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Algorithms.ox

Methods to Optimize an Objective or Solve a Non-Linear System. ⇩

Algorithms are methods to optimize/solve a vector of parameters $(\psi)$ for either an objective to maximize or a system of equations to solve. Each algorithm is derived from the Algorithm class.

See the FiveO class hierarchy to see all the available algorithms.

Author:: © 2011-2023 Christopher Ferrall

Algorithm

Base class for optimization and system-solving algorithms.

To use an algorithm:

Declare a class for your Objective (e.g. a BlackBox or System).

Create an object of your objective class.

Create an object of the algorithm class you want to use, sending your objective to the creator.

If desired, call the algorithm's Tune() method to tweak parameters and/or change the volume setting.

Call the Iterate() method of the algorithm.

Public fields
convergence		Convergence code.
holdF
IIterate		Running on the Client node or no MPI .
inparallel		Running in parallel.
istr		@internal.
iter		current iteration count.
itoler	static const	Default top level convergence tolerance.
logf		log file
lognm	const	name of log file
logpfx	const	prefix on log before timestamp added.
maxiter		maximum iterations
N		.
NormalStart		Not restarting from alg.
O	const	User's objective.
path		sequence of structural parameters .
StorePath		Store path of iterations in path.
tolerance		top level convergence tolerance
Volume		output level, NoiseLevels
Public methods
Algorithm		Base class for non-linear programming algorithms.
CheckPoint	virtual
ItEnd	virtual	Finish up iteration.
Iterate	virtual
ItStartCheck	virtual	This routine is called at the start if Iterate().
out	virtual
Paths	virtual
Tune	virtual	Tune Parameters of the Algorithm.

BFGS : QuasiNewton : HillClimbing : GradientBased : Algorithm

Broyden Fletcher Goldfarb Shanno Updating of the hessian H.

See Wikipedia::BFGS

To use this algorithm:

Declare a class for your Objective (e.g. a BlackBox or System).

Create an object of your objective class.

Create a BFGS object, sending your objective to the creator.

If desired, call Tune() method to tweak parameters and/or change the volume setting.

Call BFGS::Iterate().

class MyObjective : BlackBox{
    ⋮   // parameters should be declared as members of your class
    vfunc(subp=DoAll);  // you have to define the objective
    }
⋮
decl myobj = new MyObjective();
⋮
decl b = new BFGS(myobj);
⋮
b->Iterate();

See GetStarted for an example of using BFGS

Tune the parameters of the algorithm with Tune();

Public methods
BFGS		Create an object of the BFGS algorithm.
Hupdate	virtual	Apply BFGS updating on the Hessian.

Inherited methods from HillClimbing:: HillClimbing
Inherited methods from GradientBased:: CheckPoint, Direction, GradientBased, Gupdate, HHupdate, Iterate, ItStartCheck, Tune
Inherited methods from Algorithm:: Algorithm, ItEnd, out, Paths

Inherited fields from GradientBased:: cmsg, deltaG, deltaX, gradtoler, Hresetcnt, IamBHHH, IamNewt, igradtoler, LM, LMitmax, LMmaxstep
Inherited fields from Algorithm:: convergence, holdF, IIterate, inparallel, istr, iter, itoler, logf, lognm, logpfx, maxiter, N, NormalStart, O, path, StorePath, tolerance, Volume

BHHH : Newton : HillClimbing : GradientBased : Algorithm

Berndt Hall Hall Hausman Updating. Update Hessian with outer product of the gradient.

Public methods
BHHH		Create an object of the BHHH algorithm.
Hupdate	virtual	UPdate the Hessian for the BHHH algorithm.

Inherited methods from Newton:: Newton
Inherited methods from HillClimbing:: HillClimbing
Inherited methods from GradientBased:: CheckPoint, Direction, GradientBased, Gupdate, HHupdate, Iterate, ItStartCheck, Tune
Inherited methods from Algorithm:: Algorithm, ItEnd, out, Paths

Inherited fields from GradientBased:: cmsg, deltaG, deltaX, gradtoler, Hresetcnt, IamBHHH, IamNewt, igradtoler, LM, LMitmax, LMmaxstep
Inherited fields from Algorithm:: convergence, holdF, IIterate, inparallel, istr, iter, itoler, logf, lognm, logpfx, maxiter, N, NormalStart, O, path, StorePath, tolerance, Volume

Broyden : RootFinding : GradientBased : Algorithm

Broyden approximation to the Jacobian.

Public methods
Broyden		Create a new Broyden solution for a system of equations.
Jupdate	virtual	Apply Broyden's formula to update the Jacobian.

Inherited methods from RootFinding:: Direction, Gupdate, Iterate, ItStartCheck, JJupdate, RootFinding
Inherited methods from GradientBased:: CheckPoint, GradientBased, HHupdate, Hupdate, Tune
Inherited methods from Algorithm:: Algorithm, ItEnd, out, Paths

Inherited fields from RootFinding:: dg, USELM
Inherited fields from GradientBased:: cmsg, deltaG, deltaX, gradtoler, Hresetcnt, IamBHHH, IamNewt, igradtoler, LM, LMitmax, LMmaxstep
Inherited fields from Algorithm:: convergence, holdF, IIterate, inparallel, istr, iter, itoler, logf, lognm, logpfx, maxiter, N, NormalStart, O, path, StorePath, tolerance, Volume

CLineMax : LineMax : LineMethod : NonGradient : Algorithm

Constrained line maximization.

Public methods
CLineMax		Create a Constrained Line Maximization object.

Inherited methods from LineMax:: LineMax
Inherited methods from LineMethod:: Bracket, Golden, Iterate, LineMethod, PTry
Inherited methods from Algorithm:: Algorithm, CheckPoint, ItEnd, ItStartCheck, out, Paths, Tune

Inherited fields from LineMethod:: Delta, improved, maxstp
Inherited fields from Algorithm:: convergence, holdF, IIterate, inparallel, istr, iter, itoler, logf, lognm, logpfx, maxiter, N, NormalStart, O, path, StorePath, tolerance, Volume

DFP : QuasiNewton : HillClimbing : GradientBased : Algorithm

[not coded yet] Davidon Fletcher Powell Updating of H.

Public methods
DFP		Create an object of the DFP algorithm.
Hupdate	virtual

Inherited methods from HillClimbing:: HillClimbing
Inherited methods from GradientBased:: CheckPoint, Direction, GradientBased, Gupdate, HHupdate, Iterate, ItStartCheck, Tune
Inherited methods from Algorithm:: Algorithm, ItEnd, out, Paths

Inherited fields from GradientBased:: cmsg, deltaG, deltaX, gradtoler, Hresetcnt, IamBHHH, IamNewt, igradtoler, LM, LMitmax, LMmaxstep
Inherited fields from Algorithm:: convergence, holdF, IIterate, inparallel, istr, iter, itoler, logf, lognm, logpfx, maxiter, N, NormalStart, O, path, StorePath, tolerance, Volume

GradientBased : Algorithm

Gradient based algorithms.

Algorithms of this class use the gradient, $\nabla f(\psi)$ .

Public fields
cmsg static const

deltaG |∇_m-∇_m-1|.

deltaX |x_m-x_m-1.

gradtoler tolerance for strong convergence.

Hresetcnt # of time H reset to I

IamBHHH BHHH .

IamNewt Newton version .

igradtoler static const

LM const

LMitmax max iterations on line search.

LMmaxstep maximum step size in line search.

Public methods
CheckPoint virtual Handle gradient based checkpoint files.

Direction virtual Compute the direction for the current line search.

GradientBased Initialize a Gradient-Based algorithm.

Gupdate virtual Update the gradient ∇ f(ψ).

HHupdate Update the Hessian Hf.

Hupdate virtual Default Hessian Update: Steepest Descent, so H f(ψ) does not change.

Iterate virtual Iterate on a gradient-based algorithm.

ItStartCheck virtual This routine is called at the start if Iterate().

Tune virtual Tune Parameters of the Algorithm.

Inherited methods from Algorithm:: Algorithm, ItEnd, out, Paths

Inherited fields from Algorithm:: convergence, holdF, IIterate, inparallel, istr, iter, itoler, logf, lognm, logpfx, maxiter, N, NormalStart, O, path, StorePath, tolerance, Volume

HillClimbing : GradientBased : Algorithm

Algorithms that optimize an objective based on gradient and/or Hessian information. This is a container class. If you create an object of this type it is the same as creating GradientBased object (i.e. steepest descent).

Public methods
HillClimbing		Base for Hill Climbing objects.

Inherited methods from GradientBased:: CheckPoint, Direction, GradientBased, Gupdate, HHupdate, Hupdate, Iterate, ItStartCheck, Tune
Inherited methods from Algorithm:: Algorithm, ItEnd, out, Paths

Inherited fields from GradientBased:: cmsg, deltaG, deltaX, gradtoler, Hresetcnt, IamBHHH, IamNewt, igradtoler, LM, LMitmax, LMmaxstep
Inherited fields from Algorithm:: convergence, holdF, IIterate, inparallel, istr, iter, itoler, logf, lognm, logpfx, maxiter, N, NormalStart, O, path, StorePath, tolerance, Volume

LineMax : LineMethod : NonGradient : Algorithm

One-dimensional line search for a maximum.

Bracket a maximum, then Golden Rule iteration to reduce the interval.

This method is called by GradientBased routines, but it can be used for one-dimensional search.

Public methods
LineMax A method to optimize along a line.

Inherited methods from LineMethod:: Bracket, Golden, Iterate, LineMethod, PTry
Inherited methods from Algorithm:: Algorithm, CheckPoint, ItEnd, ItStartCheck, out, Paths, Tune

Inherited fields from LineMethod:: Delta, improved, maxstp
Inherited fields from Algorithm:: convergence, holdF, IIterate, inparallel, istr, iter, itoler, logf, lognm, logpfx, maxiter, N, NormalStart, O, path, StorePath, tolerance, Volume

LineMethod : NonGradient : Algorithm

Methods specific to solving or optimizing in one dimension.

These methods are embedded into other algorithms and in some cases can be used on their own.

Public fields
Delta Direction vector.

improved .

maxstp .

Public methods
Bracket Bracket a local maximum along the line.

Golden Golden ratio line search.

Iterate Optimize on a line optimization.

LineMethod

PTry virtual

Inherited methods from Algorithm:: Algorithm, CheckPoint, ItEnd, ItStartCheck, out, Paths, Tune

Inherited fields from Algorithm:: convergence, holdF, IIterate, inparallel, istr, iter, itoler, logf, lognm, logpfx, maxiter, N, NormalStart, O, path, StorePath, tolerance, Volume

NelderMead : NonGradient : Algorithm

The Nelder and Mead Amoeba (Simplex) algorithm.

Nelder and Mead Amoeba (Simplex) algorithm.

at Wikipedia

To use this algorithm:

Declare a class for your Objective (e.g. a BlackBox or System).

Create an object of your objective class.

Create a NelderMead object, sending your objective to the creator.

If desired, call Tune() method to tweak parameters and/or change the volume setting.

Call Iterate().

class MyObjective : BlackBox{
    ⋮
    vfunc(subp=DoAll);
    }
⋮
decl myobj = new MyObjective();
⋮
decl nm = new NelderMead(myobj);
⋮
nm -> Iterate();

See GetStarted for an example of using NelderMead

Public fields
alpha	static const	α.
beta	static const	β.
fdiff		\|f()-mean(f)\|
gamma	static const	γ.
istep	static const	default initial step size.
itoler	static const	default Plexsize tolerance.
mxstarts		number simplex resets.
n_func		function evaluations.
nodeV		function values on the simples.
nodeX		free parameter simplex.
plexsize		current area of plex.
step		current step size to create simplex
Public methods
CheckPoint
Iterate		Iterate on the Amoeba algorithm.
ItStartCheck
NelderMead		Initialize a Nelder-Mead Simplex Maximization.
Reflect		Reflect through simplex.
SimplexSize		Compute size of the current simplex.
Tune		Tune NelderMead Parameters .
Enumerations
TryResults		Results from an amoeba try.

Inherited methods from Algorithm:: Algorithm, ItEnd, out, Paths

Inherited fields from Algorithm:: convergence, holdF, IIterate, inparallel, istr, iter, logf, lognm, logpfx, maxiter, N, NormalStart, O, path, StorePath, tolerance, Volume

Newton : HillClimbing : GradientBased : Algorithm

Newton Updating of H .

Evaluate Hessian at each iteration.

To use this algorithm:

Declare a class for your Objective (e.g. a BlackBox or System).

Create an object of your objective class.

Create a Newton object, sending your objective to the creator.

If desired, call Tune() method to tweak parameters and/or change the volume setting.

Call Newton::Iterate().

class MyObjective : BlackBox{
    ⋮   // parameters should be declared as members of your class
    vfunc(subp=DoAll);  // you have to define the objective
    }
⋮
decl myobj = new MyObjective();
⋮
decl newt = new Newton(myobj);
⋮
newt -> Iterate();

Public methods
Hupdate	virtual	Compute the objective's Hessian and the current parameter vector.
Newton		Create an object of the Newton optimization algorithm.

Inherited methods from HillClimbing:: HillClimbing
Inherited methods from GradientBased:: CheckPoint, Direction, GradientBased, Gupdate, HHupdate, Iterate, ItStartCheck, Tune
Inherited methods from Algorithm:: Algorithm, ItEnd, out, Paths

Inherited fields from GradientBased:: cmsg, deltaG, deltaX, gradtoler, Hresetcnt, IamBHHH, IamNewt, igradtoler, LM, LMitmax, LMmaxstep
Inherited fields from Algorithm:: convergence, holdF, IIterate, inparallel, istr, iter, itoler, logf, lognm, logpfx, maxiter, N, NormalStart, O, path, StorePath, tolerance, Volume

NewtonRaphson : RootFinding : GradientBased : Algorithm

Update the Jacobian on each iteration.

Public methods
Jupdate	virtual	Compute the objective's Jacobian.
NewtonRaphson		Create a Newton-Raphson object to solve a system of equations.

Inherited methods from RootFinding:: Direction, Gupdate, Iterate, ItStartCheck, JJupdate, RootFinding
Inherited methods from GradientBased:: CheckPoint, GradientBased, HHupdate, Hupdate, Tune
Inherited methods from Algorithm:: Algorithm, ItEnd, out, Paths

Inherited fields from RootFinding:: dg, USELM
Inherited fields from GradientBased:: cmsg, deltaG, deltaX, gradtoler, Hresetcnt, IamBHHH, IamNewt, igradtoler, LM, LMitmax, LMmaxstep
Inherited fields from Algorithm:: convergence, holdF, IIterate, inparallel, istr, iter, itoler, logf, lognm, logpfx, maxiter, N, NormalStart, O, path, StorePath, tolerance, Volume

NonGradient : Algorithm

Container for algorithms that do not rely on gradients.

Inherited methods from Algorithm:: Algorithm, CheckPoint, ItEnd, Iterate, ItStartCheck, out, Paths, Tune

Inherited fields from Algorithm:: convergence, holdF, IIterate, inparallel, istr, iter, itoler, logf, lognm, logpfx, maxiter, N, NormalStart, O, path, StorePath, tolerance, Volume

OneDimRoot : SysMax : LineMethod : NonGradient : Algorithm

Solve for the root of a OneDimSystem system using Bracket-Bisect.

Public fields
defmxiter	static const
istepmin	static const	minimum initial step size.
itoler	static const
Public methods
Bracket		Bracket a root of the equation.
EndOneDim
Iterate		Find a 1-dimensional root.
OneDimRoot		Object to iterate to find the root a non-linear equation (OneDimSystem).

Inherited methods from SysMax:: SysMax
Inherited methods from LineMethod:: Golden, LineMethod, PTry
Inherited methods from Algorithm:: Algorithm, CheckPoint, ItEnd, ItStartCheck, out, Paths, Tune

Inherited fields from LineMethod:: Delta, improved, maxstp
Inherited fields from Algorithm:: convergence, holdF, IIterate, inparallel, istr, iter, logf, lognm, logpfx, maxiter, N, NormalStart, O, path, StorePath, tolerance, Volume

QuasiNewton : HillClimbing : GradientBased : Algorithm

Container for algorithms that use but do not compute the Hessian matrix H.

at Wikipedia

Inherited methods from HillClimbing:: HillClimbing
Inherited methods from GradientBased:: CheckPoint, Direction, GradientBased, Gupdate, HHupdate, Hupdate, Iterate, ItStartCheck, Tune
Inherited methods from Algorithm:: Algorithm, ItEnd, out, Paths

Inherited fields from GradientBased:: cmsg, deltaG, deltaX, gradtoler, Hresetcnt, IamBHHH, IamNewt, igradtoler, LM, LMitmax, LMmaxstep
Inherited fields from Algorithm:: convergence, holdF, IIterate, inparallel, istr, iter, itoler, logf, lognm, logpfx, maxiter, N, NormalStart, O, path, StorePath, tolerance, Volume

RandomSearch : SimulatedAnnealing : NonGradient : Algorithm

A special case of annealing in which the temperature stays the same and only improvements are accepted.

See also:: Explore

Public methods
RandomSearch		Random search without annealing.

Inherited methods from SimulatedAnnealing:: Iterate, Metropolis, SimulatedAnnealing, Tune
Inherited methods from Algorithm:: Algorithm, CheckPoint, ItEnd, ItStartCheck, out, Paths

Inherited fields from SimulatedAnnealing:: accept, chol, cooling, heat, holdpt, M, shrinkage, tries, Vtries, vtries
Inherited fields from Algorithm:: convergence, holdF, IIterate, inparallel, istr, iter, itoler, logf, lognm, logpfx, maxiter, N, NormalStart, O, path, StorePath, tolerance, Volume

RootFinding : GradientBased : Algorithm

Solve system of equations using Jacobian information.

Public fields
dg
USELM		use line search.
Public methods
Direction		Compute the direction.
Gupdate		Upate Jacobian when using line search aggregation of the system.
Iterate		Iterate to solve a non-linear system.
ItStartCheck	virtual	This routine is called at the start if Iterate().
JJupdate		Wrapper for updating the Jacobian of the system.
Jupdate	virtual	Compute the objective's Jacobian.
RootFinding		Solve a non-linear system of equations.

Inherited methods from GradientBased:: CheckPoint, GradientBased, HHupdate, Hupdate, Tune
Inherited methods from Algorithm:: Algorithm, ItEnd, out, Paths

Inherited fields from GradientBased:: cmsg, deltaG, deltaX, gradtoler, Hresetcnt, IamBHHH, IamNewt, igradtoler, LM, LMitmax, LMmaxstep
Inherited fields from Algorithm:: convergence, holdF, IIterate, inparallel, istr, iter, itoler, logf, lognm, logpfx, maxiter, N, NormalStart, O, path, StorePath, tolerance, Volume

SimulatedAnnealing : NonGradient : Algorithm

Metropolis Simulated Annealing algorithm .

at Wikipedia

To use an algorithm:

Declare a class for your Objective (e.g. a BlackBox or System).

Create an object of your objective class.

Create a SimulatedAnnealing object, sending your objective to the creator.

If desired, call Tune() method to tweak parameters and/or change the volume setting.

Call Iterate().

class MyObjective : BlackBox{
    ⋮
    vfunc(subp=DoAll);
    }
⋮
decl myobj = new MyObjective();
⋮
decl sa = sa SimulatedAnnealing(myobj);
⋮
sa -> Iterate();

See ?? for an example

Tune the parameters of the algorithm with Tune();

Public fields
accept
chol		cholesky matrix for random parameter change, default I
cooling		rate to cool down.
heat		current heat default intial =1.0
holdpt
M
shrinkage		rate to shrink variance.default=0.5
tries
Vtries
vtries
Public methods
Iterate		Carry out annealing.
Metropolis		accept or reject.
SimulatedAnnealing		Initialize Simulated Annealing.
Tune		Tune annealing parameters.

Inherited methods from Algorithm:: Algorithm, CheckPoint, ItEnd, ItStartCheck, out, Paths

Inherited fields from Algorithm:: convergence, holdF, IIterate, inparallel, istr, iter, itoler, logf, lognm, logpfx, maxiter, N, NormalStart, O, path, StorePath, tolerance, Volume

SQP : GradientBased : Algorithm

Sequential Quadratic Programming for constrained optimization.

Public fields
BETA	static const
ne
ni
Public methods
Gupdate		.
HHupdate		.
Hupdate	virtual	.
Iterate		Iterate.
SQP		Create a new Sequential Quadratic Programming object.

Inherited methods from GradientBased:: CheckPoint, Direction, GradientBased, ItStartCheck, Tune
Inherited methods from Algorithm:: Algorithm, ItEnd, out, Paths

Inherited fields from GradientBased:: cmsg, deltaG, deltaX, gradtoler, Hresetcnt, IamBHHH, IamNewt, igradtoler, LM, LMitmax, LMmaxstep
Inherited fields from Algorithm:: convergence, holdF, IIterate, inparallel, istr, iter, itoler, logf, lognm, logpfx, maxiter, N, NormalStart, O, path, StorePath, tolerance, Volume

SQPBFGS : SQP : GradientBased : Algorithm

Public methods
SQPBFGS

Inherited methods from SQP:: Gupdate, HHupdate, Hupdate, Iterate, SQP
Inherited methods from GradientBased:: CheckPoint, Direction, GradientBased, ItStartCheck, Tune
Inherited methods from Algorithm:: Algorithm, ItEnd, out, Paths

Inherited fields from SQP:: BETA, ne, ni
Inherited fields from GradientBased:: cmsg, deltaG, deltaX, gradtoler, Hresetcnt, IamBHHH, IamNewt, igradtoler, LM, LMitmax, LMmaxstep
Inherited fields from Algorithm:: convergence, holdF, IIterate, inparallel, istr, iter, itoler, logf, lognm, logpfx, maxiter, N, NormalStart, O, path, StorePath, tolerance, Volume

SQPNEWTON : SQP : GradientBased : Algorithm

Public methods
SQPNEWTON

Inherited methods from SQP:: Gupdate, HHupdate, Hupdate, Iterate, SQP
Inherited methods from GradientBased:: CheckPoint, Direction, GradientBased, ItStartCheck, Tune
Inherited methods from Algorithm:: Algorithm, ItEnd, out, Paths

Inherited fields from SQP:: BETA, ne, ni
Inherited fields from GradientBased:: cmsg, deltaG, deltaX, gradtoler, Hresetcnt, IamBHHH, IamNewt, igradtoler, LM, LMitmax, LMmaxstep
Inherited fields from Algorithm:: convergence, holdF, IIterate, inparallel, istr, iter, itoler, logf, lognm, logpfx, maxiter, N, NormalStart, O, path, StorePath, tolerance, Volume

SysMax : LineMethod : NonGradient : Algorithm

Systems line maximization. This specializes line optimization to be used inside the system solver.

The negative of the norm of the system is used as the objective to evaluate improvement.

A special case is OneDimRoot which brackets the root not a local optimum in the norm.

Public methods
SysMax A method to optimize along a line.

Inherited methods from LineMethod:: Bracket, Golden, Iterate, LineMethod, PTry
Inherited methods from Algorithm:: Algorithm, CheckPoint, ItEnd, ItStartCheck, out, Paths, Tune

Inherited fields from LineMethod:: Delta, improved, maxstp
Inherited fields from Algorithm:: convergence, holdF, IIterate, inparallel, istr, iter, itoler, logf, lognm, logpfx, maxiter, N, NormalStart, O, path, StorePath, tolerance, Volume

Algorithm

Algorithm :: Algorithm ( O )

Base class for non-linear programming algorithms.

Parameters:

O	Objective to work on. A timestamped log file is created for the algorithm when it is created. You can control the output level by setting Volume to the desired NoiseLevels. The default volume level is SILENT.

CheckPoint

convergence

decl convergence [public]

Convergence code. See ConvergenceResults

holdF

decl holdF [public]

IIterate

decl IIterate [public]

Running on the Client node or no MPI .

inparallel

decl inparallel [public]

Running in parallel.

istr

decl istr [public]

@internal.

ItEnd

virtual Algorithm :: ItEnd ( )

Finish up iteration. The current free parameter vector is decoded into structural parameters. If running in parallel then the client node will tell servers to stop and then announce the new structural parameter vector. The servers will drop out of the server loop and all nodes will get past Iterate()

iter

decl iter [public]

current iteration count.

Iterate

itoler

static const decl itoler [public]

Default top level convergence tolerance.

ItStartCheck

virtual Algorithm :: ItStartCheck ( ReadCheckPoint )

This routine is called at the start if Iterate().

Parameters:

ReadCheckPoint

TRUE means starting point coming from the checkpoint file.

If running in parallel it is this routine that server nodes go into the server loop. The client node will enter into the main iteration loop.

logf

decl logf [public]

log file

lognm

const decl lognm [public]

name of log file

logpfx

const decl logpfx [public]

prefix on log before timestamp added.

maxiter

decl maxiter [public]

maximum iterations

N

decl N [public]

NormalStart

decl NormalStart [public]

Not restarting from alg. checkpoint.

O

const decl O [public]

User's objective.

out

virtual Algorithm :: out ( fn )

path

decl path [public]

sequence of structural parameters .

Paths

virtual Algorithm :: Paths ( starts )

StorePath

decl StorePath [public]

Store path of iterations in path.

tolerance

decl tolerance [public]

top level convergence tolerance

Tune

virtual Algorithm :: Tune ( maxiter , toler , nfuncmax )

Tune Parameters of the Algorithm. This is the base version. Some derived algorithms replace this with their own Tune methods.

Parameters:

maxiter	integer max. number of iterations 0 use current/default value
toler	double, simplex tolerance for convergence 0 use current/default value
nfuncmax	integer, number of function evaluations before resetting the simplex 0 use current/default value

Volume

decl Volume [public]

output level, NoiseLevels

BFGS

BFGS :: BFGS ( O )

Create an object of the BFGS algorithm.

Parameters:

O	the Objective object to apply this algorithm to.

Example:

decl myobj = new MyObjective();
decl bfgs = new BFGS(myobj);
bfgs->Iterate();

See GetStarted

Hupdate

virtual BFGS :: Hupdate ( )

Apply BFGS updating on the Hessian.

Active Ox code in this routine:

decl
  dg = (OC.G - oldG),
  A = double(dx*dg'),
  B = double(outer(dx,OC.H));
  if (fabs(A) < SQRT_EPS*norm(dg,2)*deltaX ) return FAIL;
  OC.H += outer(dg,<>,'o')/A - outer(dx*OC.H,<>,'o')/B;
  return NONE;

Returns:: FAIL if the updating tolerances are not met.
Otherwise NONE
See also:: ConvergenceResults, NoiseLevels

BHHH

BHHH :: BHHH ( O )

Create an object of the BHHH algorithm.

Parameters:

O	the Objective object to apply this algorithm to.

Example:

decl myobj = new MyObjective();
decl bhhh = new BHHH(myobj);
bhhh->Iterate();

See GetStarted

Hupdate

virtual BHHH :: Hupdate ( )

UPdate the Hessian for the BHHH algorithm.

Returns:: NONE

Broyden

Broyden :: Broyden ( O , USELM )

Create a new Broyden solution for a system of equations.

Parameters:

O,	the System-derived object to solve.
USELM	FALSE [default] do not do a line search each iteration. TRUE

Example:

obj = new MyObjective();
broy = new Broyden(obj);
broy -> Iterate();

Also see GetStarted

Jupdate

virtual Broyden :: Jupdate ( dx )

Apply Broyden's formula to update the Jacobian.

Parameters:

dx	x_t+1 - x_t

CLineMax

CLineMax :: CLineMax ( O )

Create a Constrained Line Maximization object.

Parameters:

Objective

DFP

DFP :: DFP ( O )

Create an object of the DFP algorithm.

Parameters:

O	the Objective object to apply this algorithm to.

Example:

decl myobj = new MyObjective();
decl dfp = new DFP(myobj);
bfgs->Iterate();

Hupdate

GradientBased

CheckPoint

virtual GradientBased :: CheckPoint ( WriteOut )

Handle gradient based checkpoint files.

Parameters:

WriteOut

TRUE - write mode
FALSE read from file mode

cmsg

static const decl cmsg [public]

deltaG

decl deltaG [public]

|∇_m-∇_m-1|.

deltaX

decl deltaX [public]

|x_m-x_m-1.

Direction

virtual GradientBased :: Direction ( )

Compute the direction for the current line search. If inversion of H fails, reset to I

Returns:: direction vector.

GradientBased

GradientBased :: GradientBased ( O )

Initialize a Gradient-Based algorithm.

gradtoler

decl gradtoler [public]

tolerance for strong convergence.

Gupdate

virtual GradientBased :: Gupdate ( )

Update the gradient ∇ f(ψ).
This routine is called inside Iterate().: Creates a copy of the current gradient.; Calls Gradient() routine to compute &nabla f(ψ), which is (stored internally in G).; Then computes the size of ∇ using the built-in Ox norm(m,2) routine.; If Volume is louder than QUIET ∇ f() is printed to the screen.

HHupdate

GradientBased :: HHupdate ( FORCE )

Update the Hessian Hf.

Parameters:

FORCE

see below.

This is a wrapper for the virtual Hupdate() routine and is called on each iteration (except possibly the first if convergence is already achieved).

Computes the difference in parameter values from this iteration and the last as well is the norm (both used by QuasiNewton algorithms).

If FORCE is TRUE, call Gupdate() and check both STRONG and WEAK convergence criteria.

Then call the algorithm-specific Hupdate() routine.

Returns:

the output of Hupdate()

See also:

ConvergenceResults

Hresetcnt

decl Hresetcnt [public]

# of time H reset to I

Hupdate

virtual GradientBased :: Hupdate ( )

Default Hessian Update: Steepest Descent, so H f(ψ) does not change.

Since the default gradient-based algorithm is steepest descent, and since this algorithm does not use the Hessian, this return does nothing and returns

Returns:: NONE
See also:: ConvergenceResults

IamBHHH

decl IamBHHH [public]

BHHH .

IamNewt

decl IamNewt [public]

Newton version .

igradtoler

static const decl igradtoler [public]

Iterate

virtual GradientBased :: Iterate ( H )

Iterate on a gradient-based algorithm.

Parameters:

H	matrix, initial Hessian integer, use the identity `I` as the initial value, or compute H if Newton. This routine works the CFMPI to execute the parallel task of computing the gradient. Gradient-based algorithms conduct a LineMaximization on each iteration.

See also:

ConvergenceResults

ItStartCheck

virtual GradientBased :: ItStartCheck ( H )

This routine is called at the start if Iterate().

Parameters:

ReadCheckPoint

TRUE means starting point coming from the checkpoint file.

If running in parallel it is this routine that server nodes go into the server loop. The client node will enter into the main iteration loop.

LM

const decl LM [public]

LMitmax

decl LMitmax [public]

max iterations on line search.

See also:: Tune

LMmaxstep

decl LMmaxstep [public]

maximum step size in line search.

Tune

virtual GradientBased :: Tune ( maxiter , toler , nfuncmax , LMitmax , LMmaxstep )

Tune Parameters of the Algorithm.

Parameters:

maxiter	integer max. number of iterations 0 use current/default value
toler	double, simplex tolerance for convergence 0 use current/default value
nfuncmax	integer, number of function evaluations before resetting the simplex 0 use current/default value
LMitmax	integer, max number of line maximization iterions 0 use current/default value
LMmaxstep	double, maximum change in line maximization

HillClimbing

HillClimbing :: HillClimbing ( O )

Base for Hill Climbing objects.

LineMax

LineMax :: LineMax ( O )

A method to optimize along a line.

Parameters:

Objective

LineMethod

Bracket

LineMethod :: Bracket ( )

Bracket a local maximum along the line.

Delta

decl Delta [public]

Direction vector.

Golden

LineMethod :: Golden ( )

Golden ratio line search.

improved

decl improved [public]

Iterate

LineMethod :: Iterate ( Delta , maxiter , maxstp )

Optimize on a line optimization.

Parameters:

Delta	vector of directions to define the line
maxiter	> 0 max. number iterations 0 set to 1000
maxstp	> 0, maximum step size in parameters.

LineMethod

LineMethod :: LineMethod ( O )

maxstp

decl maxstp [public]

PTry

virtual LineMethod :: PTry ( pt , left , right )

NelderMead

Enumerations
TryResults	Results from an amoeba try.	hi, nxtlo, lo, worst, TryResults

alpha

static const decl alpha [public]

α.

beta

static const decl beta [public]

β.

CheckPoint

NelderMead :: CheckPoint ( WriteOut )

fdiff

decl fdiff [public]

|f()-mean(f)|

gamma

static const decl gamma [public]

γ.

istep

static const decl istep [public]

default initial step size.

Iterate

NelderMead :: Iterate ( iplex )

Iterate on the Amoeba algorithm.

Parameters:

iplex

N×N+1 matrix of initial steps.
double, initial step size (iplex is set to 0~unit(N))
integer > 0, max starts of the algorithm
0 (default), use current mxstarts.
= -1 (UseGradient), compute gradient and use its normed value as initial simplex
= -2 (UseCheckPoint), read in checkpoint file to return to last state.

Example:

See GetStarted

itoler

static const decl itoler [public]

default Plexsize tolerance.

ItStartCheck

NelderMead :: ItStartCheck ( iplex )

mxstarts

decl mxstarts [public]

number simplex resets.

n_func

decl n_func [public]

function evaluations.

NelderMead

NelderMead :: NelderMead ( O )

Initialize a Nelder-Mead Simplex Maximization.

Parameters:

O	Objective to work on.

nodeV

decl nodeV [public]

function values on the simples.

nodeX

decl nodeX [public]

free parameter simplex.

plexsize

decl plexsize [public]

current area of plex.

Reflect

NelderMead :: Reflect ( fac )

Reflect through simplex.

Parameters:

fac

factor

SimplexSize

NelderMead :: SimplexSize ( )

Compute size of the current simplex.

Returns:: ∑_j |X-μ|

step

decl step [public]

current step size to create simplex

Tune

NelderMead :: Tune ( mxstarts , toler , nfuncmax , maxiter )

Tune NelderMead Parameters .

Parameters:

mxstarts	integer number of simplex restarts 0 use current/default value
toler	double, simplex tolerance for convergence 0 use current/default value
nfuncmax	integer, number of function evaluations before resetting the simplex 0 use current/default value
maxiter	integer max. number of iterations 0 use current/default value

Newton

Hupdate

virtual Newton :: Hupdate ( )

Compute the objective's Hessian and the current parameter vector.

Returns:: NONE
See also:: ConvergenceResults, NoiseLevels

Newton

Newton :: Newton ( O )

Create an object of the Newton optimization algorithm.

Parameters:

O	the Objective object to apply this algorithm to.

Example:

decl myobj = new MyObjective();
decl newt = new Newtown(myobj);
newt->Iterate();

See GetStarted

NewtonRaphson

Jupdate

virtual NewtonRaphson :: Jupdate ( dx )

Compute the objective's Jacobian.

NewtonRaphson

NewtonRaphson :: NewtonRaphson ( O , USELM )

Create a Newton-Raphson object to solve a system of equations.

Parameters:

O,	the System-derived object to solve.
USELM	FALSE [default] do not do a line search each iteration. TRUE

Example:

obj = new MyObjective();
nr = new NewtonRaphson(obj);
hr -> Iterate();

Also see GetStarted

OneDimRoot

Bracket

OneDimRoot :: Bracket ( )

Bracket a root of the equation.

defmxiter

static const decl defmxiter [public]

EndOneDim

istepmin

static const decl istepmin [public]

minimum initial step size.

Iterate

OneDimRoot :: Iterate ( istep , maxiter , toler )

Find a 1-dimensional root.

Parameters:

istep	initial step, can be positive or negative [default=1.0]
maxiter	> 0 max. number iterations > 0 [default=50]
toler	> 0 tolerance 0 [default=SSQ_TOLER] This uses the fact that vcur.v contains the norm of the equation, not the negative,

itoler

static const decl itoler [public]

OneDimRoot

OneDimRoot :: OneDimRoot ( O )

Object to iterate to find the root a non-linear equation (OneDimSystem).

RandomSearch

RandomSearch :: RandomSearch ( O )

Random search without annealing.

RootFinding

dg

decl dg [public]

Direction

RootFinding :: Direction ( )

Compute the direction. If inversion of J fails, reset to I

Returns:: direction

Gupdate

RootFinding :: Gupdate ( )

Upate Jacobian when using line search aggregation of the system.

Iterate

RootFinding :: Iterate ( J )

Iterate to solve a non-linear system.

Parameters:

J	matrix, initial Jacobian for Broyden. integer, set to identity This routine is shared (inherited) by derived algorithms.

ItStartCheck

virtual RootFinding :: ItStartCheck ( J )

This routine is called at the start if Iterate().

Parameters:

ReadCheckPoint

TRUE means starting point coming from the checkpoint file.

If running in parallel it is this routine that server nodes go into the server loop. The client node will enter into the main iteration loop.

JJupdate

RootFinding :: JJupdate ( )

Wrapper for updating the Jacobian of the system.
This is a wrapper for the virtual Jupdate() routine.

Jupdate

virtual RootFinding :: Jupdate ( dx )

Compute the objective's Jacobian.

Parameters:

dx	argument is ignored (default=0)

RootFinding

RootFinding :: RootFinding ( O , USELM )

Solve a non-linear system of equations.

Parameters:

O	System object
USELM	FALSE [default] do not do a line search each iteration. TRUE

USELM

decl USELM [public]

use line search.

SimulatedAnnealing

accept

decl accept [public]

chol

decl chol [public]

cholesky matrix for random parameter change, default I

cooling

decl cooling [public]

rate to cool down. default = 0.85

heat

decl heat [public]

current heat default intial =1.0

holdpt

decl holdpt [public]

Iterate

SimulatedAnnealing :: Iterate ( chol )

Carry out annealing.

Parameters:

chol	Determines the Choleski matrix for random draws, `C` 0 [default] $C = I$ , the identity matrix matrix, $C =$ chol. double, common standard deviation, $C = chol I$ .

M

decl M [public]

Metropolis

SimulatedAnnealing :: Metropolis ( )

accept or reject.

shrinkage

decl shrinkage [public]

rate to shrink variance.default=0.5

SimulatedAnnealing

SimulatedAnnealing :: SimulatedAnnealing ( O )

Initialize Simulated Annealing.

Parameters:

O	Objective to work on.

tries

decl tries [public]

Tune

SimulatedAnnealing :: Tune ( maxiter , heat , cooling , shrinkage )

Tune annealing parameters.

Parameters:

maxiter	> 0, number of iterations 0 do not change
heat	> 0, temperature parameter 0 do not change
cooling	∈ (0,1], rate to cool, 0 do not change
shrinkage	∈ (0,1], rate to shrink, 0 do not change

Vtries

decl Vtries [public]

vtries

decl vtries [public]

SQP

BETA

static const decl BETA [public]

Gupdate

SQP :: Gupdate ( )

HHupdate

SQP :: HHupdate ( FORCE )

Hupdate

virtual SQP :: Hupdate ( )

Iterate

SQP :: Iterate ( H )

Iterate.

Parameters:

H	initial Hessian integer, use I

ne

decl ne [public]

ni

decl ni [public]

SQP

SQP :: SQP ( O )

Create a new Sequential Quadratic Programming object.

Parameters:

O	Constrained objective

SQPBFGS

SQPBFGS :: SQPBFGS ( O )

SQPNEWTON

SysMax

SysMax :: SysMax ( O )

A method to optimize along a line.

Parameters:

Objective

Algorithms.ox

Documentation of Items Defined in Algorithms.ox ⇧

Algorithm

BFGS : QuasiNewton : HillClimbing : GradientBased : Algorithm

BHHH : Newton : HillClimbing : GradientBased : Algorithm

Broyden : RootFinding : GradientBased : Algorithm

CLineMax : LineMax : LineMethod : NonGradient : Algorithm

DFP : QuasiNewton : HillClimbing : GradientBased : Algorithm

GradientBased : Algorithm

HillClimbing : GradientBased : Algorithm

LineMax : LineMethod : NonGradient : Algorithm

LineMethod : NonGradient : Algorithm

NelderMead : NonGradient : Algorithm

Newton : HillClimbing : GradientBased : Algorithm

NewtonRaphson : RootFinding : GradientBased : Algorithm

NonGradient : Algorithm

OneDimRoot : SysMax : LineMethod : NonGradient : Algorithm

QuasiNewton : HillClimbing : GradientBased : Algorithm

RandomSearch : SimulatedAnnealing : NonGradient : Algorithm

RootFinding : GradientBased : Algorithm

SimulatedAnnealing : NonGradient : Algorithm

SQP : GradientBased : Algorithm

SQPBFGS : SQP : GradientBased : Algorithm

SQPNEWTON : SQP : GradientBased : Algorithm

SysMax : LineMethod : NonGradient : Algorithm

Algorithm

Algorithm

CheckPoint

convergence

holdF

IIterate

inparallel

istr

ItEnd

iter

Iterate

itoler

ItStartCheck

logf

lognm

logpfx

maxiter

N

NormalStart

O

out

path

Paths

StorePath

tolerance

Tune

Volume

BFGS

BFGS

Hupdate

BHHH

BHHH

Hupdate

Broyden

Broyden

Jupdate

CLineMax

CLineMax

DFP

DFP

Hupdate

GradientBased

CheckPoint

cmsg

deltaG

deltaX

Direction

GradientBased

gradtoler

Gupdate

HHupdate

Hresetcnt

Hupdate

IamBHHH

IamNewt