e. all variables are
bounded. However, if some (all) variables are unbounded,
you may specify very small number as bound: -1000, -1.0E6
or -1.0E300, or something like that.
BndU - upper bounds, array[0..N-1].
all elements MUST be specified, i.e. all variables are
bounded. However, if some (all) variables are unbounded,
you may specify very large number as bound: +1000, +1.0E6
or +1.0E300, or something like that.
EpsG - positive number which defines a precision of search. The
subroutine finishes its work if the condition ||G|| < EpsG is
satisfied, where ||.|| means Euclidian norm, G - gradient, X -
current approximation.
EpsF - positive number which defines a precision of search. The
subroutine finishes its work if on iteration number k+1 the
condition |F(k+1)-F(k)| <= EpsF*max{|F(k)|, |F(k+1)|, 1} is
satisfied.
EpsX - positive number which defines a precision of search. The
subroutine finishes its work if on iteration number k+1 the
condition |X(k+1)-X(k)| <= EpsX is fulfilled.
MaxIts - maximum number of iterations. If MaxIts=0, the number of
iterations is unlimited.
OUTPUT PARAMETERS:
State - structure used for reverse communication.
This function initializes State structure with default optimization
parameters (stopping conditions, step size, etc.). Use MinASASet??????()
functions to tune optimization parameters.
After all optimization parameters are tuned, you should use
MinASAIteration() function to advance algorithm iterations.
NOTES:
1. you may tune stopping conditions with MinASASetCond() function
2. if target function contains exp() or other fast growing functions, and
optimization algorithm makes too large steps which leads to overflow,
use MinASASetStpMax() function to bound algorithm's steps.
-- ALGLIB --
Copyright 25.03.2010 by Bochkanov Sergey
*************************************************************************/
void minasacreate(int n,
const ap::real_1d_array& x,
const ap::real_1d_array& bndl,
const ap::real_1d_array& bndu,
minasastate& state);
/*************************************************************************
This function sets stopping conditions for the ASA optimization algorithm.
INPUT PARAMETERS:
State - structure which stores algorithm state between calls and
which is used for reverse communication. Must be initialized
with MinASACreate()
EpsG - >=0
The subroutine finishes its work if the condition
||G||<EpsG is satisfied, where ||.|| means Euclidian norm,
G - gradient.
EpsF - >=0
The subroutine finishes its work if on k+1-th iteration
the condition |F(k+1)-F(k)|<=EpsF*max{|F(k)|,|F(k+1)|,1}
is satisfied.
EpsX - >=0
The subroutine finishes its work if on k+1-th iteration
the condition |X(k+1)-X(k)| <= EpsX is fulfilled.
MaxIts - maximum number of iterations. If MaxIts=0, the number of
iterations is unlimited.
Passing EpsG=0, EpsF=0, EpsX=0 and MaxIts=0 (simultaneously) will lead to
automatic stopping criterion selection (small EpsX).
-- ALGLIB --
Copyright 02.04.2010 by Bochkanov Sergey
*************************************************************************/
void minasasetcond(minasastate& state,
double epsg,
double epsf,
double epsx,
int maxits);
/*************************************************************************
This function turns on/off reporting.
INPUT PARAMETERS:
State - structure which stores algorithm state between calls and
which is used for reverse communication. Must be
initialized with MinASACreate()
NeedXRep- whether iteration reports are needed or not
Usually algorithm returns from MinASAIteration() only when it needs
function/gradient. However, with this function we can let it stop after
each iteration (one iteration may include more than one function
evaluation), which is indicated by XUpdated field.
-- ALGLIB --
Copyright 02.04.2010 by Bochkanov Sergey
*************************************************************************/
void minasasetxrep(minasastate& state, bool needxrep);
/*************************************************************************
This function sets optimization algorithm.
INPUT PARAMETERS:
State - structure which stores algorithm state between calls and
which is used for reverse communication. Must be
initialized with MinASACreate()
UAType - algorithm type:
* -1 automatic selection of the best algorithm
* 0 DY (Dai and Yuan) algorithm
* 1 Hybrid DY-HS algorithm
-- ALGLIB --
Copyright 02.04.2010 by Bochkanov Sergey
*************************************************************************/
void minasasetalgorithm(minasastate& state, int algotype);
/*************************************************************************
This function sets maximum step length
INPUT PARAMETERS:
State - structure which stores algorithm state between calls and
which is used for reverse communication. Must be
initialized with MinCGCreate()
StpMax - maximum step length, >=0. Set StpMax to 0.0, if you don't
want to limit step length.
Use this subroutine when you optimize target function which contains exp()
or other fast growing functions, and optimization algorithm makes too
large steps which leads to overflow. This function allows us to reject
steps that are too large (and therefore expose us to the possible
overflow) without actually calculating function value at the x+stp*d.
-- ALGLIB --
Copyright 02.04.2010 by Bochkanov Sergey
*************************************************************************/
void minasasetstpmax(minasastate& state, double stpmax);
/*************************************************************************
One ASA iteration
Called after initialization with MinASACreate.
See HTML documentation for examples.
INPUT PARAMETERS:
State - structure which stores algorithm state between calls and
which is used for reverse communication. Must be initialized
with MinASACreate.
RESULT:
* if function returned False, iterative proces has converged.
Use MinLBFGSResults() to obtain optimization results.
* if subroutine returned True, then, depending on structure fields, we
have one of the following situations
=== FUNC/GRAD REQUEST ===
State.NeedFG is True => function value/gradient are needed.
Caller should calculate function value State.F and gradient
State.G[0..N-1] at State.X[0..N-1] and call MinLBFGSIteration() again.
=== NEW INTERATION IS REPORTED ===
State.XUpdated is True => one more iteration was made.
State.X contains current position, State.F contains function value at X.
You can read info from these fields, but never modify them because they
contain the only copy of optimization algorithm state.
One and only one of these fields (NeedFG, XUpdated) is true on return. New
iterations are reported only when reports are explicitly turned on by
MinLBFGSSetXRep() function, so if you never called it, you can expect that
NeedFG is always True.
-- ALGLIB --
Copyright 20.03.2009 by Bochkanov Sergey
*************************************************************************/
bool minasaiteration(minasastate& state);
/*************************************************************************
Conjugate gradient results
Called after MinASA returned False.
INPUT PARAMETERS:
State - algorithm state (used by MinASAIteration).
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
* ActiveConstraints contains number of active constraints
-- ALGLIB --
Copyright 20.03.2009 by Bochkanov Sergey
*************************************************************************/
void minasaresults(const minasastate& state,
ap::real_1d_array& x,
minasareport& rep);
#endif
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