SimulatedAnnealing (bristlecone) | bristlecone

bristlecone

SimulatedAnnealing Module

Namespace: Bristlecone.Optimisation
Assembly: Bristlecone.dll
Parent Module: MonteCarlo

A meta-heuristic that approximates a global optimium by simulating slow cooling as a slow decrease in the probability of temporarily accepting worse solutions.

Types and nested modules

Type/Module	Description
CoolingSchemes	Cooling schemes dictate the conditions under which the temperature is cooled during simulated annealing.
EndConditions
Machines
AnnealSettings<'a>	Represents configurable settings of an annealing procedure that supports (a) heating, followed by (b) annealing.

Functions and values

Function or value	Description
`anneal writeOut chainEnd annealEnd cool markov temperature (arg7, arg8) previousBests` Full Usage: `anneal writeOut chainEnd annealEnd cool markov temperature (arg7, arg8) previousBests` Parameters: writeOut : `LogEvent -> unit` chainEnd : `'a` annealEnd : `'b` cool : `float -> int -> float` markov : `'a -> float -> float * 'c -> (float * 'c) list` temperature : `float` arg6 : `float` arg7 : `'c` previousBests : `(float * 'c) list` Returns: `(float * 'c) list`	Cool between homoegenous markov chains according to `cool` schedule. Each anneal recursion begins from the end of the previous markov chain. writeOut : `LogEvent -> unit` chainEnd : `'a` annealEnd : `'b` cool : `float -> int -> float` markov : `'a -> float -> float * 'c -> (float * 'c) list` temperature : `float` arg6 : `float` arg7 : `'c` previousBests : `(float * 'c) list` Returns: `(float * 'c) list`
`classicalSimulatedAnnealing scale tDependentProposal settings` Full Usage: `classicalSimulatedAnnealing scale tDependentProposal settings` Parameters: scale : `float` tDependentProposal : `bool` settings : `AnnealSettings<float>` Returns: `Optimiser<float>`	Candidate distribution: Gaussian univariate [] Probability: Boltzmann Machine scale : `float` tDependentProposal : `bool` settings : `AnnealSettings<float>` Returns: `Optimiser<float>`
`fastSimulatedAnnealing scale tDependentProposal settings` Full Usage: `fastSimulatedAnnealing scale tDependentProposal settings` Parameters: scale : `float` tDependentProposal : `bool` settings : `AnnealSettings<float>` Returns: `Optimiser<float>`	Candidate distribution: Cauchy univariate [] Probability: Bottzmann Machine scale : `float` tDependentProposal : `bool` settings : `AnnealSettings<float>` Returns: `Optimiser<float>`
`heat write endCondition ceiling endAcceptanceRate heatingSchedule markov (arg7, arg8) temperature` Full Usage: `heat write endCondition ceiling endAcceptanceRate heatingSchedule markov (arg7, arg8) temperature` Parameters: write : `LogEvent -> unit` endCondition : `'a` ceiling : `float option` endAcceptanceRate : `float` heatingSchedule : `float -> float` markov : `'a -> float -> float * 'b -> (float * 'b) list` arg6 : `float` arg7 : `'b` temperature : `float` Returns: `float * (float * 'b)`	Heat up temperature until acceptance rate of bad moves is above the threshold `endAcceptanceRate`. If it becomes impossible to propose a move during heating, then heating ends. write : `LogEvent -> unit` endCondition : `'a` ceiling : `float option` endAcceptanceRate : `float` heatingSchedule : `float -> float` markov : `'a -> float -> float * 'b -> (float * 'b) list` arg6 : `float` arg7 : `'b` temperature : `float` Returns: `float * (float * 'b)`
`markovChain writeOut atEnd propose probability random f temperature (arg8, arg9)` Full Usage: `markovChain writeOut atEnd propose probability random f temperature (arg8, arg9)` Parameters: writeOut : `LogEvent -> unit` atEnd : `Solution<float> list -> int -> bool` propose : `Point<float> -> Point<float>` probability : `'a -> float -> float` random : `Random` f : `Point<float> -> float` temperature : `'a` arg7 : `float` arg8 : `Point<float>` Returns: `Solution<float> list`	Run a homogenous Markov chain recursively until an end condition - `atEnd` - is met. writeOut : `LogEvent -> unit` atEnd : `Solution<float> list -> int -> bool` propose : `Point<float> -> Point<float>` probability : `'a -> float -> float` random : `Random` f : `Point<float> -> float` temperature : `'a` arg7 : `float` arg8 : `Point<float>` Returns: `Solution<float> list`
`simulatedAnnealing scale settings annealEnd machine jump cool random writeOut domain startPoint f` Full Usage: `simulatedAnnealing scale settings annealEnd machine jump cool random writeOut domain startPoint f` Parameters: scale : `float` settings : `AnnealSettings<float>` annealEnd : `'a` machine : `float -> float -> float` jump : `Random -> float -> float -> unit -> float` cool : `float -> float -> int -> float` random : `Random` writeOut : `LogEvent -> unit` domain : `Domain` startPoint : `'b` f : `float[] -> float` Returns: `(float * Point<float>) list`	scale : `float` settings : `AnnealSettings<float>` annealEnd : `'a` machine : `float -> float -> float` jump : `Random -> float -> float -> unit -> float` cool : `float -> float -> int -> float` random : `Random` writeOut : `LogEvent -> unit` domain : `Domain` startPoint : `'b` f : `float[] -> float` Returns: `(float * Point<float>) list`
`tryMove propose probability random f tries (l1, theta1)` Full Usage: `tryMove propose probability random f tries (l1, theta1)` Parameters: propose : `Point<float> -> Point<float>` probability : `float -> float` random : `Random` f : `Point<float> -> float` tries : `int` l1 : `float` theta1 : `Point<float>` Returns: `Solution<float> option`	Jump based on a proposal function and probability function propose : `Point<float> -> Point<float>` probability : `float -> float` random : `Random` f : `Point<float> -> float` tries : `int` l1 : `float` theta1 : `Point<float>` Returns: `Solution<float> option`