Customization

Subtypes of Downhill.Wrapper are used to add custom behavior to optimization, such as stopping after reaching certain criteria, limiting timesteps etc.

A wrapper around an object of AbstractOptBuffer type should at a minimum provide the base_method(M::Wrapper) to return the wrapped object.

The wrappers can modify the optimization behavior by overloading the following methods:

Downhill.init! — Function

optfn! must be the 3-arg closure that computes fdf(x + α*d) and overwrites M's gradient

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Downhill.callfn! — Function

callfn!(fdf, M::AbstractOptBuffer, x, α, d)

Return the value of fdf(x + α * d) overwriting the internal buffers of M as needed. fdf(x, g) must return a tuple (f(x), ∇f(x)) and, if g is mutable, overwrite it with the gradient (see optimize!).

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Downhill.reset! — Function

reset!(M::AbstractOptBuffer, args...; kwargs...)

Reset the solver parameters to the default (or to specific value – see the documentation for the specific types).

Each method has to implement a parameter-free reset!(M) method.

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Downhill.step! — Function

step!(fdf, M::AbstractOptBuffer; kw...)

Make one iteration of optimization routine from the current state of M using fdf as the objective function and modifying the internal buffers as needed.

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Downhill.stopcond — Function

stopcond(M::AbstractOptBuffer)

Decide if the optimization should be stopped from the state of M.

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Downhill.conv_success — Function

conv_success(M::AbstractOptBuffer)

Decide if the optimization has converged from the state of M (useful to distinguish between stopping by convergence and stopping by iterations count when the convergence has been achieved on the limiting iteration).

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Predefined Wrappers

Downhill.BasicConvergenceStats — Type

BasicConvergenceStats

Wrapper type to provide basic stop conditions: magnitude of gradient is less than the specified value, objective function call count exceeds threshold, iteration count exceeds threshold.

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Downhill.ConstrainStepSize — Type

ConstrainStepSize

Wrapper type to limit step sizes attempted in optimization, given a function (origin, direction) -> max_step.

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