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.
Machines
Tuning
AnnealSettings	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, arg7) previousBests` Full Usage: `anneal writeOut chainEnd annealEnd cool markov temperature (arg7, arg7) previousBests` Parameters: writeOut : `LogEvent -> unit` chainEnd : `'a` annealEnd : `'b` cool : `float -> int -> float` markov : `'a -> float -> float<MeasureProduct<-logL, MeasureOne>> * 'c -> (float<MeasureProduct<-logL, MeasureOne>> * 'c) list` temperature : `float` arg6 : `float<MeasureProduct<-logL, MeasureOne>>` arg7 : `'c` previousBests : `(float<MeasureProduct<-logL, MeasureOne>> * 'c) list` Returns: `(float<MeasureProduct<-logL, MeasureOne>> * '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<MeasureProduct<-logL, MeasureOne>> * 'c -> (float<MeasureProduct<-logL, MeasureOne>> * 'c) list` temperature : `float` arg6 : `float<MeasureProduct<-logL, MeasureOne>>` arg7 : `'c` previousBests : `(float<MeasureProduct<-logL, MeasureOne>> * 'c) list` Returns: `(float<MeasureProduct<-logL, MeasureOne>> * 'c) list`
`classicalSimulatedAnnealing scale tDependentProposal settings` Full Usage: `classicalSimulatedAnnealing scale tDependentProposal settings` Parameters: scale : `float<MeasureProduct<optim-space, MeasureOne>>` tDependentProposal : `bool` settings : `AnnealSettings` Returns: `Optimiser`	Candidate distribution: Gaussian univariate [] Probability: Boltzmann Machine scale : `float<MeasureProduct<optim-space, MeasureOne>>` tDependentProposal : `bool` settings : `AnnealSettings` Returns: `Optimiser`
`fastSimulatedAnnealing scale tDependentProposal settings` Full Usage: `fastSimulatedAnnealing scale tDependentProposal settings` Parameters: scale : `float<MeasureProduct<optim-space, MeasureOne>>` tDependentProposal : `bool` settings : `AnnealSettings` Returns: `Optimiser`	Candidate distribution: Cauchy univariate [] Probability: Bottzmann Machine scale : `float<MeasureProduct<optim-space, MeasureOne>>` tDependentProposal : `bool` settings : `AnnealSettings` Returns: `Optimiser`
`heat write endCondition ceiling endAcceptanceRate heatingSchedule markov (arg7, arg7) temperature` Full Usage: `heat write endCondition ceiling endAcceptanceRate heatingSchedule markov (arg7, arg7) temperature` Parameters: write : `LogEvent -> unit` endCondition : `'a` ceiling : `float option` endAcceptanceRate : `float` heatingSchedule : `float -> float` markov : `'a -> float -> float<MeasureProduct<-logL, MeasureOne>> * 'b -> (float<MeasureProduct<-logL, MeasureOne>> * 'b) list` arg6 : `float<MeasureProduct<-logL, MeasureOne>>` arg7 : `'b` temperature : `float` Returns: `float * (float<MeasureProduct<-logL, MeasureOne>> * '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<MeasureProduct<-logL, MeasureOne>> * 'b -> (float<MeasureProduct<-logL, MeasureOne>> * 'b) list` arg6 : `float<MeasureProduct<-logL, MeasureOne>>` arg7 : `'b` temperature : `float` Returns: `float * (float<MeasureProduct<-logL, MeasureOne>> * 'b)`
`markovChain writeOut atEnd propose probability random f temperature (arg8, arg8)` Full Usage: `markovChain writeOut atEnd propose probability random f temperature (arg8, arg8)` Parameters: writeOut : `LogEvent -> unit` atEnd : `Solution list -> int<MeasureProduct<iteration, MeasureOne>> -> OptimStopReason` propose : `Point -> Point` probability : `'a -> float<MeasureProduct<-logL, MeasureOne>> -> float<MeasureOne>` random : `Random` f : `Objective` temperature : `'a` arg7 : `float<MeasureProduct<-logL, MeasureOne>>` arg8 : `Point` Returns: `Solution list`	Run a homogenous Markov chain recursively until an end condition - `atEnd` - is met. writeOut : `LogEvent -> unit` atEnd : `Solution list -> int<MeasureProduct<iteration, MeasureOne>> -> OptimStopReason` propose : `Point -> Point` probability : `'a -> float<MeasureProduct<-logL, MeasureOne>> -> float<MeasureOne>` random : `Random` f : `Objective` temperature : `'a` arg7 : `float<MeasureProduct<-logL, MeasureOne>>` arg8 : `Point` Returns: `Solution list`
`simulatedAnnealing scale settings annealEnd machine jump cool random writeOut domain f` Full Usage: `simulatedAnnealing scale settings annealEnd machine jump cool random writeOut domain f` Parameters: scale : `float<MeasureProduct<optim-space, MeasureOne>>` settings : `AnnealSettings` annealEnd : `'a` machine : `float -> float<MeasureProduct<-logL, MeasureOne>> -> float` jump : `Random -> float<MeasureProduct<optim-space, MeasureOne>> -> float -> unit -> float<MeasureProduct<optim-space, MeasureOne>>` cool : `float -> float -> int -> float` random : `Random` writeOut : `LogEvent -> unit` domain : `Domain` f : `TypedTensor<Vector, MeasureProduct<optim-space, MeasureOne>> -> TypedTensor<Scalar, MeasureProduct<-logL, MeasureOne>>` Returns: `(float<MeasureProduct<-logL, MeasureOne>> * Point) list`	scale : `float<MeasureProduct<optim-space, MeasureOne>>` settings : `AnnealSettings` annealEnd : `'a` machine : `float -> float<MeasureProduct<-logL, MeasureOne>> -> float` jump : `Random -> float<MeasureProduct<optim-space, MeasureOne>> -> float -> unit -> float<MeasureProduct<optim-space, MeasureOne>>` cool : `float -> float -> int -> float` random : `Random` writeOut : `LogEvent -> unit` domain : `Domain` f : `TypedTensor<Vector, MeasureProduct<optim-space, MeasureOne>> -> TypedTensor<Scalar, MeasureProduct<-logL, MeasureOne>>` Returns: `(float<MeasureProduct<-logL, MeasureOne>> * Point) list`
`tryMove propose probability random f tries (l1, theta1)` Full Usage: `tryMove propose probability random f tries (l1, theta1)` Parameters: propose : `Point -> Point` probability : `float<MeasureProduct<-logL, MeasureOne>> -> float<MeasureOne>` random : `Random` f : `Objective` tries : `int` l1 : `float<MeasureProduct<-logL, MeasureOne>>` theta1 : `Point` Returns: `Solution option`	Jump based on a proposal function and probability function propose : `Point -> Point` probability : `float<MeasureProduct<-logL, MeasureOne>> -> float<MeasureOne>` random : `Random` f : `Objective` tries : `int` l1 : `float<MeasureProduct<-logL, MeasureOne>>` theta1 : `Point` Returns: `Solution option`