Configurable options
There are several options that simply take on some default values if the user doesn't supply anything else than a function (and gradient) and a starting point.
Solver options
There quite a few different solvers available in Optim, and they are all listed below. Notice that the constructors are written without input here, but they generally take keywords to tweak the way they work. See the pages describing each solver for more detail.
Requires only a function handle:
NelderMead()
SimulatedAnnealing()
Requires a function and gradient (will be approximated if omitted):
BFGS()
LBFGS()
ConjugateGradient()
GradientDescent()
MomentumGradientDescent()
AcceleratedGradientDescent()
Requires a function, a gradient, and a Hessian (cannot be omitted):
Newton()
NewtonTrustRegion()
Box constrained minimization:
Fminbox()
Special methods for bounded univariate optimization:
Brent()
GoldenSection()
General Options
In addition to the solver, you can alter the behavior of the Optim package by using the following keywords:
x_tol
: Absolute tolerance in changes of the input vectorx
, in infinity norm. Defaults to0.0
.f_tol
: Relative tolerance in changes of the objective value. Defaults to0.0
.g_tol
: Absolute tolerance in the gradient, in infinity norm. Defaults to1e-8
. For gradient free methods, this will control the main convergence tolerance, which is solver specific.f_calls_limit
: A soft upper limit on the number of objective calls. Defaults to0
(unlimited).g_calls_limit
: A soft upper limit on the number of gradient calls. Defaults to0
(unlimited).h_calls_limit
: A soft upper limit on the number of Hessian calls. Defaults to0
(unlimited).allow_f_increases
: Allow steps that increase the objective value. Defaults tofalse
. Note that, when setting this totrue
, the last iterate will be returned as the minimizer even if the objective increased.iterations
: How many iterations will run before the algorithm gives up? Defaults to1_000
.store_trace
: Should a trace of the optimization algorithm's state be stored? Defaults tofalse
.show_trace
: Should a trace of the optimization algorithm's state be shown onstdout
? Defaults tofalse
.extended_trace
: Save additional information. Solver dependent. Defaults tofalse
.trace_simplex
: Include the full simplex in the trace forNelderMead
. Defaults tofalse
.show_every
: Trace output is printed everyshow_every
th iteration.callback
: A function to be called during tracing. A return value oftrue
stops theoptimize
call.time_limit
: A soft upper limit on the total run time. Defaults toNaN
(unlimited).
We currently recommend the statically dispatched interface by using the Optim.Options
constructor:
res = optimize(f, g!,
[0.0, 0.0],
GradientDescent(),
Optim.Options(g_tol = 1e-12,
iterations = 10,
store_trace = true,
show_trace = false))
Another interface is also available, based directly on keywords:
res = optimize(f, g!,
[0.0, 0.0],
method = GradientDescent(),
g_tol = 1e-12,
iterations = 10,
store_trace = true,
show_trace = false)
Notice the need to specify the method using a keyword if this syntax is used. This approach might be deprecated in the future, and as a result we recommend writing code that has to maintained using the Optim.Options
approach.