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math::probopt(3tcl) Tcl Math Library math::probopt(3tcl) 





NAME


math::probopt - Probabilistic optimisation methods






SYNOPSIS


package require Tcl 8.6


package require TclOO 


package require math::probopt 1


::math::probopt::pso function bounds
    args


::math::probopt::sce function bounds
    args


::math::probopt::diffev function bounds
    args


::math::probopt::lipoMax function bounds
    args


::math::probopt::adaLipoMax function bounds
    args










DESCRIPTION


The purpose of the math::probopt package is to provide
    various optimisation algorithms that are based on probabilistic techniques.
    The results of these algorithms may therefore vary from one run to the next.
    The algorithms are all well-known and well described and proponents
    generally claim they are efficient and reliable.


As most of these algorithms have one or more tunable parameters or
    even variations, the interface to each accepts options to set these
    parameters or the select the variation. These take the form of key-value
    pairs, for instance, -iterations 100.


This manual does not offer any recommendations with regards to
    these algorithms, nor does it provide much in the way of guidelines for the
    parameters. For this we refer to online articles on the algorithms in
    question.


A few notes, however:


    
  
  • With the exception of LIPO, the algorithms are capable of dealing with
      irregular (non-smooth) and even discontinuous functions.
    • 
  
  • The results depend on the random number seeding and are likely not to be
      very accurate, especially if the function varies slowly in the vicinty of
      the optimum. They do give a good starting point for a deterministic
      algorithm.
    • 



The collection consists of the following algorithms:


    
  
  • PSO - particle swarm optimisation
    • 
  
  • SCE - shuffled complexes evolution
    • 
  
  • DE - differential evolution
    • 
  
  • LIPO - Lipschitz optimisation
    • 



The various procedures have a uniform interface:






   set result [::math::probopt::algorithm function bounds args]




The arguments have the following meaning:


  
•

  
The argument function is the name of the procedure that evaluates
      the function. Its interface is:








    set value [function coords]






  

  
where coords is a list of coordinates at which to evaluate the
      function. It is supposed to return the function value.




    
  
  • The argument bounds is a list of pairs of minimum and maximum for
      each coordinate. This list implicitly determines the dimension of the
      coordinate space in which the optimum is to be sought, for instance for a
      function like x**2 + (y-1)**4, you may specify the bounds as
      {{-1 1} {-1 1}}, that is, two pairs for the two coordinates.
    • 
  
  • The rest (args) consists of zero or more key-value pairs to specify
      the options. Which options are supported by which algorithm, is documented
      below.
    • 



The result of the various optimisation procedures is a dictionary
    containing at least the following elements:


    
  
  • optimum-coordinates is a list containing the coordinates of the
      optimum that was found.
    • 
  
  • optimum-value is the function value at those coordinates.
    • 
  
  • evaluations is the number of function evaluations.
    • 
  
  • best-values is a list of successive best values, obtained as part
      of the iterations.
    • 







DETAILS ON THE ALGORITHMS


The algorithms in the package are the following:



  
::math::probopt::pso function bounds args

  
The "particle swarm optimisation" algorithm uses the idea that
      the candidate optimum points should swarm around the best point found so
      far, with variations to allow for improvements.
    
It recognises the following options:

  






    
  
  • -swarmsize number: Number of particles to consider (default:
    50)
    • 
  
  • -vweight value: Weight for the current "velocity" (0-1,
      default: 0.5)
    • 
  
  • -pweight value: Weight for the individual particle's best position
      (0-1, default: 0.3)
    • 
  
  • -gweight value: Weight for the "best" overall position as
      per particle (0-1, default: 0.3)
    • 
  
  • -type local/global: Type of optimisation
    • 
  
  • -neighbours number: Size of the neighbourhood (default: 5, used if
      "local")
    • 
  
  • -iterations number: Maximum number of iterations
    • 
  
  • -tolerance value: Absolute minimal improvement for minimum
    value
    • 






  
::math::probopt::sce function bounds args

  
The "shuffled complex evolution" algorithm is an extension of
      the Nelder-Mead algorithm that uses multiple complexes and reorganises
      these complexes to find the "global" optimum.
    
It recognises the following options:

  






    
  
  • -complexes number: Number of particles to consider (default:
    2)
    • 
  
  • -mincomplexes number: Minimum number of complexes (default: 2; not
      currently used)
    • 
  
  • -newpoints number: Number of new points to be generated (default:
      1)
    • 
  
  • -shuffle number: Number of iterations after which to reshuffle the
      complexes (if set to 0, the default, a number will be calculated from the
      number of dimensions)
    • 
  
  • -pointspercomplex number: Number of points per complex (if set to
      0, the default, a number will be calculated from the number of
    dimensions)
    • 
  
  • -pointspersubcomplex number: Number of points per subcomplex (used
      to select the best points in each complex; if set to 0, the default, a
      number will be calculated from the number of dimensions)
    • 
  
  • -iterations number: Maximum number of iterations (default:
    100)
    • 
  
  • -maxevaluations number: Maximum number of function evaluations
      (when this number is reached the iteration is broken off. Default: 1000
      million)
    • 
  
  • -abstolerance value: Absolute minimal improvement for minimum value
      (default: 0.0)
    • 
  
  • -reltolerance value: Relative minimal improvement for minimum value
      (default: 0.001)
    • 






  
::math::probopt::diffev function bounds
    args

  
The "differential evolution" algorithm uses a number of initial
      points that are then updated using randomly selected points. It is more or
      less akin to genetic algorithms. It is controlled by two parameters,
      factor and lambda, where the first determines the update via random points
      and the second the update with the best point found sofar.
    
It recognises the following options:

  






    
  
  • -iterations number: Maximum number of iterations (default:
    100)
    • 
  
  • -number number: Number of point to work with (if set to 0, the
      default, it is calculated from the number of dimensions)
    • 
  
  • -factor value: Weight of randomly selected points in the updating
      (0-1, default: 0.6)
    • 
  
  • -lambda value: Weight of the best point found so far in the
      updating (0-1, default: 0.0)
    • 
  
  • -crossover value: Fraction of new points to be considered for
      replacing the old ones (0-1, default: 0.5)
    • 
  
  • -maxevaluations number: Maximum number of function evaluations
      (when this number is reached the iteration is broken off. Default: 1000
      million)
    • 
  
  • -abstolerance value: Absolute minimal improvement for minimum value
      (default: 0.0)
    • 
  
  • -reltolerance value: Relative minimal improvement for minimum value
      (default: 0.001)
    • 






  
::math::probopt::lipoMax function bounds
    args

  
The "Lipschitz optimisation" algorithm uses the
      "Lipschitz" property of the given function to find a
      maximum in the given bounding box. There are two variants,
      lipoMax assumes a fixed estimate for the Lipschitz parameter.
    
It recognises the following options:

  






    
  
  • -iterations number: Number of iterations (equals the actual number
      of function evaluations, default: 100)
    • 
  
  • -lipschitz value: Estimate of the Lipschitz parameter (default:
      10.0)
    • 






  
::math::probopt::adaLipoMax function bounds
    args

  
The "adaptive Lipschitz optimisation" algorithm uses the
      "Lipschitz" property of the given function to find a
      maximum in the given bounding box. The adaptive variant actually
      uses two phases to find a suitable estimate for the Lipschitz parameter.
      This is controlled by the "Bernoulli" parameter.
    
When you specify a large number of iterations, the algorithm
        may take a very long time to complete as it is trying to improve on the
        Lipschitz parameter and the chances of hitting a better estimate
        diminish fast.

    
It recognises the following options:

  






    
  
  • -iterations number: Number of iterations (equals the actual number
      of function evaluations, default: 100)
    • 
  
  • -bernoulli value: Parameter for random decisions (exploration
      versus exploitation, default: 0.1)
    • 









REFERENCES


The various algorithms have been described in on-line
    publications. Here are a few:







KEYWORDS


mathematics, optimisation, probabilistic calculations






CATEGORY


Mathematics







  

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